CN115389964B - Battery life prediction method - Google Patents
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Abstract
The invention discloses a battery life prediction method, and belongs to the technical field of battery life prediction. The method can quickly and accurately match the mapping variable value of the battery to be tested to the theoretical battery life period in which the battery to be tested is currently supposed to be located after the mapping variable value of the battery to be tested is obtained through the found mapping relation between the mapping variable value and the battery life attenuation amount and the four incidence relations among the mapping variable value, the internal charging and discharging times in the specified period, the battery life attenuation amount and the theoretical battery life period; the first life value of the battery to be tested is predicted by using the deviation between the ideal battery life period and the theoretical battery life period estimated according to the accumulated charged and discharged times of the battery to be tested, so that the accuracy of predicting the battery life is improved; through carrying out battery operational environment temperature, humidity compensation and environmental salinity compensation to first life-span value, the degree of accuracy of battery life prediction has further been promoted.
Description
Technical Field
The invention relates to the technical field of battery life prediction, in particular to a battery life prediction method.
Background
Current prediction methods for battery life can be divided into two categories based on experience and performance. The experience-based method generally uses a statistical method, such as the conventional cycle number method, ampere-hour method, and weighted integration method, to predict the battery life by using the experience and knowledge of the battery during its use. The method based on experience is simple and quick, but can only be used under the conditions of large knowledge reserve of the battery and special conditions, and is difficult to predict under the complicated condition change (such as the change of temperature, current and the like).
The performance-based method predicts the battery life by judging whether the performance and the health state of the current battery can be continuously used, and the prediction process mainly comprises the following two steps:
step one, identifying the current aging state of a battery through known battery operation information;
and step two, predicting the residual charge-discharge cycle number of the battery with the future performance declining to the failure threshold value by using a corresponding algorithm, wherein the prediction result is the residual life of the battery.
However, the operation process of the related algorithm (for example, the patent with publication number CN110059377B adopts a deep convolutional neural network to predict the life of the fuel cell) adopted by some existing performance-based battery life prediction methods is complicated, the actual working environment of the battery is difficult to simulate, and the accuracy of predicting the remaining life of the battery is not ideal.
The temperature, humidity, current level during charging and discharging, and depth of discharge of the battery operating environment are considered to be major factors affecting the life of the battery. However, the current fluctuation degree and the fluctuation time of each charge and discharge, and the environmental salinity of the battery environment are also important factors affecting the battery life. In the whole life cycle of the battery, if the number of charging and discharging times with current fluctuation is large, for example, hundreds or even thousands of times, such a large number of charging and discharging current fluctuation can have a great influence on the life of the battery. However, in the existing method, because it is difficult to measure the influence degree of the charge and discharge fluctuation times, fluctuation degree and fluctuation duration on the battery life, the influence of the factors on the battery life is directly ignored in the existing method, and the result of predicting the battery life is not accurate enough.
Disclosure of Invention
The invention provides a method for predicting the service life of a battery, aiming at improving the accuracy and the speed of predicting the service life of the battery. In order to achieve the purpose, the invention adopts the following technical scheme:
there is provided a battery life prediction method, the method comprising:
collecting charge and discharge data of each charge and discharge of a battery to be tested in a specified period under a real use environment;
calculating mapping variable values of the battery to be tested according to the plurality of groups of charging and discharging data;
obtaining the battery life attenuation of the battery to be tested after a plurality of times of charging and discharging in the specified period based on the mapping relation between the mapping variable value and the battery life attenuation;
forming a mapping variable value, internal charging and discharging times in a specified period and a battery life attenuation data pair of the battery to be tested, and then performing data matching on the data pair and each element in the four association relation libraries to obtain a theoretical battery life period recorded in the successfully matched elements;
estimating the service life of the ideal battery according to the accumulated charged and discharged times of the battery to be tested, calculating the deviation between the theoretical battery service life and the ideal battery service life, and calculating a first service life value of the battery to be tested by using the deviation;
and performing temperature and humidity compensation and environmental salinity compensation on the first service life value to obtain a second service life value serving as a final service life value prediction result of the battery to be tested.
Preferably, each set of the charge and discharge data comprises charge current, discharge current, charge duration, discharge depth, charge current fluctuation value, discharge current fluctuation value, charge fluctuation time, discharge fluctuation time, ambient temperature and humidity of the battery and salinity of the environment of the battery in one charge and discharge process.
Preferably, the mapping relationship between the mapping variable value and the battery life decrement, and the four association relationships are established by:
acquiring a first total life value of an experimental battery which is shipped in the same batch with the battery to be tested in a full-reference charging and discharging scene;
building the experimental battery under a sudden charging and discharging scene;
collecting a plurality of groups of charging and discharging data generated by the experimental battery in the appointed period under the abrupt charging and discharging scene, recording the charging and discharging times, and then calculating the mapping variable value corresponding to the experimental battery;
recovering the experimental battery to be in a full-reference charging and discharging scene so as to continue carrying out a service life test experiment on the experimental battery to obtain a second total service life value after the experimental battery is subjected to the abrupt charging and discharging scene;
calculating the life difference value of the first total life value and the second total life value as the battery life attenuation quantity which has a mapping relation with the mapping variable value of the experimental battery, forming the mapping relation corresponding to the experimental battery and adding the mapping relation into a mapping relation library, and forming four incidence relations among the mapping variable value, the charge and discharge times in a specified period, the battery life attenuation quantity and the theoretical battery life period and respectively adding the four incidence relations into the four incidence relation libraries.
Preferably, the method for creating the abrupt charging and discharging scene comprises the following steps:
connecting the experimental battery into a charging and discharging circuit;
selecting any fluctuation adjusting circuit in the charging and discharging circuits to build a circuit for a sudden charging and discharging scene;
calculating the corresponding conduction time length of the specified period, then calculating the charge-discharge time length of each charge-discharge in the conduction time length, and arranging the sequence of each charge-discharge;
controlling the selected fluctuation adjusting circuit to be conducted and keeping the conduction time, wherein during the conduction period of the fluctuation adjusting circuit, the charging and discharging circuit is a closed loop;
and controlling the experimental battery to execute a plurality of corresponding charging and discharging actions within the conducting time according to the arranged charging and discharging sequence and the arranged charging and discharging time.
Preferably, the method for creating the abrupt charging and discharging scene comprises the following steps:
connecting the experimental battery into a charge and discharge circuit;
selecting a fluctuation adjusting circuit which has the capacity of creating the charge-discharge current fluctuation characteristic of the battery in a real use scene in the charge-discharge circuit as a sudden charge-discharge scene creating circuit corresponding to the charge-discharge;
determining a charging and discharging time length and a charging and discharging sequence for each selected fluctuation adjusting circuit, wherein the sum of the charging and discharging time lengths corresponding to each selected fluctuation adjusting circuit is equal to the conduction time length of the charging and discharging circuit;
and during the conduction period of the charge and discharge circuit, driving the corresponding fluctuation adjusting circuits to be conducted according to the charge and discharge sequence, and completing the execution of corresponding charge and discharge actions during the conduction period.
Preferably, the charging and discharging circuit includes a plurality of fluctuation adjusting circuits connected in parallel and having the same or different current fluctuation amplitude adjusting capability and different fluctuation duration adjusting capability, each of the fluctuation adjusting circuits includes a fluctuation duration adjusting circuit and a fluctuation amplitude adjusting circuit, the fluctuation duration adjusting circuit is an inductor, and inductance values of the inductors in each of the fluctuation adjusting circuits are different.
