CN112924887B - Battery pack health detection method and device, readable storage medium and electronic equipment - Google Patents
Battery pack health detection method and device, readable storage medium and electronic equipment Download PDFInfo
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- G—PHYSICS
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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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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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]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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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]
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Abstract
A battery pack health detection method, a device, a readable storage medium and an electronic apparatus, the method includes: obtaining the internal resistance of each single battery of the battery pack in a preset time period; determining corresponding fault evaluation parameters according to the internal resistances of all the single batteries, wherein the fault evaluation parameters comprise the Z fraction of the internal resistances of the single batteries, the accumulated deviation of the internal resistances of the single batteries and the cosine set variance of the internal resistances of the single batteries and the adjacent two single batteries; comparing the evaluation parameters corresponding to each single battery with preset parameter conditions corresponding to each health type to determine the health type of each single battery, wherein the health type comprises normal, slight abnormality and abnormality; counting the number of the single batteries of the normal type, the slight abnormal type and the abnormal type respectively; and comparing the number of the single batteries of the normal type, the slight abnormal type and the abnormal type with preset parameter conditions corresponding to the health states so as to determine the health state of the battery pack.
Description
Technical Field
The present invention relates to the field of automobiles, and in particular, to a method and apparatus for detecting health of a battery pack, a readable storage medium, and an electronic device.
Background
With the rapid development of the automobile industry, the requirements of people on the safety performance of the automobile are higher and higher, a battery system is an important component part of the automobile, and the safety problem is a bottleneck for limiting the rapid development of the automobile industry. The battery system may have various faults due to the influence of poor abuse resistance, external environment and use conditions, and the accelerated degradation of the power battery is caused, even the safety accidents such as electrolyte leakage, fire, explosion and the like are caused. Fault diagnosis studies are therefore important to improve the safety of battery systems.
At present, most vehicle enterprises only detect faults of a battery system, and abnormality or fault judgment is based on parameters such as voltage, pressure difference, temperature difference and the like, when the parameters are abnormal to make judgment, the vehicle is often in a serious safety problem and fault or safety accidents such as fire and explosion of a power battery are happened. Therefore, the current battery detection method has certain hysteresis, and the state of health of the battery cannot be detected timely and accurately.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method and apparatus for detecting the health of a battery pack, a readable storage medium and an electronic device, which solve the problem that the health of the battery pack in the prior art cannot be detected timely and accurately.
A battery pack health detection method comprising:
obtaining the internal resistance of each single battery of the battery pack in a preset time period;
Determining corresponding fault evaluation parameters according to the internal resistances of all the single batteries, wherein the fault evaluation parameters comprise the Z fraction of the internal resistances of the single batteries, the accumulated deviation of the internal resistances of the single batteries and the cosine set variance of the internal resistances of the single batteries and the adjacent two single batteries;
Comparing the evaluation parameters corresponding to the single batteries with preset parameter conditions corresponding to the health types to determine the health types of the single batteries, wherein the health types comprise normal, slight abnormality and abnormality;
Counting the number of the single batteries of the normal type, the slight abnormal type and the abnormal type respectively;
And comparing the number of the normal type, the slight abnormal type and the abnormal type single batteries with preset parameter conditions corresponding to each health state to determine the health state of the battery pack.
Further, in the above battery pack health detection method, the step of comparing the number of the normal type, the slight abnormal type and the abnormal type of the single batteries with preset parameter conditions corresponding to each health state to determine the health state of the battery pack includes:
when the percentage ratio of the abnormal type single batteries in the battery pack is larger than a first threshold ratio, determining that the battery pack is in a fault state;
When the percentage ratio of the abnormal type single batteries in the battery pack is smaller than or equal to a first threshold ratio and the percentage ratio of the normal type single batteries is larger than or equal to a second threshold ratio, determining that the battery pack is in a good state;
When the percentage ratio of the abnormal type single batteries in the battery pack is smaller than or equal to a first threshold ratio and the percentage ratio of the normal type single batteries is smaller than a second threshold ratio, determining that the battery pack is in a sub-health state; wherein the second threshold duty cycle is greater than the first threshold proportion.
