CN112666464B - Battery health state prediction method, device, electronic equipment and readable storage medium - Google Patents
Battery health state prediction method, device, electronic equipment and readable storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
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Abstract
The embodiment of the application provides a battery state of health prediction method, a device, electronic equipment and a readable storage medium, which relate to the technical field of computers.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and apparatus for predicting a battery health state, an electronic device, and a readable storage medium.
Background
At present, along with the enhancement of environmental awareness of people, the development of new energy vehicles is faster and faster, and more people select new energy vehicles as transportation means for traveling, wherein the new energy vehicles use electric energy as main energy, so the battery life of the new energy vehicles is strongly related to the performance of the new energy vehicles.
The life of a battery can be expressed by the State of health (SOH), which is a parameter that cannot be directly obtained and needs to be determined by a prediction algorithm.
In the related art, the battery SOH at a certain time in the future is often predicted by external characteristic parameters such as current and voltage, but such a prediction method is difficult to ensure the accuracy of the battery SOH.
Disclosure of Invention
In view of this, the embodiments of the present application provide a method, an apparatus, an electronic device, and a readable storage medium for predicting a battery state of health, so as to improve the accuracy of battery state of health prediction.
In a first aspect, a method for predicting a state of health of a battery is provided, the method being applied to an electronic device, the method comprising:
and determining the current driving mileage and the current battery parameters of the target electric car.
And determining the current battery health state corresponding to the current driving mileage based on a first corresponding relation between the preset driving mileage and the battery health state.
And inputting the current battery health state, the current driving mileage and the current battery parameters into a pre-trained machine learning model to determine a target battery health state of the target electric car corresponding to the target driving mileage.
In a second aspect, there is provided a battery state of health prediction apparatus, the apparatus being applied to an electronic device, the apparatus comprising:
and the first determining module is used for determining the current driving mileage and the current battery parameters of the target electric car.
And the second determining module is used for determining the current battery health state corresponding to the current driving mileage based on a first corresponding relation between the preset driving mileage and the battery health state.
And the prediction module is used for inputting the current battery health state, the current driving mileage and the current battery parameters into a pre-trained machine learning model so as to determine the target battery health state of the target electric car corresponding to the target driving mileage.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, the memory storing one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method as described in the first aspect.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement a method according to the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product comprising a computer program/instruction which, when executed by a processor, implements a method as described in the first aspect.
According to the embodiment of the application, the current health state corresponding to the current running mileage of the target electric car can be determined through the preset first corresponding relation, then the current battery health state, the current running mileage and the current battery parameters can be synthesized based on the pre-trained machine learning model, and the target battery health state corresponding to the target running mileage of the target electric car can be predicted.
Drawings
The above and other objects, features and advantages of embodiments of the present application will become more apparent from the following description of embodiments of the present application with reference to the accompanying drawings, in which:
fig. 1 is a schematic diagram of a battery state of health prediction method according to an embodiment of the present application;
fig. 2 is a flowchart of a method for predicting a battery state of health according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a driving range in a first correspondence provided in an embodiment of the present application;
FIG. 4 is a flowchart of another method for predicting a battery state of health according to an embodiment of the present application;
fig. 5 is a schematic diagram of a battery capacity curve at 25 degrees celsius according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a second correspondence provided in an embodiment of the present application;
FIG. 7 is a schematic diagram of a first correspondence relationship according to an embodiment of the present application;
FIG. 8 is a graph showing a comparison of experimental results provided in the examples of the present application;
fig. 9 is a schematic structural diagram of a battery state of health prediction apparatus according to an embodiment of the present application;
fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The present application is described below based on examples, but the present application is not limited to only these examples. In the following detailed description of the present application, certain specific details are set forth in detail. The present application will be fully understood by those skilled in the art without the details described herein. Well-known methods, procedures, flows, components and circuits have not been described in detail so as not to obscure the nature of the application.
Moreover, those of ordinary skill in the art will appreciate that the drawings are provided herein for illustrative purposes and that the drawings are not necessarily drawn to scale.
Unless the context clearly requires otherwise, the words "comprise," "comprising," and the like in the description are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, it is the meaning of "including but not limited to".