Preferably, the method for calculating the mapping variable value of the battery to be tested or the experimental battery in the specified period or the conduction time duration corresponding to the specified period includes:
respectively calculating the charging current fluctuation value in each of the charging and discharging data in the designated period or the conduction time interval
Discharge current fluctuation value
Depth of discharge
Duration of fluctuation
Are respectively recorded as
、
、
、
，
The weighted summation value of the charging fluctuation time length and the discharging fluctuation time length in each group of charging and discharging data is obtained;
corresponding to said charge-discharge data for each of a plurality of groups
The mean value of (a);
judging each group of the charge and discharge data
And
is less than a first threshold value and
and with
Is less than a second threshold value and
and with
Is less than a third threshold value and
and with
Is less than a fourth threshold value,
if yes, adding the group of the charging and discharging data into a first data set,
if not, adding the group of the charging and discharging data into a second data set;
and calculating a charge quantity average value and a discharge quantity average value of a plurality of groups of charge and discharge data in the first data set and the second data set respectively to obtain a first charge quantity average value and a first discharge quantity average value corresponding to the first data set, obtaining a second charge quantity average value and a second discharge quantity average value corresponding to the second data set, performing weighted summation calculation on the first charge quantity average value and the second charge quantity average value to obtain a first sum value, performing weighted summation calculation on the first discharge quantity average value and the second discharge quantity average value to obtain a second sum value, and performing weighted summation calculation on the first sum value and the second sum value to obtain a sum value serving as the mapping variable value.
Preferably, the method for calculating the first life value of the battery to be tested by using the deviation comprises the following steps:
acquiring a first accumulated charged and discharged frequency of the battery to be tested from leaving a factory to experiencing the abrupt charge and discharge scene, a rated charge and discharge frequency of a full life cycle and a second accumulated charged and discharged frequency of an experimental battery corresponding to the element successfully matched in the four incidence relation libraries after experiencing the abrupt charge and discharge scene;
calculating the ratio of the first accumulated charged and discharged times to the second accumulated charged and discharged times;
calculating the difference between the rated charge-discharge frequency and the first accumulated charged-discharge frequency;
calculating a product of the difference and the ratio;
and calculating a sum of the multiplied value and the first cumulative charged/discharged number of times as the first life value.
Preferably, the method for compensating the first life value by temperature, humidity and environmental salinity comprises the following steps:
calculating the charging and discharging electricity data of a plurality of groups collected by the battery to be tested in the appointed periodAverage value of ambient temperature of battery during charging and discharging
Average value of ambient humidity
And average salinity of the environment
；
Will be provided with
、
、
Respectively as independent variables of the corresponding fitting functions, and solving to obtain respectively corresponding temperature compensation coefficients
Coefficient of humidity compensation
Environmental salinity compensation coefficient
；
For the first life value, respectively calculating the sum of the first life value and the second life value
、
、
Multiplication of (d), result
、
、
；
To pair
、
、
Carrying out weighted summation to obtain the life value prediction result;
preferably, the fitting function is expressed by the following formula (1):
in the formula (1), the first and second groups,
to represent
Or
Or
；
The invention has the following beneficial effects:
1. the charging and discharging circuit is provided, and different charging and discharging fluctuation time lengths are given to an experimental battery under a sudden charging and discharging scene during charging or discharging by using different current slow-release capacities of inductors with different inductance values, so that the real charging and discharging fluctuation time length characteristic of the battery is simulated; meanwhile, the fluctuation range adjusting circuit with different charging and discharging current fluctuation range adjusting capabilities is utilized to endow the experimental battery constructed in the sudden charging and discharging scene with different charging and discharging current fluctuation values during charging or discharging so as to simulate the real charging and discharging fluctuation range characteristic of the battery, and the charging and discharging fluctuation duration characteristic and the charging and discharging fluctuation range characteristic of the experimental battery in the sudden charging and discharging scene are taken as important factors influencing the service life of the battery, so that the found mapping relation between the mapping variable value and the service life attenuation quantity of the battery is more accurate, and the accuracy of predicting the service life of the battery is improved.
2. The method includes the steps that 9 factors of a charging current fluctuation value, a discharging current fluctuation value, a charging fluctuation duration, a discharging depth, a charging current, a discharging current, a charging duration and a discharging duration which affect the accuracy of predicting the service life of the battery are represented by mapping variable values, the mapping relation between the mapping variable values and the service life attenuation of the battery is searched, when the attenuation of the service life of the battery after the battery is subjected to charging and discharging data of a plurality of times in a specified period is predicted, the mapping variable values are obtained only by calculation according to the charging and discharging data of the battery to be tested in the specified period, the service life attenuation of the battery can be quickly matched based on the mapping relation, and the prediction speed of the service life attenuation of the battery is greatly improved.
3. According to the mapping variable value, the internal charging and discharging times in the appointed period and the four incidence relations between the battery life attenuation and the theoretical battery life period which are summarized in advance, as long as the mapping variable value, the internal charging and discharging times in the appointed period and the battery life attenuation data pair of the battery to be detected are obtained, the theoretical battery life period in which the battery to be detected should be located at present can be quickly matched based on the four incidence relations, then the ideal battery life period in which the battery to be detected is located at present is calculated according to the accumulated charging and discharging times of the battery to be detected, and then the first life value of the battery to be detected can be quickly and accurately calculated by utilizing the deviation between the theoretical battery life period and the ideal battery life period;
4. the influence of the temperature, the humidity and the environmental salinity of the working environment of the battery on the service life of the battery is considered, the temperature, the humidity and the environmental salinity of the first service life value are compensated, the second service life value is obtained and used as a final service life value prediction result of the battery to be tested, and the prediction result is more accurate.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below. It is obvious that the drawings described below are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a diagram showing an apparatus configuration of an automated simulation apparatus for simulating a real usage environment of a battery according to an embodiment of the present invention;
fig. 2 is a schematic circuit diagram of a charging/discharging circuit according to an embodiment of the present invention;
FIG. 3 is an exemplary diagram of dividing the full life cycle of a battery into several battery life periods;
fig. 4 is a diagram of implementation steps of a method for predicting battery life according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
Wherein the showings are for the purpose of illustration only and are shown by way of illustration only and not in actual form, and are not to be construed as limiting the present patent; for a better explanation of the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inner", "outer", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not indicated or implied that the referred device or element must have a specific orientation, be constructed in a specific orientation and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limitations of the present patent, and the specific meanings of the terms may be understood by those skilled in the art according to specific situations.
In the description of the present invention, unless otherwise explicitly specified or limited, the term "connected" or the like, if appearing to indicate a connection relationship between the components, is to be understood broadly, for example, as being fixed or detachable or integral; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or may be connected through one or more other components or may be in an interactive relationship with one another. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The principle of the invention for rapidly and accurately predicting the service life of the battery is as follows:
the method comprises the steps of firstly, collecting a plurality of groups of charging and discharging data (one group is formed by one charging and discharging) of a battery to be tested in a specified time period under a real use environment, and then calculating a corresponding mapping variable value according to the collected plurality of groups of charging and discharging data, wherein the mapping variable value considers the influences of a charging current fluctuation value, a discharging current fluctuation value, a charging fluctuation time length, a discharging depth, an accumulated charging amount and an accumulated discharging amount in the specified time period on a battery life prediction result.
And then obtaining the battery life attenuation after the battery to be tested is charged and discharged for a plurality of times in a specified period based on the mapping relation between the predetermined mapping variable value and the battery life attenuation.
In different battery life periods, the same abrupt charging and discharging scene is created for the experimental battery, the same number of charging and discharging times are executed, the same mapping variable value is obtained, but the experimental result shows that the same mapping variable value obtained in different battery life periods generally corresponds to different battery life attenuation quantities. The invention utilizes the characteristic to find the correlation between the mapping variable value, the internal charging and discharging times in the appointed period and the battery life attenuation data and the theoretical battery life period, which is called as four correlation. After the mapping variable value, the internal charging and discharging times and the battery life attenuation of the battery to be tested are known in the specified period, the theoretical battery life period where the battery to be tested should be located at present can be quickly matched based on the four association relations.