Further, in the above battery pack health detection method, the step of comparing the evaluation parameter corresponding to each of the single batteries with the preset parameter condition corresponding to each of the health types to determine the health type of each of the single batteries includes:
When the absolute value of the Z fraction of the detected internal resistance of the current single battery is smaller than or equal to a first threshold value, and when the accumulated deviation of the internal resistances of the current single battery is smaller than the first threshold value deviation and the cosine set variance of the included angles of the internal resistances of the current single battery and the adjacent two single batteries meets a first target condition, determining that the current single battery is of a normal type, wherein the first target condition is as follows:
Wherein Cos ave is the average value of cosine sets of all included angles, and IRV is the variance of cosine sets of the internal resistances of the current single battery and two adjacent single batteries;
When the absolute value of the Z fraction of the internal resistance of the current single battery is larger than the first threshold and smaller than or equal to a second threshold, determining that the current single battery is of a slight abnormal type, or when the accumulated deviation of the internal resistance of the current single battery is larger than or equal to the first threshold deviation, determining that the current single battery is of a slight abnormal type, or when the cosine set variance of the internal resistance included angles of the current single battery and two adjacent single batteries meets a second target condition, determining that the current single battery is of a normal type, wherein the second target condition is that:
and when the absolute value of the Z fraction of the internal resistance of the current single battery is larger than the second threshold value, determining that the current single battery is of an abnormal type.
Further, the method for detecting the health of the battery pack, wherein the step of detecting the internal resistance of each single battery of the battery pack in the preset time period includes:
acquiring driving data of a vehicle in a preset time period, wherein the driving data comprises total voltage of a battery pack and current and voltage of each single battery, and the preset time period comprises time periods of two states of starting and flameout of the vehicle;
and determining the internal resistance of each single battery of the battery pack according to the driving data.
Further, in the above battery pack health detection method, the calculation formula of the Z fraction of the internal resistance of the single battery is:
Wherein Z i,j is the internal resistance Z fraction of the single battery j at the moment i, R i,j is the internal resistance of the j-th single battery at the moment i, R ave is the average value of the internal resistances of the single batteries at the moment i, and sigma R is the standard deviation of the internal resistances of the single batteries at the moment i.
Further, in the above battery pack health detection method, a calculation formula of the accumulated deviation of the unit battery is:
Wherein D i is the accumulated deviation of the internal resistances of the I-number single batteries in the time period from t1 to t2, U t is the total voltage of the batteries at the time t, I t is the current of the batteries at the time t, and n is the number of single batteries.
Further, in the above battery pack health detection method, a calculation formula of an included angle cosine set variance of internal resistances of any one single battery and two adjacent single batteries is as follows:
Wherein IRV is cosine set variance of the included angle between the internal resistances of the single battery A and the adjacent two single batteries, Vectors formed for internal resistances of the body cell A and the unit cell B, and/>The vector is formed by the internal resistances of the single battery A and the single battery C in the battery pack.
The invention also discloses a battery pack health detection method, which comprises the following steps:
The acquisition module is used for acquiring the internal resistances of all the single batteries of the battery pack in a preset time period;
the first determining module is used for determining corresponding fault evaluation parameters according to the internal resistances of the single batteries, wherein the fault evaluation parameters comprise a Z fraction of the internal resistances of the single batteries, accumulated deviation of the internal resistances of the single batteries and cosine set variance of the internal resistances of the single batteries and two adjacent single batteries;
The second determining module is used for comparing the evaluation parameters corresponding to the single batteries with preset parameter conditions corresponding to the health types so as to determine the health types of the single batteries, wherein the health types comprise normal, slight abnormality and abnormality;
The statistics module is used for respectively counting the number of the single batteries of the normal type, the slight abnormal type and the abnormal type;
and the third determining module is used for comparing the number of the normal type, the slight abnormal type and the abnormal type single batteries with preset parameter conditions corresponding to each health state so as to determine the health state of the battery pack.