In the description of the present application, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
At present, as new energy vehicles are rapidly developed, more and more people select new energy vehicles as traveling vehicles, and take new energy vehicles as an example, the new energy vehicles are applied to various scenes (such as new energy private cars, new energy buses, shared electric cars and the like), and the new energy vehicles take electric energy as main energy, namely, the new energy vehicles acquire electric energy through batteries, so that the service lives of the batteries of the new energy vehicles are strongly related to the performances of the new energy vehicles.
The life of a battery can be expressed by the State of health (SOH), which is a parameter that cannot be directly obtained and needs to be determined by a prediction algorithm.
In the related art, the battery SOH can be estimated from two dimensions, one of which can be estimated by observing the reaction mechanism and parameters inside the battery, such as loss of active lithium ions, collapse of material lattice, anode and cathode materials inside the battery, separator size, electrolyte ion concentration and diffusion coefficient, based on the dimensions of electrochemical mechanism, but it is difficult to estimate the battery SOH in this way because the above reaction mechanism and parameters are not easily available due to confidentiality and the like. Secondly, the SOH of the battery can be estimated by external characteristic parameters of the battery (e.g., current, voltage, temperature, etc.), but this approach does not accurately represent the decay of the battery, i.e., the future SOH of the battery cannot be accurately predicted in this way.
In order to accurately predict the future SOH of a battery, the embodiment of the application provides a battery state of health prediction method, which can be applied to electronic equipment and can predict the battery SOH of a target trolley in the future of a certain target driving mileage through the electronic equipment, wherein the electronic equipment can be a terminal or a server, the terminal can be a smart phone, a tablet computer or a personal computer (Personal Computer, PC) and the like, and the server can be a single server, a server cluster configured in a distributed mode or a cloud server.
As shown in fig. 1, fig. 1 is a schematic diagram of a battery state of health prediction method according to an embodiment of the present application, where the schematic diagram includes: a target electric car 11, a first correspondence 12, and a machine learning model 13.
Firstly, the electronic device may obtain a current driving distance and a current battery parameter of the target electric car 11, where the target electric car 11 may be an electric car shown in fig. 1, or may be an electric two-wheel car, an electric three-wheel car, or the like, and the embodiment of the present application does not limit this, and the current driving distance is used to represent a driving distance that the target electric car 11 has accumulated, and the current battery parameter is a relevant parameter of a battery in the target electric car 11.
Then, the electronic device may determine the current battery health status corresponding to the current driving distance according to the preset first correspondence 12, where the first correspondence 12 is a correspondence between the driving distance and the battery health status, and when the driving distance is known, may determine the battery health status corresponding to the driving distance according to the first correspondence 12.
The electronic device may then input the current driving range, the current battery state of health, and the current battery parameters of the target electric car 11 into the pre-trained machine learning model 13, such that the machine learning model 13 outputs the target battery state of health of the target electric car 11, wherein the target battery state of health is used to characterize the battery state of health of the target electric car 11 at a certain range in the future, that is, the machine learning model 13 may predict the battery state of health of the target electric car 11 at any range in the future according to the current driving range, the current battery state of health, and the current battery parameters of the target electric car 11.
The following will describe a battery state of health prediction method according to an embodiment of the present application in detail with reference to a specific embodiment, as shown in fig. 2, and the specific steps are as follows:
in step 21, the current driving range and the current battery parameters of the target electric car are determined.
In practical application, the service life of an electric car can be generally represented by the accumulated running mileage, for example, when the electric car runs to about 12 km, the electric car will run out of life and be in a scrapped state, and of course, the target electric car can be other types of electric cars, such as an electric two-wheel car, an electric three-wheel car, etc., and the type of the target electric car is not limited in the embodiment of the application.
In addition, in an alternative embodiment, the current battery parameters may include a battery temperature, a discharge current and a discharge voltage, where the battery temperature refers to a phenomenon that the surface of the battery generates heat due to chemical, electrochemical changes, electron migration, substance transmission, and the like of the internal structure of the battery in use, and the battery temperature may be measured by a battery temperature sensor.
Of course, the above battery temperature, discharge current and discharge voltage are only an alternative embodiment of the present battery parameter, and the present battery parameter may also include other data, which is not limited by the embodiment of the present application.
In step 22, the current battery state of health corresponding to the current driving distance is determined based on the preset first correspondence between the driving distance and the battery state of health.