The theoretical battery life period is summarized in an experimental environment, and the influence of the fluctuation amplitude and the fluctuation duration of the charging and discharging current in each stage in the full life cycle in a real environment on the estimation accuracy of the battery life cycle is not considered. So that there is a deviation between this theoretical battery life period calculated for the battery under test and the ideal battery life period estimated from the accumulated charged and discharged times of the battery under test. For example, the theoretical battery life of the battery under test is the age shown in fig. 3, and the battery under test has not yet passed the growth period according to the actual accumulated charged and discharged times of the battery under test, i.e. the battery under test should still be in the growth period under ideal conditions, as measured according to the accumulated charged and discharged times. The invention uses the deviation between the theoretical battery life period and the ideal battery life period to calculate the first life value of the battery to be tested.
When calculating the mapping variable value, searching the mapping relation between the mapping variable value and the battery life attenuation, searching the mapping variable value, the charge and discharge times in a specified period, the battery life attenuation and the correlation of the theoretical battery life, if the temperature and the humidity of the battery environment and the salinity of the environment are also taken as the factors influencing the battery life, the difficulty of calculating the mapping variable value, searching the mapping relation and the correlation of the four is exponentially increased, so that the environment temperature, the humidity and the salinity are not taken as the factors when calculating the mapping variable value, searching the mapping relation and the correlation of the four. However, the environmental temperature, humidity and salinity are important factors affecting the prediction accuracy of the battery life and are not negligible, so that the temperature, humidity and salinity compensation are required to be performed on the first life value finally, and the second life value is obtained and used as the final life value prediction result of the battery to be tested.
According to the above life prediction principle, the battery life prediction method provided by the embodiment of the invention comprises the following six technical parts:
1. collecting charge and discharge data required for life prediction
And collecting the charge and discharge data of the battery to be tested in each charge and discharge in a specified period under the real use environment. Taking charging and discharging of a battery pack of a certain user energy storage system as an example, the usage scenario of the user energy storage is that the battery pack is discharged for 30 minutes in 8 am and 30 and in 17 pm for 30. To collect charge and discharge data required for predicting the life of the battery pack, 30 days or 15 days may be designated as a period for collecting charge and discharge data (i.e., a designated period). If 15 days are selected as the designated period of data acquisition, according to the application scenario, 30 sets of discharge data and 15 sets of charge data are acquired, the discharge time of each set of discharge data is 30 minutes, and the charge time of each set of charge data is 240 minutes. However, 15 days are taken as a designated period, and the charging and discharging data acquisition can be completed after 15 days, so that the efficiency is too low, and therefore, after the charging and discharging rules of the battery are determined, the designated period can be compressed to improve the testing efficiency. For example, the above determines that the charging data to be acquired is 30 groups of 30 minutes each, the discharging data is 15 groups of 240 minutes each, and the charging and discharging manner is determined as follows: when the charging is performed for 240 minutes and the discharging is performed for 2 times for 30 minutes each time, the time required for completing the charging and discharging is 30+240=300 minutes, the charging and discharging can be performed for 4.8 times in 24 hours a day, and the charging and discharging can be completed within 15/4.8 days in 15 times in total. Therefore, in order to ensure that the battery pack has enough time to cool down after charging or discharging is completed each time, the specified period can be determined to be 4 days, and the time which is more than 15/4.8 of the specified period can be inserted into the gap between charging and discharging each time in a segmented manner so as to reserve enough time for cooling down the battery pack. By the charging and discharging data acquisition mode, the acquisition scene is ensured to be consistent with the real charging and discharging scene as much as possible, and the data acquisition efficiency is improved.
The purpose of collecting the charging and discharging data of the battery to be measured in each charging and discharging in the specified period is to calculate the mapping variable value of the battery to be measured, and the mapping variable value fully considers the influence of the charging current fluctuation value, the discharging current fluctuation value, the fluctuation duration, the discharging depth, the accumulated charging amount and the accumulated discharging amount in the specified period on the battery life prediction result in each charging and discharging. Therefore, each group of charge-discharge data to be collected comprises charge current, discharge current, charge duration, discharge depth, charge current fluctuation value, discharge current fluctuation value, charge fluctuation time and discharge fluctuation time in the charge-discharge process, wherein the charge current and the discharge current refer to stable values of the charge current and the discharge current after the charge or discharge process is stable; the charging current fluctuation value and the discharging current fluctuation value respectively refer to the difference value of the maximum current and the minimum current after charging is started and after discharging is started and before the current reaches the steady flow; the charging fluctuation time length and the discharging fluctuation time length are the duration time length before the charging or discharging reaches the steady flow.
It should be emphasized here that when the mapping relationship between the mapping variable value of the experimental battery and the battery life attenuation is found, and when the mapping variable value of the experimental battery, the number of charging and discharging times in a specified period, and the correlation between the data of the battery life attenuation and the theoretical battery life are found, if the environmental temperature, humidity, and salinity of the experimental battery are taken into consideration to influence the battery life, the complexity of the mapping relationship and the correlation finding will increase exponentially, and the experimental process will become very difficult. Thus, to reduce the complexity of finding these two relationships, the present invention does not take into account ambient temperature, humidity, and ambient salinity when finding these two relationships, i.e., the automated simulation apparatus shown in FIG. 1 is inoperative when finding these two relationships. However, the environmental temperature, humidity and salinity have important influence on the battery life and are not negligible factors, so the last step provided by the invention is to perform temperature, humidity compensation and environmental salinity compensation on the first life value predicted under the condition of not considering the environmental temperature, humidity and salinity. Since temperature, humidity compensation and environmental salinity compensation are required in the last step, the environmental temperature, humidity and environmental salinity of the battery are also required to be collected when the charging and discharging data are collected every time (salt solution is attached to the surface of the battery and may corrode the pole and the shell of the battery, so that the internal active material and the electrolyte enter impurities, and the service life of the battery is influenced by the impurities).
2. Calculating the mapping variable value of the battery to be measured
The mapping variable value of the battery to be measured considers the influence of a charging current fluctuation value, a discharging current fluctuation value, a charging fluctuation time length, a discharging depth, an accumulated charging amount and an accumulated discharging amount in a specified time period on a battery life prediction result and the respective influence degrees of the influencing factors, and the calculation method comprises the following steps:
respectively calculating the charging current fluctuation value in each group of charging and discharging data in a plurality of groups collected in a specified period
Discharge current fluctuation value
Depth of discharge
Duration of fluctuation
Are respectively recorded as
、
、
、
. The charging current fluctuation value is the difference value between the maximum current and the minimum current before the current is stabilized after the charging is started; the discharge current fluctuation value is the difference between the maximum current and the minimum current before the current is stabilized after the discharge is started. For example, it is assumed that each set of charge and discharge data collected in a given period includes two sets of discharge data and one set of charge data, and the charge current fluctuation values in the two sets of discharge data
If used separately
、
Indicating the fluctuation value of the discharge current in a set of charging data
By using
And if 10 groups of charging and discharging data are collected, the 10 groups of charging and discharging fluctuation data are expressed as: (
、
、
）、（
、
、
）、（
、
、
）、……、（
、
、
）、……、（
、
、
) Then, then
，
. Depth of discharge
If it is used
That is, 10 sets of discharge depths are expressed as
、
) Then, then
。
while
Corresponding to each of several groups of charging and discharging data
Mean value of the values. For example, note (
、
、
) Corresponding to
Is composed of
、（
、
、
) Corresponding to
Is composed of
Then 10 groups
Corresponding to
。
In general, the larger the fluctuation value of the current per charge and discharge, the deeper the depth of discharge, and the longer the fluctuation time, the greater the influence on the battery life. Therefore, the invention gives more weight to the charge and discharge data with high volatility, high discharge depth and long time in fluctuation when representing the influence quantity on the service life of the battery, and the weight giving mode is as follows:
determining charge or discharge data of each group
And
is less than a first threshold value and
and
is less than a second threshold value and
and
is less than a third threshold value and
and
is less than a fourth threshold value,
if yes, adding the group of charging and discharging data into the first data set,
if not, adding the group of charge and discharge data into a second data set;
it should be noted here that the above-mentioned patent documents,
and
a distance of,
And
the distance of,
And
the distance of,
And
is the absolute value of the difference between the two values, e.g.