The invention also discloses a readable storage medium having stored thereon a program which when executed by a processor implements any of the methods described above.
The invention also discloses an electronic device, which comprises a memory, a processor and a program stored on the memory and capable of running on the processor, wherein the processor realizes the method of any one of the above when executing the program.
The invention evaluates the health state of the battery pack through the Z fraction of the internal resistance of each single battery, the accumulated deviation of the internal resistance of the single battery and the cosine set variance of the included angle of the internal resistance of the single battery, can effectively and timely find out the abnormality of the battery pack, timely find out the failure problem of the battery and the single battery transmitting the failure before the safety problem and the failure occur, effectively reduce the occurrence of safety accidents such as fire and explosion of the electric automobile and avoid the loss of huge economy of life and property.
Drawings
Fig. 1 is a flowchart of a battery pack health detection method according to a first embodiment of the present invention;
Fig. 2 is a flowchart of a battery pack health detection method according to a second embodiment of the present invention;
Fig. 3 is a block diagram illustrating a structure of a battery pack health detection apparatus according to a third embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
These and other aspects of embodiments of the invention will be apparent from and elucidated with reference to the description and drawings described hereinafter. In the description and drawings, particular implementations of embodiments of the invention are disclosed in detail as being indicative of some of the ways in which the principles of embodiments of the invention may be employed, but it is understood that the scope of the embodiments of the invention is not limited correspondingly. On the contrary, the embodiments of the invention include all alternatives, modifications and equivalents as may be included within the spirit and scope of the appended claims.
The battery pack fault state assessment method is suitable for all types of electric vehicles, calculates the internal resistance of the battery by using ohm law based on the data of the real-time running process of the electric vehicle, monitors each internal resistance of the battery in real time by using a Z-score model, a statistical model and an angle variance model according to the calculated internal resistance of the single battery, and identifies the health state of the battery pack according to the monitoring result so as to achieve the aim of early warning in time or even in advance.
Referring to fig. 1, a method for detecting the health of a battery pack according to a first embodiment of the present invention includes steps S11 to S15.
Step S11, obtaining the internal resistances of all the single batteries of the battery pack in a preset time period.
The internal resistances of the individual battery cells can be calculated from the operation data of the vehicle. In the implementation, the operation data of the vehicle is extracted from the vehicle networking platform, and the data of the parameters of the single battery in the preset time period are obtained from the historical data. The vehicle operation data must have 4 parameters: time, current, total voltage, cell voltage. The predetermined time period is, for example, data of a period of time during a start of the vehicle and/or data of a period of time during a flameout of the vehicle.
When the automobile starts and stops, the battery is in a charging state and a discharging state respectively, the voltage, the current and the like of the battery are in a stable state, and the data are credible. Thus, driving data in the vehicle start-up and/or flameout state is acquired. Meanwhile, only driving data of one vehicle are extracted at a time, and the discharging and charging process data are not less than 100.
Further, the acquired driving data requires voltage values of 2.5-5V, if the voltage values exceed the range, the single battery or the whole battery system can be initially indicated to be abnormal, and meanwhile, the data of the driving data, of which the current values are between 0 and 5A, are required to be not less than 20, so that the reliability of the data is ensured.
Further, due to the complexity of the data transmission environment and the possible interference of the data input end, some invalid or abnormal data exist in the data recorded on the internet of vehicles platform, and the data are quite common, such as incomplete data, abnormal data and empty data. Based on the method, firstly, abnormal and invalid data on the Internet of vehicles are processed to obtain useful and real data of the vehicles, and interference of data problems on early warning results is eliminated.