The first correspondence may be a pre-established correspondence, which may represent a correspondence between a driving distance of a certain type of electric car and a state of health of a battery of the type of electric car under a normal condition.
For example, as shown in fig. 3, fig. 3 is a schematic diagram of a driving range in a first correspondence provided in an embodiment of the present application, where the schematic diagram includes: including a number of consecutive driving range segments.
In practical applications, the mileage life of an electric vehicle is about 12 km, that is, when an electric vehicle travels to about 12 km, the life will be exhausted.
Therefore, in the embodiment of the present application, 12 km may be used as an upper limit of the mileage, and a plurality of mileage segments may be preset, as shown in fig. 3, the number axis in fig. 3 is used to represent the mileage range of 0-12 km, where the embodiment of the present application further uses 0.5 km as a length of the mileage segment, and divides 12 km into 24 mileage segments.
In practical applications, a driving distance segment with an excessive range (for example, a driving distance segment length of 1 ten thousand kilometers) may cause too many or too few sample trolleys corresponding to a driving distance segment, for example, 15 sample trolleys corresponding to a driving distance segment of 1 ten thousand kilometers to 2 ten thousand kilometers and 1 sample trolley corresponding to 2 ten thousand kilometers to 3 ten thousand kilometers, which may cause uneven sample distribution and lower prediction accuracy.
Accordingly, if the range included in the driving distance segment is too small (for example, 0.1 km is taken as a driving distance segment length), the data difference corresponding to the adjacent driving distance segment is small, and thus the waste of calculation resources is caused.
Therefore, in a preferred manner, as shown in fig. 3, 0.5 km may be used as the length of the driving range, so that not only can the uniform distribution of samples be ensured, but also certain differences between the data corresponding to the adjacent driving range can be ensured.
In an alternative embodiment, as shown in fig. 4, the first correspondence may be determined based on the following steps:
in step 41, real-time battery parameters of a plurality of sample trolleys and attenuation coefficients of corresponding models of the sample trolleys are acquired.
The model numbers of the sample electric car and the target electric car are the same, and the real-time driving mileage of each sample electric car corresponds to a preset driving mileage section.
At step 42, for each sample trolley, a real-time battery state of health of the sample trolley is determined based on the real-time battery parameters and the decay factor.
The attenuation coefficient can correct the real-time battery parameters when the real-time battery health state of the sample trolley is determined.
In the embodiment of the application, the attenuation coefficient can be determined based on a battery capacity curve of a corresponding model of the sample trolley, and the battery capacity curve is a curve obtained by fitting at a specific temperature.
Specifically, as shown in fig. 5, fig. 5 is a schematic diagram of a battery capacity curve at 25 degrees celsius according to an embodiment of the present application, where the schematic diagram includes: time axis (in hours), battery voltage axis (in volts), current/battery capacity axis (in amperes/amperes), battery voltage profile, battery capacity profile, and charging current profile.
In the embodiment of the present application, the battery capacity curve diagram shown in fig. 5 is obtained by fitting battery data of the same type of electric vehicles during charging, and in the fitting process, data such as current, voltage, battery temperature, time and the like in the process of charging from 20% to 100% of each same type of electric vehicles can be obtained, and the data are fitted to obtain the battery capacity curve shown in fig. 5.
The SOC is used to represent the state of charge of the battery, and is commonly used to reflect the remaining capacity of the battery, where the value is defined as the ratio of the remaining capacity to the battery capacity, and is commonly expressed as a percentage. The SOC ranges from 0 to 1 (i.e., 0% to 100%), and when soc=0, the battery is completely discharged, and when soc=1, the battery is completely charged.
Since the battery temperature cannot be kept constant in the charging process, that is, the battery temperature cannot be maintained at 25 ℃ throughout the process, there is a difference between the data in actual charging and the data in ideal charging (the ideal state is the curve shown in fig. 5), and further, the attenuation coefficient can be determined according to the difference, and specifically, the formula of the attenuation coefficient can be as follows:
wherein P is used for representing attenuation coefficient, the attenuation coefficient can correct battery data to 25 ℃ when the battery is charged, and Charge Energy is used for representing Charge Energy, namely current and soc in a full battery state end For characterizing the remaining charge of the battery at the end of the charge, soc begin For characterizing the remaining charge of the battery at the beginning of the charge, tem_min end Used to characterize the minimum cell temperature when the battery is charged.