And
the absolute value of the difference of (a). In fact, is satisfying
And
is less than a first threshold value, and
and
、
and
、
and
when any one or more of the distances of the battery life prediction data do not satisfy the condition that the distances are smaller than the corresponding threshold values, the mapping variable values obtained by the method of data set collection are usually inconsistent, different deviations can be generated on the influence of the battery life prediction accuracy, and the influence degree of the different situations on the battery life prediction result should be considered under the ideal condition so as to improve the battery life prediction accuracy, however, considering these situations, the complexity of the correlation between the mapping variable value and the battery life attenuation, and the correlation between the mapping variable value, the number of charge/discharge cycles in a predetermined period, the battery life attenuation, and the theoretical battery life period increases exponentially, and the experimental process becomes extremely complicated
And
is less than a first threshold value and
and
is less than a second threshold value and
and
is less than a third threshold value and
and
the distance less than the fourth threshold value is a data set collection condition, the charge and discharge data meeting the condition are added into the first data set, and the charge and discharge data not meeting the condition are added into the second data set. The influence of the charging and discharging current fluctuation value, the discharging depth and the fluctuation duration of the charging and discharging data added into the first data set on the accuracy of the battery life prediction result is considered to be lower than that of the charging and discharging data added into the second data set.
And finally, calculating a charge quantity average value and a discharge quantity average value of a plurality of groups of charge and discharge data in the first data set respectively to obtain a first charge quantity average value and a first discharge quantity average value, calculating a charge quantity average value and a discharge quantity average value of a plurality of groups of charge and discharge data in the second data set respectively to obtain a second charge quantity average value and a second discharge quantity average value, performing weighted summation calculation on the first charge quantity average value and the second charge quantity average value to obtain a first sum value, performing weighted summation calculation on the first discharge quantity average value and the second discharge quantity average value to obtain a second sum value, and performing weighted summation calculation on the first sum value and the second sum value to obtain a sum value which is a mapping variable value obtained by solving.
It should be noted that, when solving the first sum and the second sum, the weight of the average value of the second charge is greater than the weight of the first charge, the weight of the second discharge is greater than the weight of the first discharge, how much weight is given is determined by the distance between the first data set and the second data set, and the distance between the first data set and the second data set can be represented by calculating the difference between the average values of the charge current fluctuation between the first data set and the second data set, or by using other variable values, for example, and the specific method for calculating the distance between the two data sets is not specifically described because it is not within the scope of the present invention. Similarly, when solving the values of the mapping variables, the weight of the second sum is greater than that of the first sum, and the specific weight assignment manner is based on the distance between the two data sets, which is not specifically described herein.
3. Solving for battery life decay
The establishment of the mapping relation between the mapping variable value and the battery life attenuation is the technical key of the application for predicting the battery life, and the invention establishes the mapping relation between the mapping variable value and the battery life attenuation by the following method:
(1) Acquiring a first total life value of an experimental battery which is shipped in the same batch with a battery to be tested in a full-reference charging and discharging scene; the experimental battery which is shipped in the same batch with the battery to be tested is selected because the type and the battery shipping performance of the experimental battery and the battery to be tested are consistent, and the mapping relation between the mapping variable value and the battery life attenuation quantity which is constructed by taking the experimental battery as an object has the foundation. The full-reference charging and discharging scene is an ideal charging and discharging scene. In an ideal charging and discharging scenario, the temperature, humidity and environmental salinity of the battery usage environment are constant, so a full-benchmark charging and discharging scenario can usually only be completed in a laboratory. For example, in a laboratory, a test battery is charged once per day at 17 pm, charged for 240 minutes each time, and discharged twice per day, once at 8 am, 00, 8 am, 30, and once per day from battery factory to battery life end at 17 pm, 00, 17, and such charging and discharging are performed once per day, and this charging and discharging scenario is considered to be a full reference charging and discharging scenario. The charging and discharging scenes other than the full-reference charging and discharging scene are non-reference charging and discharging scenes, that is, abrupt charging and discharging scenes mentioned in the following contents. The first total life value of the experimental battery under the full-reference charging and discharging scene can be obtained by summarizing uninterrupted charging and discharging experiments. It should be noted here that, for the above battery usage scenario of twice-a-day discharge and once-charge, if the experimental battery is completely discharged according to the following charging times of 8 am, 30 pm and 17 pm. Therefore, the charge and discharge efficiency in the experiment can be improved by adopting the mode of compressing the specified period and then acquiring the charge and discharge data of the battery to be tested in the first part.
(2) And building the experimental battery under a sudden charging and discharging scene, and keeping the duration as the specified period of the battery to be tested or the corresponding conduction duration of the specified period. The invention creates a sudden charging and discharging scene for an experimental battery through the charging and discharging circuit shown in fig. 2, and the specific creating methods include the following two methods:
method one
The experimental battery is firstly connected into the charging and discharging circuit. As shown in fig. 2, the charging and discharging circuit according to the present invention includes a driving circuit 300, a plurality of ripple adjusting circuits 400 connected in parallel with each other, a switching circuit 500, and a controller 16 (preferably, a BMS battery management system). After the experimental battery is connected, the driving circuit 300 drives any one of the fluctuation adjusting circuits 400 to be turned on according to a driving instruction sent by the controller 16 (the turn-on duration corresponds to a specified period for collecting charge and discharge data of the battery to be tested, and after the turn-on duration is reached, the driving circuit drives the fluctuation adjusting circuit 400 to be turned off according to the driving instruction of the controller 16, so that the charge and discharge circuit is turned off), and drives the switching circuit 500 to be turned on, so as to form a closed loop among the anode of the experimental battery, the fluctuation adjusting circuit, the switching circuit and the cathode of the experimental battery, and then the controller 16 controls the experimental battery to execute charge and discharge actions according to a preset strategy and collects the charge and discharge data of each charge and discharge. It should be particularly noted that the execution strategy of the experimental battery charging/discharging action is related to the actual usage scenario of the battery under test. For example, the actual usage scenario of the battery to be tested is as follows: the charging is performed once a day, the charging is performed for 240 minutes in 17 minutes each day, the discharging is performed twice each day, one time is at 8, 8 in the morning, and the other time is at 17, 17 in the afternoon, then the charging and discharging sequence is that the discharging is performed first for 2 times and the recharging is performed once, in order to eliminate the influence of the temperature of the battery generated by the previous discharging on the discharged data of the next time, the idle time needs to be reserved between the two times of discharging for cooling the battery after the previous discharging is completed, and the idle time also needs to be reserved after the discharging of the next time to avoid the influence on the charging data acquisition. If the idle time between charging and discharging is 30 minutes, the operation of charging and discharging the experimental battery once can be 30 minutes for the first discharging, 30 minutes for the idle, 30 minutes for the second discharging, 30 minutes for the idle, 240 minutes for charging, and 30 minutes for the idle, so that 390 minutes is required for completing the one-time charging and discharging of the experimental battery. If the charging and discharging data of the experimental battery 20 group needs to be collected, 7800 minutes are needed, the 7800 minutes are the time length of the fluctuation adjusting circuit needing to be conducted for creating the sudden charging and discharging scene, and the conducting time length corresponds to the specified period of the battery to be tested in the real use environment and is 20 days.