The calculation formula of the internal resistance of the single battery according to ohm law is as follows:
Wherein, R i,j is the internal resistance of the jth battery cell at the moment I, U i,j is the voltage of the jth battery cell at the moment I, and I i is the current at the moment I.
Step S12, corresponding fault evaluation parameters are determined according to the internal resistances of the single batteries, wherein the fault evaluation parameters comprise the Z fraction of the internal resistances of the single batteries, the accumulated deviation of the internal resistances of the single batteries and the cosine set variance of the internal resistances of the single batteries and the adjacent single batteries.
And S13, comparing the evaluation parameters corresponding to the single batteries with preset parameter conditions corresponding to the health types to determine the health types of the single batteries, wherein the health types comprise normal, slight abnormal and abnormal.
In the present embodiment, the health type of the unit cell is evaluated by each of the failure evaluation parameters. The evaluation parameters comprise the Z fraction of the internal resistance of the single battery, the accumulated deviation of the internal resistances of the single battery and the cosine set variance of the internal resistances of the single battery and two adjacent single batteries. The Z fraction of the internal resistance of the single battery, the accumulated deviation of the internal resistance and the cosine set variance of the included angle of the internal resistance can be used for indicating the health condition of the single battery.
Step S14, counting the number of the single batteries of the normal type, the slight abnormal type and the abnormal type respectively.
And S15, comparing the number of the normal type, the slight abnormal type and the abnormal type single batteries with preset parameter conditions corresponding to each health state to determine the health state of the battery pack.
After the health type of each single battery is determined, the state of the battery pack is determined according to the parameter conditions corresponding to the health state of the battery pack set by the system. The state of health of the battery pack may be set to various states, such as a good state, a sub-health state, and a fault state, and it is understood that only two states of health, namely a good state and a fault state, may be set in other embodiments of the present invention. Each state of health is provided with a corresponding parameter condition, for example, the battery pack is considered to be in a good state if the normal type of single battery accounts for more than 95%, and is considered to be in a fault state if the fault type of single battery exceeds 2%.
According to the embodiment, the health state of the battery pack is evaluated through the Z fraction of the internal resistance of each single battery, the accumulated deviation of the internal resistance of the single battery and the cosine set variance of the included angle of the internal resistance of the single battery, so that the abnormality of the battery pack can be effectively and timely found, the fault problem of the battery and the single battery emitting the fault can be timely found before the safety problem and the fault occur, the occurrence of safety accidents such as fire and explosion of the electric automobile can be effectively reduced, and huge economic losses of life and property can be avoided.
Referring to fig. 2, a method for detecting the health of a battery pack according to a second embodiment of the invention includes steps S21 to S27.
Step S21, obtaining the internal resistances of all the single batteries of the battery pack in a preset time period.
Step S22, determining a fault evaluation parameter of the battery pack according to the internal resistance of each single battery, wherein the fault evaluation parameter comprises a Z fraction of the internal resistance of the single battery, an accumulated deviation of the internal resistances of the single battery and an included angle cosine set variance of the internal resistances of the single battery and two adjacent single batteries.
Step S23, comparing the evaluation parameters corresponding to the individual battery cells with preset parameter conditions corresponding to the individual health types, so as to determine the health type of the individual battery cells, wherein the health type includes normal, slightly abnormal and abnormal.
The health type of the single battery can be determined according to three indexes of the Z fraction of the internal resistance, the accumulated deviation and the cosine set variance of the included angle, and the health state of the single battery can be evaluated from different levels by different indexes, so that the health state of the single battery can be determined more accurately and comprehensively, and the evaluation accuracy of a battery pack is improved.