After the attenuation coefficient is determined, the real-time battery state of health of each sample trolley may be determined based on the real-time battery parameters and the attenuation coefficient.
In an alternative embodiment, the real-time battery parameters may include a charging current and an accumulated charging time, and further, the step 42 may be specifically performed as: the method includes determining an accumulated charge amount of the sample electric vehicle based on the charge current, the accumulated charge time, and the decay factor, and determining a real-time battery state of health of the sample electric vehicle based on the accumulated charge amount and a nominal capacity of the sample electric vehicle.
Specifically, for the accumulated charge amount of the sample electric car, the accumulated charge amount of the sample electric car may be calculated based on the following formula:
wherein W is t A accumulative charge quantity, P for representing sample electric car t For characterising attenuation coefficient, I t For characterizing the charging current, t for characterizing the time.
In practical application, because the current of the battery is always constant in the charging process, and meanwhile, the electric quantity discharged in the battery working process is almost equal to the electric quantity charged by the battery, the accumulated charging electric quantity calculated based on the charging current is higher in accuracy.
Further, based on the accurate accumulated charge amount and the nominal capacity of the sample trolley, the accurate real-time battery health status of the sample trolley can be determined.
In an alternative embodiment, the process of determining the real-time battery state of health of the sample trolley based on the accumulated charge level and the nominal capacity of the sample trolley may be performed as: and determining the real-time cycle number of the sample trolley based on the accumulated charge quantity and the nominal capacity of the sample trolley, and determining the real-time battery health state corresponding to the real-time cycle number based on the real-time cycle number and a preset second corresponding relation.
The real-time cycle number is used for representing the number of charge/discharge cycles of the sample trolley, the second corresponding relation is used for representing the corresponding relation between the cycle number and the battery health state, and the nominal capacity refers to the battery capacity measured after the battery is completely discharged, namely, the nominal capacity of the battery can be used for representing the storable electric quantity of the battery.
Specifically, after determining the accumulated charge of the sample electric car (i.e., the accumulated charge of the battery in the sample electric car), the accumulated charge of the sample electric car may be divided by the nominal capacity of the sample electric car battery to determine the real-time cycle number.
The formula for determining the real-time cycle times of the sample trolley can be as follows:
wherein N is t For characterising the number of cycles in real time, i.e. the current number of cycles of the sample trolley battery, W t A accumulated charge Capacity, capability for representing above-mentioned sample electric car BOL For characterizing the nominal capacity of the sample trolley battery.
In the embodiment of the application, the accumulated charge quantity is higher in precision, and the nominal capacity is also a more accurate value, so that the real-time cycle times corresponding to the sample trolley can represent the accurate battery cycle times of the sample trolley.
Further, based on the real-time cycle number and a preset second corresponding relation, a real-time battery health state corresponding to the real-time cycle number can be determined.
The second correspondence is used to represent the correspondence between the cycle number and the battery state of health, for example, as shown in fig. 6, fig. 6 is a schematic diagram of the second correspondence provided in the embodiment of the present application, where the schematic diagram includes: a battery cycle number axis, an SOH/% axis, and a line segment for characterizing SOH/% as a function of battery cycle number.
Fig. 6 is a correspondence relationship between the number of battery cycles and the battery capacity fade (SOH) determined with reference to a current magnification of 1C, where C is used to represent the battery charge-discharge capacity magnification and 1C is the current intensity when the battery is fully discharged for one hour. For example, a battery with a nominal capacity of 2200mAh is discharged at a 1C intensity for 1 hour, at which time the discharge current is 2200mA.
As shown in fig. 6, when the number of battery cycles is 0, SOH/% of the battery is 100, that is, when the number of battery cycles is 0, the life consumption of the battery is 0 (i.e., when the SOH of the battery is 100%), and as the number of battery cycles increases, the life of the battery is gradually consumed, and the SOH/% of the battery is lower.
Through the second correspondence shown in fig. 6, after determining the real-time cycle number of the sample electric car, SOH/% corresponding to the real-time cycle number, that is, SOH corresponding to the real-time cycle number, may be determined.