In the charging and discharging circuit shown in fig. 2, each fluctuation adjusting circuit has different charging and discharging current fluctuation adjusting amplitude and adjusting time length, and in the first method, charging and discharging data after one fluctuation adjusting circuit is conducted (other fluctuation adjusting circuits are kept in an off state) are collected, so that the first method is suitable for the situation that the charging and discharging current fluctuation amplitude and the fluctuation time length of each charging and discharging of a battery in a specified period are relatively stable. For the situation that the fluctuation range and the fluctuation duration of the charging and discharging current of each charging and discharging of the battery are unstable in the specified period, if a plurality of groups of charging and discharging data collected by the first method are used for calculating the mapping variable value and searching the mapping relation between the mapping variable value and the battery life attenuation amount, and then the battery life is predicted based on the mapping relation, obviously, the obtained prediction result is not accurate. In order to solve this problem, the present application provides a second construction method, namely:
method two
Firstly, an experimental battery is connected into a charging and discharging circuit shown in fig. 2;
then according to the charge-discharge current fluctuation characteristics of the battery in a historical specified period under a real use scene, selecting a corresponding fluctuation adjusting circuit as a sudden change charge-discharge scene construction circuit corresponding to the charge-discharge of a time, wherein the selecting process specifically comprises the following steps: example (B)For example, the charging current fluctuation value of the battery during a certain charging and discharging in a specified period of history
If the difference value of the charging current fluctuation adjustment amplitude which can be realized by a certain fluctuation adjustment circuit in the charging and discharging circuit shown in fig. 2 is smaller than the preset difference value threshold, the fluctuation adjustment circuit is selected to create a circuit for the sudden charging and discharging scene of the charging and discharging;
determining the charging and discharging time length and the charging and discharging sequence of charging and discharging for each selected fluctuation adjusting circuit, wherein the charging and discharging time length is the charging and discharging time length of corresponding charging and discharging in a historical specified period, and the sum of the charging and discharging time lengths corresponding to each selected fluctuation adjusting circuit is equal to the conducting time length of the charging and discharging circuit;
it should be noted here that the charge and discharge sequence of each fluctuation adjusting circuit is determined based on the actual usage of the battery. For example, in a real scene, the charging current fluctuation ranges of the battery for 3 consecutive charging and discharging are respectively a first fluctuation range, a second fluctuation range and a third fluctuation range, then firstly, a fluctuation adjusting circuit with first fluctuation range creating capability is used for creating a sudden change charging and discharging scene for the experimental battery, and firstly, a charging and discharging action is executed in the sudden change charging and discharging scene, after the charging and discharging are completed, a fluctuation adjusting circuit with second fluctuation range creating capability is used for creating a corresponding sudden change charging and discharging scene for the experimental battery, and a charging and discharging action is executed again in the sudden change charging and discharging scene, and finally, a fluctuation adjusting circuit with third fluctuation range creating capability is used for creating a sudden change charging and discharging scene for the experimental battery and executing a final charging and discharging action.
It should be noted here that each of the fluctuation adjusting circuits in the charging and discharging circuits shown in fig. 2 includes a fluctuation amplitude adjusting circuit and a fluctuation time length adjusting circuit, the fluctuation amplitude adjusting circuit may be formed by existing resistance circuits with different charging and discharging current fluctuation amplitude adjusting capabilities, the fluctuation time length adjusting circuit is an inductor, and the inductance value of the inductor in each fluctuation adjusting circuit is different. The invention utilizes different current slow release capacities of inductors with different inductance values to endow the experimental battery constructed in a sudden charging and discharging scene with different charging and discharging fluctuation time lengths during charging or discharging so as to simulate the real charging and discharging fluctuation time length characteristic of the battery. Meanwhile, the fluctuation range adjusting circuit with different charging and discharging current fluctuation range adjusting capabilities is utilized to endow the experimental battery constructed in the sudden charging and discharging scene with different charging and discharging current fluctuation values during charging or discharging so as to simulate the real charging and discharging fluctuation range characteristic of the battery, the charging and discharging fluctuation duration characteristic and the charging and discharging fluctuation range characteristic of the experimental battery in the sudden charging and discharging scene are taken as important factors influencing the service life of the battery, and the accuracy of battery service life prediction is improved.
(3) The method comprises the steps of collecting a plurality of groups of charging and discharging data of an experimental battery in a sudden charging and discharging scene, recording the charging and discharging times, and then calculating the mapping variable value corresponding to the experimental battery. The calculation method of the mapping variable value of the experimental battery is the same as that of the mapping variable value of the battery to be tested in the second part, and is not described herein again.
(4) Recovering the experimental battery to be in a full-reference charging and discharging scene so as to continue carrying out a service life test experiment on the experimental battery to obtain a second total service life value after the experimental battery is subjected to a sudden charging and discharging scene;
(5) And calculating the service life difference value of the first total service life value and the second total service life value as a battery service life attenuation quantity which has a mapping relation with the mapping variable value of the experimental battery, and forming a mapping relation between the mapping variable value corresponding to the experimental battery and the battery service life attenuation quantity to be added into a mapping relation library.
4. Obtaining a theoretical battery life span for a battery under test
The experimental battery always works in a full-reference charging and discharging scene at other times in the experimental process except for the built sudden charging and discharging scene. Assuming that charging and discharging are performed according to a fixed charging and discharging rule in a full-standard charging and discharging scene, the rated life of the experimental battery is 800 times of accumulated cyclic charging and discharging as shown in fig. 3, wherein the battery is defined to be in a growth period within 250 times from factory leaving to accumulated charging and discharging times, the battery is defined to be in a growth period between 250 times and 700 times, and the battery is defined to be in an old period between 700 times and 800 times. Assuming that before the experimental battery enters the abrupt charge-discharge scene, 280 times of charge-discharge are accumulated under the full-base charge-discharge scene, and the number of charge-discharge times under the abrupt charge-discharge scene is 30 times, after the 30 times of charge-discharge under the abrupt charge-discharge scene is completed, the accumulated number of charge-discharge times is 310 times, and the battery life period corresponding to 310 times is the year period in fig. 3, and this year period is the theoretical battery life period. Four correlation relations are formed between the theoretical battery life period and the mapping variable value, the charging and discharging times and the battery life attenuation amount of the experimental battery calculated under the sudden charging and discharging scene, the four correlation relations are stored in a four correlation relation library, the 310 accumulated charging and discharging times are saved, and the purpose of saving the accumulated charging and discharging times is to calculate the deviation between the theoretical battery life period and the ideal battery life period subsequently.
5. Calculating a first life value of the battery to be tested
In the experimental process, it is found that, for experimental batteries in different battery life periods, even if the same abrupt charging and discharging scene is created, the completely same charging and discharging behaviors are executed in the abrupt scene, and the completely same mapping variable values are obtained, the finally obtained attenuation amounts of the battery life are often different. Therefore, the direct influence on the battery life prediction result caused by the battery life period of the battery to be tested is proved when the battery life to be tested is predicted. In order to reduce the influence of the factors on the battery life prediction result, the method finds out the four association relations among the mapping variable values, the internal charging and discharging times in the specified period, the battery life attenuation and the theoretical battery life period through repeated experimental summarization. And then, the current theoretical battery life period can be quickly matched based on the four incidence relations by only obtaining the mapping variable value, the charging and discharging times and the battery life attenuation calculated by the battery to be tested in the sudden charging and discharging scene. The searching process of the four association relations is briefly described as follows:
as shown in fig. 3, if the full life cycle of the battery is divided into a growth period, a growth period and an old period (the full life cycle may be divided into smaller granularity, the smaller the granularity is, the more battery life stages are, the more compact the correlation relationship between the four is, and the more accurate the first life value to be predicted subsequently), the first total life value of the experimental battery in the full-reference charging and discharging scene is assumed to be circularly chargeable and dischargeable for 800 times, according to the summary of the experiments on the performances of the battery in different stages, the first 250 times are defined as the growth period, the middle 450 times are the growth period, and the last 100 times are the old period. For the same experimental battery, the same abrupt charge and discharge scene is created for a plurality of times in the growth period (for example, the same abrupt charge and discharge scene is created when the accumulated charged and discharged times of the experimental battery under the full-standard charge and discharge scene reach 100 times, 160 times, 220 times, and the like, the same abrupt charge and discharge scene means that the designated period of charge and discharge is the same, the charge and discharge times in the designated period are the same, and the obtained mapping variable values are also the same), and then the experiment summarizes the battery life attenuation amount of the experimental battery after each abrupt charge and discharge scene is created.