The step of comparing the evaluation parameters corresponding to the single batteries with preset parameter conditions corresponding to the health types to determine the health types of the single batteries comprises the following steps:
When the absolute value of the Z fraction of the detected internal resistance of the current single battery is smaller than or equal to a first threshold value, and when the accumulated deviation of the internal resistances of the current single battery is smaller than the first threshold value deviation and the cosine set variance of the included angles of the internal resistances of the current single battery and the adjacent two single batteries meets a first target condition, determining that the current single battery is of a normal type, wherein the first target condition is as follows:
Wherein Cos ave is the average value of cosine sets of all included angles, and IRV is the variance of cosine sets of the internal resistances of the current single battery and two adjacent single batteries;
when the absolute value of the Z fraction of the internal resistance of the current single battery is larger than the first threshold and smaller than or equal to a second threshold, determining that the current single battery is of a slight abnormal type, or when the accumulated deviation of the internal resistance of the current single battery is larger than or equal to the first threshold deviation, determining that the current single battery is of a slight abnormal type, or when the cosine set variance of the internal resistance included angles of the current single battery and two adjacent single batteries meets a second target condition, determining that the current single battery is of a normal type, wherein the second target condition is that:
and when the absolute value of the Z fraction of the internal resistance of the current single battery is larger than the second threshold value, determining that the current single battery is abnormal.
In the above step, the calculation formula of the Z fraction of the internal resistance of the single battery is:
Wherein Z i,j is the internal resistance Z fraction of the single battery j at the moment i, R i,j is the internal resistance of the j-th single battery at the moment i, R ave is the average value of the internal resistances of the single batteries at the moment i, and sigma R is the standard deviation of the internal resistances of the single batteries at the moment i.
The calculation formula of the accumulated deviation of the internal resistance of the single battery is as follows:
Wherein D i is the accumulated deviation of the internal resistances of the I-number single batteries in the time period from t1 to t2, U t is the total voltage of the batteries at the time t, I t is the current of the batteries at the time t, and n is the number of single batteries.
It can be understood that the accumulated deviation of the single batteries is calculated according to the internal resistance of the preset time period. In specific implementation, the acquired driving data in the preset time period are divided according to the preset time period, and data in a plurality of time periods are obtained. The preset duration can be determined according to the acquisition condition of travel data, for example, the travel data of the vehicle in the Internet of vehicles within 5 minutes of starting and 5 minutes of flameout of the vehicle are acquired during evaluation, the travel data are acquired once for 1s, and the preset duration can be set to be 10s. Namely, the accumulated deviation of the internal resistances of all the single batteries in the equal time periods of 0 to 10s,10 to 20s and 20 to 30s is counted.
The calculation formula of the cosine set variance of the included angle of the internal resistance of any single battery and the internal resistances of two adjacent single batteries is as follows:
Wherein IRV is cosine set variance of the included angle between the internal resistances of the single battery A and the adjacent two single batteries, Vectors formed for internal resistances of the body cell A and the unit cell B, and/>The vector is formed by the internal resistances of the single battery A and the single battery C in the battery pack.
The first threshold and the second threshold are set according to practical situations, for example, to 2.5 and 3.5, respectively. The first threshold deviation may be the 95 th percentile D 95 of the Di values over each time period.
Whether the single battery i is in a normal state or not is comprehensively determined according to three indexes (Z fraction, accumulated deviation and included angle cosine set variance), namely when the single battery i is |Z i,j|≤2.5、Di＜D95+ andWhen this cell i is of the normal type.
When 2.5 < |Z i,j |is less than or equal to 3.5, or Di is more than or equal to D 95+ orIndicating that the cell i is slightly abnormal.
When |Zi, j| > 3.5, the single battery i is of an abnormal type.
Step S24, counting the number of the single batteries of the normal type, the slight abnormal type and the abnormal type respectively.
And S25, when the percentage ratio of the abnormal type single batteries in the battery pack is larger than a first threshold ratio, determining that the battery pack is in a fault state.
And S26, determining that the battery pack is in a good state when the percentage ratio of the abnormal type single batteries in the battery pack is smaller than or equal to a first threshold ratio and the percentage ratio of the normal type single batteries is larger than or equal to a second threshold ratio.