In another alternative embodiment, the second correspondence may also be in the form of a table, as shown in the following table one, which is another table of the second correspondence provided in the embodiment of the present application, and the table is as follows:
list one
| Number of battery cycles | SOH/％ |
| 0-199 | 100 |
| 200-399 | 95 |
| 400-599 | 90 |
| 600-799 | 85 |
| 800-899 | 80 |
| 900-1000 | 75 |
As can be seen from the first table, the relationship between the battery cycle number and the SOH can be determined in the form of a table, and in addition, the second correspondence can be expressed based on other forms, which is not described in detail in the embodiment of the present application.
In step 43, for each driving distance segment, a first correspondence is established based on the real-time battery state of health of each sample electric vehicle.
In an alternative embodiment, step 43 may be performed as: and determining an average value of the real-time battery health states of all the sample electric vehicles corresponding to the driving mileage segments, and establishing a first corresponding relation between the average value of the real-time battery health states and the driving mileage segments.
For example, as shown in fig. 7, fig. 7 is a schematic diagram of a first correspondence relationship according to an embodiment of the present application, where the schematic diagram includes: the system comprises a plurality of axes of continuous driving mileage segments and SOH values corresponding to each driving mileage segment.
As can be seen from fig. 7, when determining the current driving distance of the target electric vehicle, the SOH corresponding to the current driving distance can be determined according to the first correspondence shown in fig. 7, and further, the state of health of the target battery corresponding to the target driving distance of the target electric vehicle can be determined based on the SOH corresponding to the current driving distance.
In step 23, the current battery state of health, the current driving distance and the current battery parameters are input into a pre-trained machine learning model to determine a target battery state of health of the target electric vehicle corresponding to the target driving distance.
The machine learning model is a prediction model, which can output the battery health state corresponding to a certain future driving distance of the target electric car, and in an alternative embodiment, the machine learning model can be constructed based on XGBoost (Extreme Gradient Boosting), a support vector machine (Support Vector Machine, SVM) or a linear regression algorithm (linear regression).
XGBoost is an optimized distributed gradient enhancement library, is an improvement on a gradient lifting algorithm, adopts Newton's method when solving the extremum of a loss function, expands the Taylor of the loss function to be second order, and adds a regularization term into the loss function, so that XGBoost is essentially improved on the basis of a gradient lifting decision tree (Gradient Boosting Decision Tree, GBDT) algorithm, and is more efficient, flexible and portable.
The SVM is a generalized linear classifier (generalized linear classifier) for binary classification of data according to a supervised learning mode, a decision boundary is a maximum margin hyperplane (maximum-margin hyperplane) for solving a learning sample, the SVM calculates an empirical risk (empirical risk) by using a hinge loss function (range loss), and a regularization term is added in a solving system to optimize a structural risk (structural risk), and the SVM is a classifier with sparsity and robustness. The SVM can perform nonlinear classification by a kernel method (kernel method), which is one of the common kernel learning methods.
The linear regression algorithm is a statistical analysis method for determining the quantitative relationship of mutual dependence between two or more variables by using regression analysis in mathematical statistics, and is quite widely used, wherein the linear regression algorithm is a regression analysis for modeling the relationship between one or more independent variables and dependent variables by using a least square function called a linear regression equation, and the function is a linear combination of one or more model parameters called regression coefficients, and the case of only one independent variable is called simple regression and the case of more than one independent variable is called multiple regression.
In an example, the current driving distance of the target electric vehicle a is 5 kilometers, the SOH (i.e., the current battery health state) corresponding to the current driving distance (5 kilometers) of the target electric vehicle a may be determined according to the first correspondence (e.g., the correspondence shown in fig. 7) of the embodiment of the present application, and then the current battery health state, the current driving distance and the current battery parameters may be input into a pre-trained machine learning model to determine the health state of the battery (i.e., the target battery health state) of the target electric vehicle a at 10 kilometers.
According to the embodiment of the application, the current health state corresponding to the current running mileage of the target electric car can be determined through the preset first corresponding relation, then the current battery health state, the current running mileage and the current battery parameters can be synthesized based on the pre-trained machine learning model, and the target battery health state corresponding to the target running mileage of the target electric car can be predicted.
In one example, the embodiment of the application randomly selects 4 types of target trolleys (a target trolley A, a target trolley B, a target trolley C and a target trolley D) for experiments, and the 4 types of target trolleys are respectively predicted by the battery health state prediction method provided by the embodiment of the application.