For example, when the accumulated charge/discharge times in the growth period reaches 100 times, the obtained battery life attenuation amount is 5 times less than the full-life cycle charge/discharge times, when the accumulated charge/discharge times in the growth period reaches 160 times, the obtained battery life attenuation amount is 10 times less than the full-life cycle charge/discharge times, when the accumulated charge/discharge times in the growth period reaches 220 times, the obtained battery life attenuation amount is 15 times less than the full-life cycle charge/discharge times, then the average value of the battery life attenuation amounts is 10 times as the average battery life attenuation amount for creating the sudden charge/discharge scene in the growth period, so that the four-person correlation relationship among the mapping variable value, the specified period charge/discharge times, the battery life attenuation amount (the average battery life attenuation amount, i.e., 10 times), and the theoretical battery life period (the theoretical battery life attenuation amount) can be formed, and the correlation relationship among the four-person correlation data of the experimental mapping variable value, the specified period charge/discharge times, the theoretical life attenuation amount, and the theoretical battery life attenuation amount is more accurate. The four correlations over the other battery life periods can be summarized using the same method described above. And subsequently, after a mapping variable value, a specified period charging and discharging frequency and a battery life attenuation data pair of the battery to be tested are formed, data matching is carried out on the data pair and each element in the four association relation libraries, and a theoretical battery life period recorded in the obtained matched elements is used as the theoretical battery life period where the current theory of the battery to be tested should be located.
Here, the matching success criteria for matching the mapping variable value of the battery to be tested, the number of charge and discharge times in the specified period, and the battery life attenuation data with each element in the four association relationship libraries are as follows: and the absolute value of the difference between the mapping variable value in the battery to be tested and the mapping variable value in a certain element and the absolute value of the difference between the battery life attenuation amounts are smaller than the corresponding difference threshold, and the charging and discharging times in a specified period are the same, namely the matching is judged to be successful.
In addition, when the four correlation relations are formed, the experimental battery operates in the full-reference charging and discharging scene except for the abrupt charging and discharging scene created in the specified period, so that the battery life period matched based on the four correlation relations is defined as the 'theoretical battery life period' of the battery to be tested, and has a deviation from the ideal battery life period after the battery is charged and discharged in the real environment. Neither the theoretical battery life cycle nor the ideal battery life cycle can represent the prediction result of the battery life, but the deviation between the theoretical battery life cycle and the ideal battery life cycle can just represent the difference of the battery life prediction in the experimental environment and the actual use environment. Therefore, the invention uses the deviation to calculate the first life value of the battery to be measured, and the calculating method specifically comprises the following steps:
firstly, acquiring a first accumulated charged and discharged number (for example, 523) of times of a battery to be tested from factory shipment to a sudden charge and discharge scene, a rated charge and discharge number (for example, 250, 450 and 100 rated charge and discharge numbers of a growth period, an aging period and an old age period shown in fig. 3) of each battery life period in a full life cycle, and a second accumulated charged and discharged number (for example, 582) of times of an experimental battery corresponding to an element successfully matched in a quadruple relation library after the experimental battery undergoes the sudden charge and discharge scene;
then, calculating the ratio of the first accumulated charged and discharged times to the second charged and discharged times, namely 523/582;
calculating the difference between the rated charge and discharge times of the full life cycle and the first accumulated charge and discharge times, namely 800 and 523=277;
calculating the product of the ratio 523/582 and the difference 277, i.e.
；
The sum of the multiplied value and the first cumulative charged and discharged number of times is calculated as a first life value, i.e., 523+249=772.
6. Compensating for the first life value for ambient temperature, humidity and salinity
It has been demonstrated in the above sections one through five that in order to reduce the complexity of finding the mapping relationships and the four correlations, the temperature, humidity, and environmental salinity of the battery environment are not taken into consideration during the finding process to affect the life of the battery. But the ambient temperature, humidity and salinity are important factors affecting the life of the battery and cannot be ignored. Therefore, in the last step, the invention needs to compensate the first service life value by temperature, humidity and environmental salinity so as to correct the first service life value, and the second service life value obtained after correction is used as the final prediction result of the service life of the battery to be measured. The method for compensating the temperature, the humidity and the environmental salinity of the first service life value comprises the following steps: charging and discharging a plurality of groups of batteries to be tested in a specified period, and calculating the average value of the environmental temperature of the batteries to be tested in the charging and discharging process
Average value of ambient humidity
And average value of ambient salinity
(ii) a For example, wherein the ambient temperature of the primary charge-discharge process is
At the other time is
The average value of the ambient temperatures of the two charging and discharging operations is
，
、
The calculation method is the same as that of
Are not described herein again;
will be provided with
、
、
Respectively as independent variables of the corresponding fitting functions, and solving to obtain respectively corresponding temperature compensation coefficients
Coefficient of humidity compensation
Environmental salinity compensation factor
；
For the first life value, respectively calculating the sum
、
、
Multiplication of (d), the result is noted as
、
、
；
To pair
、
、
Carrying out weighted summation to obtain a life value prediction result;
for solving compensation coefficients
、
、
Is expressed by the following formula (1):
in the formula (1), the first and second groups of the compound,
to represent
Or
Or
；
The fitting function expressed by the formula (1) is
And with
A chamber,
And
a chamber,
And
a mapping relation is established between the batteries, and then the batteries to be tested are obtained by calculation
、
、
Based on the mapping relationship, the corresponding compensation coefficient can be rapidly calculated
. Whether this mapping is exactly directly related to the values of the parameters quadratic coefficient, first order coefficient, and constant term in equation (1). Therefore, in order to conveniently, quickly and accurately obtain the parameter values of all the parameters, the invention adopts the following two measures:
(1) The automated simulation apparatus shown in fig. 1 is provided specifically;
(2) And fitting by an interpolation method of Lagrange polynomials to obtain parameter values of all the parameters.
The following method for conveniently, quickly and accurately obtaining the parameter values of each parameter is mainly described: as shown in fig. 1, the automatic simulation apparatus includes a water tank 1, a mixed gas generation chamber 2, a mixed gas processing chamber 3, and a transparent test chamber 4 for placing a battery 100 to be tested (the test chamber is set to be transparent in order to create a real light environment), and an automatic control device for automatically simulating a real use environment of the battery. The automatic control device comprises a first electromagnetic valve 5 for switching the opening and closing of the pipeline, which is arranged on each input pipeline of the mixed gas generating chamber 2, a fan 21 arranged at the bottom in the mixed gas generating chamber 2, a valve 6 arranged on the pipeline between the water tank 1 and the transparent testing chamber 4, a second electromagnetic valve 7, a water suction pump 200, a quick heater 8 with a temperature sensing function, a third electromagnetic valve 9 and a suction pump 20 which are arranged on the pipeline between the mixed gas processing chamber 3 and the transparent testing chamber 4, and an output pipeline of the mixed gas generating chamber 2 is connected to a mixed gas input port 31 of the mixed gas processing chamber 3 through a fourth electromagnetic valve 18, which are shown in fig. 1. The automatic control equipment also comprises a heating device 10 arranged at the bottom of the mixed gas processing chamber 3 and a salinity detector 17 arranged at the top, a temperature sensor 11 arranged inside the mixed gas processing chamber 3, an atomizing nozzle 12 arranged in the transparent test chamber 4, a temperature sensor 13, a humidity sensor 14, a salinity detector 15 and a heating device 19, and a controller 16 which is in communication connection with the first electromagnetic valve 5, the fan 21, the second electromagnetic valve 7, the water suction pump 200, the quick heater 8, the third electromagnetic valve 9, the air suction pump 20, the fourth electromagnetic valve 18, the heating device 10, the salinity detector 17, the temperature sensor 11, the atomizing nozzle 12, the temperature sensor 13, the humidity sensor 14, the salinity detector 15 and the heating device 19, which are shown in figure 1.