And step S27, when the percentage ratio of the abnormal type single batteries in the battery pack is smaller than or equal to a first threshold ratio and the percentage ratio of the normal type single batteries is smaller than the second threshold ratio, determining that the battery pack is in a sub-health state. Wherein the second threshold duty cycle is greater than the first threshold proportion.
The first threshold ratio and the second threshold ratio can be set according to actual needs, and in general, the unit batteries in the battery pack in the electric automobile are different from hundreds to thousands, so that the normal operation of the battery pack is not affected by the abnormality or slight abnormality of a certain unit battery. For example, the first threshold ratio may be set to 2% and the second threshold ratio may be set to 95%.
When the percentage ratio of the abnormal type single battery is more than 2%, the battery pack is indicated to be faulty, and the state of health of the battery pack is determined to be the fault state.
And when the percentage ratio of the abnormal type single battery is less than or equal to 2 percent and the percentage ratio of the normal type single battery is greater than or equal to 95 percent, determining that the battery pack is in a healthy state. The percentage ratio of the normal type, the slight abnormal type and the abnormal type of the unit cells should be equal to 100%. Therefore, the percentage of the slightly abnormal single battery in the healthy state is between 0 and 5%.
And when the percentage ratio of the abnormal type single battery is less than or equal to 2 percent and the percentage ratio of the normal type single battery is less than 95 percent, determining that the battery pack is in a sub-health state.
It will be appreciated that battery cars also require that all cells be normal, and that the performance or life of the battery pack may be affected if an abnormal cell occurs. Thus, in other embodiments of the present invention, the first threshold may be set to 0% and the second threshold may be set according to actual needs, for example, up to 20% of slightly abnormal cells may be tolerated, and the second threshold is 80%.
Further, after the health state of the battery pack is determined, corresponding measures can be adopted, for example, when the battery pack is determined to be in a sub-health state, the battery pack can be monitored in a key manner, and when the battery pack is determined to be in a fault state, early warning information is sent, wherein the early warning information comprises the monomer numbers of the monomer batteries of the abnormal type. The early warning information can be uploaded to the cloud, or sent to the user through mail, or sent to the user through short message.
Referring to fig. 3, a method for detecting health of a battery pack according to a third embodiment of the present invention includes:
an obtaining module 10, configured to obtain internal resistances of individual battery cells of the battery pack in a preset time period;
The first determining module 20 is configured to determine corresponding fault evaluation parameters according to internal resistances of the individual battery cells, where the fault evaluation parameters include a Z fraction of the internal resistances of the individual battery cells, an accumulated deviation of the internal resistances of the individual battery cells, and an included angle cosine set variance of the internal resistances of the individual battery cells and two adjacent individual battery cells;
The second determining module 30 compares the evaluation parameters corresponding to the single batteries with preset parameter conditions corresponding to the health types to determine the health types of the single batteries, wherein the health types comprise normal, slightly abnormal and abnormal;
A statistics module 40 for counting the number of the unit cells of the normal type, the slight abnormal type and the abnormal type, respectively;
And a third determining module 50, configured to compare the number of the normal type, the slight abnormal type, and the abnormal type of the single batteries with preset parameter conditions corresponding to respective health states, so as to determine the health state of the battery pack.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
The battery fault detection device provided by the embodiment of the invention has the same implementation principle and technical effects as those of the embodiment of the method, and for the sake of brevity, reference may be made to the corresponding content in the embodiment of the method.
The embodiment of the invention also provides a readable storage medium, on which a program is stored, which when executed by a processor, implements any of the methods described above.