The specific results are shown in the following table:
watch II
The capacity in table two to the battery capacity, the predicted capacity can be calculated based on the predicted SOH. In addition, the first data set and the second data set respectively represent mileage parameters and battery parameters of each target electric car in two different time periods.
As can be seen from the second table, according to the embodiment of the application, the prediction error of the battery capacity of each target electric car (i.e. the battery SOH of the target electric car) is less than 5%, and the accuracy is high.
In another example, the embodiment of the present application selects data (mileage parameter and battery parameter) of the target electric car E in a first period of time and predicts SOH of the target electric car E at 12 kilometers (target mileage) based on the data of the first period of time, and then the embodiment of the present application also selects data (mileage parameter and battery parameter) of the target electric car E in a second period of time and predicts SOH of the target electric car E at 12 kilometers (target mileage) based on the data of the second period of time.
Specifically, as shown in fig. 8, fig. 8 is a comparison chart of experimental results provided in the embodiment of the present application, where the comparison chart includes: a driving distance axis (in ten thousand kilometers), an SOH (%) axis, an SOH change trend line segment of the target electric car E predicted based on the data of the first period, an SOH change trend line segment of the target electric car E predicted based on the data of the second period, and measured data.
As can be seen from fig. 8, according to the embodiment of the present application, the error between the SOH predicted value and the measured value of the target electric car E is smaller, i.e. the prediction method according to the embodiment of the present application has higher accuracy.
Based on the same technical concept, the embodiment of the application further provides a device for predicting the health state of a battery, as shown in fig. 9, the device comprises: a first determination module 91, a second determination module 92 and a prediction module 93.
The first determining module 91 is configured to determine a current driving range and a current battery parameter of the target electric vehicle.
The second determining module 92 is configured to determine, based on a first correspondence between preset driving ranges and battery health states, a current battery health state corresponding to the current driving range.
The prediction module 93 is configured to input the current battery state of health, the current driving distance, and the current battery parameter into a pre-trained machine learning model, so as to determine a target battery state of health of the target electric vehicle corresponding to the target driving distance.
Optionally, the first correspondence is determined based on the following modules:
the acquisition module is used for acquiring real-time battery parameters of a plurality of sample trolleys and attenuation coefficients of corresponding models of the sample trolleys, the models of the sample trolleys are the same as that of the target trolleys, and real-time driving mileage of each sample trolleybus corresponds to a preset driving mileage section.
And the third determining module is used for determining the real-time battery health state of each sample trolley based on the real-time battery parameters and the attenuation coefficient.
The building module is used for building a first corresponding relation based on the real-time battery health state of each sample electric car for each driving mileage.
Optionally, the real-time battery parameters include a charging current and an accumulated charging time.
The third determining module is specifically configured to:
based on the charging current, the accumulated charging time, and the decay factor, an accumulated charge amount of the sample electric car is determined.
Based on the accumulated charge amount and the nominal capacity of the sample trolley, a real-time battery state of health of the sample trolley is determined.
Optionally, the third determining module is specifically configured to:
based on the accumulated charge quantity and the nominal capacity of the sample trolley, a real-time number of cycles of the sample trolley is determined, the real-time number of cycles being used to characterize the number of charge/discharge cycles of the sample trolley.
And determining the real-time battery health state corresponding to the real-time cycle times based on the real-time cycle times and a preset second corresponding relation, wherein the second corresponding relation is used for representing the corresponding relation between the cycle times and the battery health state.
Optionally, the establishing module is specifically configured to:
and determining the average value of the real-time battery health states of all the sample electric vehicles corresponding to the driving mileage.
And establishing a first corresponding relation between the average value of the real-time battery health state and the driving mileage.
Optionally, the attenuation coefficient is determined based on a battery capacity curve of a corresponding model of the sample trolley, and the battery capacity curve is a curve obtained by fitting at a specific temperature.
Optionally, the current battery parameters include battery temperature, discharge current, and discharge voltage.
Alternatively, the machine learning model is built based on XGBoost, support vector machine, or linear regression algorithm.