The method for simulating the real use environment of the battery for the experimental battery by the automatic simulation device comprises the following steps:
the user opens the valve 6 shown in fig. 1, and then inputs a control command formed by the temperature control quantity, the humidity control quantity and the salinity control quantity of the battery use environment into the intelligent terminal, and sends the control command to the controller 16, after receiving the control command, the controller 16 firstly controls the first electromagnetic valve 5 on the pipeline for introducing the air of the test environment in fig. 1 to be opened, after the first electromagnetic valve 5 is opened, the outside air is introduced into the mixed gas generation chamber 2, then controls the fan 21 in fig. 1 to be opened, and then opens the fourth electromagnetic valve 18 in fig. 1, and the air in the mixed gas generation chamber 2 is input into the mixed gas processing chamber 3 through the pipeline where the fourth electromagnetic valve 18 is located and the mixed gas input port 31 of the mixed gas processing chamber 3 under the wind force of the fan 21. The salinity detector 17 arranged in the mixed gas processing chamber 3 detects the indoor salinity in real time and feeds the indoor salinity back to the controller 16, when the controller 16 judges that the salinity in the mixed gas processing chamber 3 does not reach the preset salinity control quantity, the controller 16 controls the two electromagnetic valves 51 and 52 arranged on the pipelines for respectively introducing the salt fog and the air in the controller 1 to be opened, the controller 16 respectively calculates the opening time of the electromagnetic valves 51 and 52 according to the gas quantity introduced in the predetermined pipeline unit time and the environmental salinity measurement (namely respectively calculates the environmental salinity and the air introduction time), and controls the corresponding electromagnetic valve 51 or 52 to be closed after the introduction time is reached. The mixing device arranged in the mixed gas generating chamber 2 fully mixes the introduced salt fog and the air to obtain mixed salt fog, and the mixed salt fog is introduced into the mixed gas processing chamber 3 under the action of the wind power of the fan 21. The salinity is detected again by the salinity detector 17 in the mixed gas processing chamber 3, when the preset salinity control amount is reached, the first electromagnetic valve 5, 51, 52, the fourth electromagnetic valve 18 and the fan 21 are controlled to be closed, and then the heating device 10 in the mixed gas processing chamber 3 is controlled to heat the mixed gas in the chamber (the heating amount is the battery using environment temperature control amount input by the user). The temperature sensor 11 in the mixed gas processing chamber 3 detects the indoor temperature in real time and sends the detected value to the controller 16, and when the controller 16 judges that the temperature reaches the battery use environment temperature control amount inputted by the user, the controller controls the heating device 11 to stop heating, and then controls the third electromagnetic valve 9 and the suction pump 20 in fig. 1 to be opened to suck the mixed gas in the mixed gas processing chamber 3 into the transparent test chamber 4. And then controlling a second electromagnetic valve 7 shown in fig. 1 to be opened and controlling a quick heater 8 with a temperature sensing function to adjust the quick heating temperature to the battery use environment temperature control quantity input by the user, and then controlling and starting a water suction pump 200 to pump the water in the water tank 1 to an atomizing nozzle 12, so that the atomized water is sprayed into the transparent test chamber 4. In the spraying of the atomizing nozzle 12, the temperature sensor 13, the humidity sensor 14 and the salinity detector 15 in the transparent test chamber 4 work simultaneously, the real-time detection results are respectively sent to the controller 16, and when the controller 16 judges that the humidity detected by the humidity sensor 14 reaches the humidity control quantity input by the user, the second electromagnetic valve 7 is controlled to be automatically closed and the water pump 200 and the quick heater 8 are controlled to stop working. When the temperature sensor 13 detects that the temperature in the transparent test chamber 4 is lower than the battery use environment temperature control amount input by the user and reaches the preset temperature difference threshold value, the controller 16 controls the heating device 19 in the transparent test chamber 4 to automatically start heating, and controls the heating device 19 to stop heating when the temperature sensor 13 detects that the indoor temperature reaches the battery use environment temperature control amount. When the salinity detector 15 detects that the salinity in the transparent test chamber 4 does not reach the preset salinity control amount, the control process is repeated until the temperature, the humidity and the salinity in the transparent test chamber 4 all reach the control amount requirement.
To respectively obtain
As an independent variable, with
As a first fitting function of the dependent variable, to
As an independent variable, with
A second fitting function as a dependent variable to
As an independent variable, with
The third fitting function is a dependent variable, and when an automatic simulation device is used, the simulation of the environmental temperature, the environmental humidity and the environmental salinity are respectively carried out on the experimental battery, and the specific method comprises the following steps: through the technical schemes of the first part to the fifth part, the first service life value is obtained through prediction, and the influence of the environmental temperature, the environmental humidity and the environmental salinity on the service life of the battery is not considered in the prediction process of the first service life value. To find the influence of the ambient temperature, humidity and ambient salinity on the battery life prediction results one by one, taking the first fitting function as an example, for the same first life value, different temperatures are simulated in the transparent test chamber 4 by the automated simulation apparatus shown in fig. 1, and then each simulated temperature is obtained
The real life value of the lower experimental battery, and the division value of the real life value and the first life value is the simulated temperature
Value of this
The values are mapped to the simulated temperature, and the accuracy of the mapping is characterized by the values of the parameters of the first fitting function.
In order to obtain the parameter values of the parameters in the first fitting function, the invention preferably adopts an interpolation method of Lagrange polynomials for each simulated temperature
And corresponding
And fitting the fitting points into a curve, and performing inverse extrapolation according to the fitting curve to obtain the parameter values of the parameters in the first fitting function. Similarly, the method for obtaining the second fitting function and the third fitting function is the same as the principle for obtaining the first fitting function, and is not described herein again.
By integrating the above technical solutions of the six parts, the implementation process of the battery life prediction method provided by the present application is summarized and obtained as shown in fig. 4, and the implementation process includes the following steps:
s1, collecting charge and discharge data of each charge and discharge of a battery to be tested in a specified period under a real use environment;
s2, calculating a mapping variable value of the battery to be tested according to the plurality of groups of charging and discharging data;
s3, obtaining the battery life attenuation after the battery to be tested is charged and discharged for a plurality of times in a specified period based on the mapping relation between the mapping variable value and the battery life attenuation;
s4, forming a mapping variable value of the battery to be tested, the number of charging and discharging times in a specified period and a battery life attenuation data pair, and then performing data matching on the data pair and each element in the four incidence relation libraries to obtain a theoretical battery life period recorded in the successfully matched elements;
s5, estimating the service life of the ideal battery according to the accumulated charged and discharged times of the battery to be tested, calculating the deviation between the theoretical battery service life and the ideal battery service life, and calculating a first service life value of the battery to be tested by using the deviation;
and S6, performing temperature and humidity compensation and environmental salinity compensation on the first service life value to obtain a second service life value serving as a final service life value prediction result of the battery to be tested.
It should be understood that the above-described embodiments are merely preferred embodiments of the invention and the technical principles applied thereto. Various modifications, equivalent substitutions, changes, etc., will also be apparent to those skilled in the art. However, such variations are within the scope of the invention as long as they do not depart from the spirit of the invention. In addition, certain terminology used in the description and claims of the present application is not limiting, but is used for convenience only.