The embodiment of the invention also provides electronic equipment, which comprises a memory, a processor and a program stored on the memory and capable of running on the processor, wherein the processor realizes the method of any one of the above when executing the program.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (8)
1. A battery pack health detection method, comprising:
obtaining the internal resistance of each single battery of the battery pack in a preset time period;
Determining corresponding fault evaluation parameters according to the internal resistances of all the single batteries, wherein the fault evaluation parameters comprise the Z fraction of the internal resistances of the single batteries, the accumulated deviation of the internal resistances of the single batteries and the cosine set variance of the internal resistances of the single batteries and the adjacent two single batteries;
Comparing the evaluation parameters corresponding to the single batteries with preset parameter conditions corresponding to the health types to determine the health types of the single batteries, wherein the health types comprise normal, slight abnormal and abnormal, and the specific steps comprise:
When the absolute value of the Z fraction of the detected internal resistance of the current single battery is smaller than or equal to a first threshold value, and when the accumulated deviation of the internal resistances of the current single battery is smaller than the first threshold value deviation and the cosine set variance of the included angles of the internal resistances of the current single battery and the adjacent two single batteries meets a first target condition, determining that the current single battery is of a normal type, wherein the first target condition is as follows:
Wherein Cos ave is the average value of cosine sets of all included angles, and IRV is the variance of cosine sets of the internal resistances of the current single battery and two adjacent single batteries;
When the absolute value of the Z fraction of the internal resistance of the current single battery is larger than the first threshold and smaller than or equal to a second threshold, determining that the current single battery is of a slight abnormal type, or when the accumulated deviation of the internal resistance of the current single battery is larger than or equal to the first threshold deviation, determining that the current single battery is of a slight abnormal type, or when the cosine set variance of the internal resistance included angles of the current single battery and two adjacent single batteries meets a second target condition, determining that the current single battery is of a normal type, wherein the second target condition is that:
When the absolute value of the Z fraction of the internal resistance of the current single battery is larger than the second threshold value, determining that the current single battery is of an abnormal type;
Counting the number of the single batteries of the normal type, the slight abnormal type and the abnormal type respectively;
Comparing the number of the normal type, the slight abnormal type and the abnormal type of single batteries with preset parameter conditions corresponding to each health state to determine the health state of the battery pack, wherein the specific steps comprise:
when the percentage ratio of the abnormal type single batteries in the battery pack is larger than a first threshold ratio, determining that the battery pack is in a fault state;
When the percentage ratio of the abnormal type single batteries in the battery pack is smaller than or equal to a first threshold ratio and the percentage ratio of the normal type single batteries is larger than or equal to a second threshold ratio, determining that the battery pack is in a good state;
When the percentage ratio of the abnormal type single batteries in the battery pack is smaller than or equal to a first threshold ratio and the percentage ratio of the normal type single batteries is smaller than a second threshold ratio, determining that the battery pack is in a sub-health state; wherein the second threshold duty cycle is greater than the first threshold proportion.
2. The battery pack health detection method as set forth in claim 1, wherein the step of the internal resistances of the individual battery cells of the battery pack for the preset time period includes:
acquiring driving data of a vehicle in a preset time period, wherein the driving data comprises total voltage of a battery pack and current and voltage of each single battery, and the preset time period comprises time periods of two states of starting and flameout of the vehicle;
and determining the internal resistance of each single battery of the battery pack according to the driving data.
3. The battery pack health detection method of claim 1, wherein the calculation formula of the Z fraction of the internal resistance of the single battery is:
Wherein Z i,j is the internal resistance Z fraction of the single battery j at the moment i, R i,j is the internal resistance of the j-th single battery at the moment i, R ave is the average value of the internal resistances of the single batteries at the moment i, and sigma R is the standard deviation of the internal resistances of the single batteries at the moment i.
4. The battery pack health detection method of claim 1, wherein the calculation formula of the accumulated deviation of the unit cells is:
Wherein D i is the accumulated deviation of the internal resistances of the I-number single batteries in the time period from t1 to t2, U t is the total voltage of the batteries at the time t, I t is the current of the batteries at the time t, and n is the number of single batteries.