According to the embodiment of the application, the current health state corresponding to the current running mileage of the target electric car can be determined through the preset first corresponding relation, then the current battery health state, the current running mileage and the current battery parameters can be synthesized based on the pre-trained machine learning model, and the target battery health state corresponding to the target running mileage of the target electric car can be predicted.
Fig. 10 is a schematic diagram of an electronic device according to an embodiment of the application. As shown in fig. 10, the electronic device shown in fig. 10 is a general address query device, which includes a general computer hardware structure including at least a processor 101 and a memory 102. The processor 101 and the memory 102 are connected by a bus 103. The memory 102 is adapted to store instructions or programs executable by the processor 101. The processor 101 may be a separate microprocessor or may be a collection of one or more microprocessors. Thus, the processor 101 implements processing of data and control of other devices by executing instructions stored by the memory 102 to perform the method flows of embodiments of the application as described above. Bus 103 connects the above components together and connects the above components to display controller 104 and display device and input/output (I/O) device 105. Input/output (I/O) device 105 may be a mouse, keyboard, modem, network interface, touch input device, somatosensory input device, printer, and other devices known in the art. Typically, the input/output devices 105 are connected to the system through input/output (I/O) controllers 106.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, apparatus (device) or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may employ a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations of methods, apparatus (devices) and computer program products according to embodiments of the application. It will be understood that each of the flows in the flowchart may be implemented by computer program instructions.
These computer program instructions may be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
Another embodiment of the present application is directed to a non-volatile storage medium storing a computer readable program for causing a computer to perform some or all of the method embodiments described above.
That is, it will be understood by those skilled in the art that all or part of the steps in implementing the methods of the embodiments described above may be implemented by specifying relevant hardware by a program, where the program is stored in a storage medium, and includes several instructions for causing a device (which may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps in the methods of the embodiments of the application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Another embodiment of the application relates to a computer program product comprising a computer program/instruction which, when executed by a processor, can implement some or all of the above-described method embodiments.
That is, those skilled in the art will appreciate that embodiments of the application may be implemented by a processor executing a computer program product (computer program/instructions) to specify associated hardware, including the processor itself, to carry out all or part of the steps of the methods of the embodiments described above.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, and various modifications and variations may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (16)
1. A method of predicting a state of health of a battery, the method comprising:
determining the current driving mileage and the current battery parameter of the target electric car;
determining the current battery health state corresponding to the current driving mileage based on a first corresponding relation between the preset driving mileage and the battery health state; and
inputting the current battery health state, the current driving mileage and the current battery parameters into a pre-trained machine learning model to determine a target battery health state of the target electric car corresponding to the target driving mileage;
the first corresponding relation corresponds to the model of the target electric car, and the first corresponding relation characterizes the corresponding relation between a preset driving mileage section and a battery health state;
the first correspondence is determined based on the steps of:
acquiring real-time battery parameters of a plurality of sample trolleys and attenuation coefficients of corresponding models of the sample trolleys, wherein the models of the sample trolleys are the same as that of the target trolleys, and the real-time driving mileage of each sample trolleybus corresponds to a preset driving mileage section;
for each sample trolley, determining a real-time battery state of health of the sample trolley based on the real-time battery parameters and the attenuation coefficient; and
establishing the first corresponding relation based on the real-time battery health status of each sample electric car for each driving mileage;
wherein the attenuation coefficient is determined based on the following formula:
wherein, the P is used for representing the attenuation coefficient, the Charge Energy is used for representing Charge Energy, and the soc end For characterizing the remaining battery power at the end of charging, said soc begin For characterizing the remaining battery power at the beginning of the charge, said tem_min end Used to characterize the minimum cell temperature when the battery is charged.
2. The method of claim 1, wherein the real-time battery parameters include a charging current and an accumulated charging time;
the determining the real-time battery health status of the sample trolley based on the real-time battery parameters and the attenuation coefficient includes:
determining an accumulated charge quantity of the sample electric car based on the charge current, the accumulated charge time, and the decay factor; and
and determining a real-time battery health state of the sample trolley based on the accumulated charge quantity and a nominal capacity of the sample trolley.