Claims (6)
1. A method of predicting battery life, the method comprising:
collecting charge and discharge data of each charge and discharge of a battery to be tested in a specified period under a real use environment;
calculating mapping variable values of the battery to be tested according to the plurality of groups of charging and discharging data;
obtaining the battery life attenuation of the battery to be tested after a plurality of times of charging and discharging in the specified period based on the mapping relation between the mapping variable value and the battery life attenuation;
forming a mapping variable value, internal charging and discharging times in a specified period and a battery life attenuation data pair of the battery to be tested, and then performing data matching on the data pair and each element in the four association relation libraries to obtain a theoretical battery life period recorded in the successfully matched elements;
estimating the service life of the ideal battery according to the accumulated charged and discharged times of the battery to be tested, calculating the deviation between the service life of the theoretical battery and the service life of the ideal battery, and calculating a first service life value of the battery to be tested by using the deviation;
performing temperature and humidity compensation and environmental salinity compensation on the first service life value to obtain a second service life value serving as a final service life value prediction result of the battery to be tested;
each group of the charge and discharge data comprises charge current, discharge current, charge duration, discharge depth, charge current fluctuation value, discharge current fluctuation value, charge fluctuation time, discharge fluctuation time, ambient temperature and humidity of the battery and salinity of the environment of the battery in one charge and discharge process;
the mapping relation of the mapping variable value and the battery life decrement and the four association relations are established by the following method:
acquiring a first total life value of an experimental battery which is shipped in the same batch with the battery to be tested in a full-reference charging and discharging scene;
building the experimental battery under a sudden charging and discharging scene;
collecting a plurality of groups of charging and discharging data generated by the experimental battery in the appointed period under the abrupt charging and discharging scene, recording the charging and discharging times, and then calculating the mapping variable value corresponding to the experimental battery;
recovering the experimental battery to be in a full-reference charging and discharging scene so as to continue carrying out a service life test experiment on the experimental battery to obtain a second total service life value after the experimental battery is subjected to the abrupt charging and discharging scene;
calculating a life difference value of the first total life value and the second total life value as the battery life attenuation quantity which has a mapping relation with the mapping variable value of the experimental battery, forming the mapping relation corresponding to the experimental battery and adding the mapping relation into a mapping relation library, and forming four incidence relations of the mapping variable value, the charging and discharging times in a specified period, the battery life attenuation quantity and the theoretical battery life period and respectively adding the mapping variable value, the charging and discharging times in the specified period, the battery life attenuation quantity and the theoretical battery life period into the four incidence relation libraries;
the method for calculating the mapping variable value of the battery to be tested or the experimental battery in the specified period or the conducting time corresponding to the specified period comprises the following steps:
respectively calculating the charging current fluctuation value in each of the charging and discharging data in the designated period or the conduction time interval
Discharge current fluctuation value
Depth of discharge
Duration of wave
Are respectively recorded as
、
、
、
，
The weighted sum value of the charging fluctuation time length and the discharging fluctuation time length in each group of charging and discharging data is obtained;
for each of a plurality of groupsCorresponding to charge and discharge data
The mean value of (a);
judging each group of the charge and discharge data
And
is less than a first threshold value and
and
is less than a second threshold value and
and
is less than a third threshold value and
and with
Is less than a fourth threshold value,
if yes, adding the group of the charge and discharge data into a first data set,
if not, adding the group of the charging and discharging data into a second data set;
respectively calculating a charge quantity average value and a discharge quantity average value of a plurality of groups of charge and discharge data in the first data set and the second data set to obtain a first charge quantity average value and a first discharge quantity average value corresponding to the first data set, obtaining a second charge quantity average value and a second discharge quantity average value corresponding to the second data set, then performing weighted summation calculation on the first charge quantity average value and the second charge quantity average value to obtain a first sum value, performing weighted summation calculation on the first discharge quantity average value and the second discharge quantity average value to obtain a second sum value, and finally performing weighted summation calculation on the first sum value and the second sum value to obtain a sum value serving as the mapping variable value;
the method for calculating the first life value of the battery to be tested by using the deviation comprises the following steps:
acquiring a first accumulated charging and discharging frequency of the battery to be tested from the factory to the sudden charging and discharging scene, a rated charging and discharging frequency of a full life cycle and a second accumulated charging and discharging frequency of an experimental battery corresponding to the element successfully matched in the four incidence relation libraries after the experimental battery is subjected to the sudden charging and discharging scene;
calculating the ratio of the first accumulated charged and discharged times to the second accumulated charged and discharged times;
calculating the difference between the rated charge-discharge frequency and the first accumulated charged-discharge frequency;
calculating a product of the difference and the ratio;
and calculating a sum of the multiplied value and the first cumulative charged/discharged number of times as the first life value.
2. The method of claim 1, wherein the abrupt charging and discharging scenario is created by:
connecting the experimental battery into a charging and discharging circuit;
selecting any fluctuation adjusting circuit in the charging and discharging circuits to create a circuit for a sudden change charging and discharging scene;
calculating the corresponding conduction time length of the specified period, then calculating the charge-discharge time length of each charge-discharge in the conduction time length, and arranging the sequence of each charge-discharge;
controlling the selected fluctuation adjusting circuit to be conducted and keeping the conduction duration, wherein during the conduction period of the fluctuation adjusting circuit, the charging and discharging circuit is a closed loop;
and controlling the experimental battery to execute a plurality of corresponding charging and discharging actions within the conducting time according to the arranged charging and discharging sequence and the arranged charging and discharging time.
3. The method of claim 1, wherein the abrupt charging and discharging scenario is created by:
connecting the experimental battery into a charging and discharging circuit;
selecting a fluctuation adjusting circuit which has the capacity of creating the charge-discharge current fluctuation characteristic of the battery in a real use scene in the charge-discharge circuit as a sudden charge-discharge scene creating circuit corresponding to the charge-discharge;
determining a charging and discharging time length and a charging and discharging sequence for each selected fluctuation adjusting circuit, wherein the sum of the charging and discharging time lengths corresponding to each selected fluctuation adjusting circuit is equal to the conduction time length of the charging and discharging circuit;
and during the conduction period of the charge and discharge circuit, driving the corresponding fluctuation adjusting circuits to be conducted according to the charge and discharge sequence, and completing the execution of corresponding charge and discharge actions during the conduction period.
4. The method according to claim 2, wherein the charge and discharge circuit includes a plurality of the ripple adjustment circuits connected in parallel with each other and having the same or different current ripple amplitude adjustment capabilities and different ripple duration adjustment capabilities, each of the ripple adjustment circuits includes a ripple duration adjustment circuit and a ripple amplitude adjustment circuit, the ripple duration adjustment circuit is an inductor, and an inductance value of the inductor in each of the ripple adjustment circuits is different.
5. The method of predicting battery life according to claim 1, wherein the compensating the first life value for temperature, humidity and ambient salinity is performed by:
during the specified periodCalculating the average value of the environmental temperature of the battery to be measured in the charging and discharging process aiming at a plurality of groups of charging and discharging electric data collected by the battery to be measured within a certain period
Average value of ambient humidity
And the average value of the environmental salinity of the environment
；
Will be provided with
、
、
Respectively as independent variables of the corresponding fitting functions, and solving to obtain respectively corresponding temperature compensation coefficients
Coefficient of humidity compensation
Environmental salinity compensation coefficient
；
For the first life value, respectively calculating the sum
、
、
Multiplication of (d), the result is noted as
、
、
；
To pair
、
、
And carrying out weighted summation to obtain the life value prediction result.
6. The battery life prediction method of claim 5, wherein the fitting function is expressed by the following equation (1):
in the formula (1), the first and second groups,
to represent
Or
Or
；
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