5. The battery pack health detection method of claim 1, wherein the calculation formula of the cosine set variance of the internal resistances of any one single battery and two adjacent single batteries is:
Wherein IRV is cosine set variance of the included angle between the internal resistances of the single battery A and the adjacent two single batteries, Vectors formed for internal resistances of the body cell A and the unit cell B, and/>The vector is formed by the internal resistances of the single battery A and the single battery C in the battery pack.
6. A battery pack health detection device, comprising:
The acquisition module is used for acquiring the internal resistances of all the single batteries of the battery pack in a preset time period;
the first determining module is used for determining corresponding fault evaluation parameters according to the internal resistances of the single batteries, wherein the fault evaluation parameters comprise a Z fraction of the internal resistances of the single batteries, accumulated deviation of the internal resistances of the single batteries and cosine set variance of the internal resistances of the single batteries and two adjacent single batteries;
The second determining module is used for comparing the evaluation parameters corresponding to the single batteries with preset parameter conditions corresponding to the health types so as to determine the health types of the single batteries, wherein the health types comprise normal, slight abnormality and abnormality; the specific functions of the second determining module include:
When the absolute value of the Z fraction of the detected internal resistance of the current single battery is smaller than or equal to a first threshold value, and when the accumulated deviation of the internal resistances of the current single battery is smaller than the first threshold value deviation and the cosine set variance of the included angles of the internal resistances of the current single battery and the adjacent two single batteries meets a first target condition, determining that the current single battery is of a normal type, wherein the first target condition is as follows:
Wherein Cos ave is the average value of cosine sets of all included angles, and IRV is the variance of cosine sets of the internal resistances of the current single battery and two adjacent single batteries;
When the absolute value of the Z fraction of the internal resistance of the current single battery is larger than the first threshold and smaller than or equal to a second threshold, determining that the current single battery is of a slight abnormal type, or when the accumulated deviation of the internal resistance of the current single battery is larger than or equal to the first threshold deviation, determining that the current single battery is of a slight abnormal type, or when the cosine set variance of the internal resistance included angles of the current single battery and two adjacent single batteries meets a second target condition, determining that the current single battery is of a normal type, wherein the second target condition is that:
When the absolute value of the Z fraction of the internal resistance of the current single battery is larger than the second threshold value, determining that the current single battery is of an abnormal type;
The statistics module is used for respectively counting the number of the single batteries of the normal type, the slight abnormal type and the abnormal type;
The third determining module is configured to compare the number of the normal type, the slight abnormal type, and the abnormal type of the single batteries with preset parameter conditions corresponding to respective health states, so as to determine the health state of the battery pack, where the specific functions of the third determining module include:
when the percentage ratio of the abnormal type single batteries in the battery pack is larger than a first threshold ratio, determining that the battery pack is in a fault state;
When the percentage ratio of the abnormal type single batteries in the battery pack is smaller than or equal to a first threshold ratio and the percentage ratio of the normal type single batteries is larger than or equal to a second threshold ratio, determining that the battery pack is in a good state;
When the percentage ratio of the abnormal type single batteries in the battery pack is smaller than or equal to a first threshold ratio and the percentage ratio of the normal type single batteries is smaller than a second threshold ratio, determining that the battery pack is in a sub-health state; wherein the second threshold duty cycle is greater than the first threshold proportion.
7. A readable storage medium having stored thereon a program, which when executed by a processor, implements the method according to any of claims 1-5.
8. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1-5 when the program is executed by the processor.
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| CN113759266B (en) * | 2021-08-25 | 2023-07-25 | 电子科技大学长三角研究院(湖州) | Method for predicting health state of lithium ion battery based on artificial intelligence |
| CN114325400B (en) * | 2021-11-24 | 2024-03-19 | 北京百度网讯科技有限公司 | Method and device for determining remaining life of battery, electronic equipment and storage medium |
| CN115877215B (en) * | 2022-09-23 | 2024-01-30 | 四川新能源汽车创新中心有限公司 | Battery pack state detection method and related device |
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