3. The method of claim 2, wherein the determining the real-time battery health status of the sample electric vehicle based on the accumulated charge and the nominal capacity of the sample electric vehicle comprises:
determining a real-time number of cycles of the sample trolley based on the accumulated charge quantity and a nominal capacity of the sample trolley, the real-time number of cycles being used to characterize a number of charge/discharge cycles of the sample trolley; and
and determining the real-time battery health state corresponding to the real-time cycle times based on the real-time cycle times and a preset second corresponding relation, wherein the second corresponding relation is used for representing the corresponding relation between the cycle times and the battery health state.
4. The method of claim 1, wherein establishing the first correspondence based on real-time battery health status of each sample trolley comprises:
determining an average value of the real-time battery health states of all the sample electric vehicles corresponding to the driving mileage; and
and establishing a first corresponding relation between the average value of the real-time battery health state and the driving mileage.
5. The method of claim 1, wherein the attenuation coefficient is determined based on a battery capacity curve of the corresponding model of the sample electric car, the battery capacity curve being a curve fitted at a specific temperature.
6. The method of claim 1, wherein the current battery parameters include battery temperature, discharge current, and discharge voltage.
7. The method of claim 1, wherein the machine learning model is constructed based on XGBoost, support vector machine, or linear regression algorithm.
8. A battery state of health prediction apparatus, the apparatus comprising:
the first determining module is used for determining the current driving mileage and the current battery parameter of the target electric car;
the second determining module is used for determining the current battery health state corresponding to the current driving mileage based on a first corresponding relation between the preset driving mileage and the battery health state; and
the prediction module is used for inputting the current battery health state, the current driving mileage and the current battery parameters into a pre-trained machine learning model so as to determine a target battery health state of the target electric car corresponding to the target driving mileage;
the first corresponding relation corresponds to the model of the target electric car, and the first corresponding relation characterizes the corresponding relation between a preset driving mileage section and a battery health state;
the first correspondence is determined based on the following modules:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring real-time battery parameters of a plurality of sample trolleys and attenuation coefficients of corresponding models of the sample trolleys, the model of the sample trolleys is the same as that of the target trolleys, and the real-time driving mileage of each sample trolleybus corresponds to a preset driving mileage section;
a third determining module, configured to determine, for each sample trolley, a real-time battery health status of the sample trolley based on the real-time battery parameter and the attenuation coefficient; and
the building module is used for building the first corresponding relation based on the real-time battery health state of each sample trolley for each driving mileage;
wherein the attenuation coefficient is determined based on the following formula:
wherein, the P is used for representing the attenuation coefficient, the Charge Energy is used for representing Charge Energy, and the soc end For characterizing the remaining battery power at the end of charging, said soc begin For characterizing the remaining battery power at the beginning of the charge, said tem_min end Used to characterize the minimum cell temperature when the battery is charged.
9. The apparatus of claim 8, wherein the real-time battery parameters include a charging current and an accumulated charging time;
the third determining module is specifically configured to:
determining an accumulated charge quantity of the sample electric car based on the charge current, the accumulated charge time, and the decay factor; and
and determining a real-time battery health state of the sample trolley based on the accumulated charge quantity and a nominal capacity of the sample trolley.
10. The apparatus according to claim 9, wherein the third determining module is specifically configured to:
determining a real-time number of cycles of the sample trolley based on the accumulated charge quantity and a nominal capacity of the sample trolley, the real-time number of cycles being used to characterize a number of charge/discharge cycles of the sample trolley; and
and determining the real-time battery health state corresponding to the real-time cycle times based on the real-time cycle times and a preset second corresponding relation, wherein the second corresponding relation is used for representing the corresponding relation between the cycle times and the battery health state.
11. The apparatus according to claim 8, wherein the establishing module is specifically configured to:
determining an average value of the real-time battery health states of all the sample electric vehicles corresponding to the driving mileage; and
and establishing a first corresponding relation between the average value of the real-time battery health state and the driving mileage.
12. The apparatus of claim 8, wherein the attenuation coefficient is determined based on a battery capacity curve of a corresponding model of the sample electric car, the battery capacity curve being a curve fitted at a specific temperature.
13. The apparatus of claim 8, wherein the current battery parameters include battery temperature, discharge current, and discharge voltage.
14. The apparatus of claim 8, wherein the machine learning model is constructed based on XGBoost, support vector machine, or linear regression algorithm.
15. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method of any of claims 1-7.
16. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program which, when executed by a processor, implements the method of any of claims 1-7.
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