CN116148680B - Battery parameter identification method, device, computer equipment and storage medium - Google Patents
Battery parameter identification method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application provides a battery parameter identification method, a battery parameter identification device, computer equipment and a storage medium. Wherein the method comprises the following steps: obtaining a plurality of groups of candidate parameter values of battery parameters; the battery parameters are parameters to be identified of the battery to be tested; acquiring a single particle model corresponding to a battery to be tested, and acquiring a current-voltage curve of the battery to be tested; the single particle model is used for representing the relation among the battery parameters of the battery to be tested, the current of the battery to be tested and the voltage of the battery to be tested, and the current-voltage curve is a curve formed by the actual current value of the battery to be tested and the actual voltage value of the battery to be tested; performing iterative operation of a genetic algorithm based on a plurality of groups of candidate parameter values, a single particle model and a current-voltage curve; and under the condition that the genetic algorithm reaches the iteration termination condition, obtaining the identification result of the battery parameters. By adopting the method, the identification efficiency of the battery parameters is improved.
Description
Technical Field
The application relates to the field of battery technology. In particular, the application relates to a battery parameter identification method, a battery parameter identification device, a computer device and a storage medium.
Background
Battery (e.g., lithium ion battery) safety has been a focus of battery management system attention. How to accurately evaluate the health status of the battery and respond to possible safety accidents of the battery in advance is a problem that the current development of the battery industry must be solved. In the monitoring process of the battery, the battery needs to be modeled and the battery parameters in the model need to be identified. The P2D model (pseudo two-dimensional model) simplified based on the porous electrode theory is widely used for the physicochemical process description inside the battery.
However, due to the characteristics of complex partial differential equation, multiple parameters, large scale of the energy storage power station and the like of the P2D model, the solving speed of the battery parameters is always a main bottleneck for limiting the practical application of the battery model.
In view of this, how to improve the identification efficiency of the battery parameters is a technical problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a battery parameter identification method, apparatus, computer device, and storage medium that can improve the battery parameter identification efficiency.
In a first aspect, the present application provides a method for identifying a battery parameter. The method comprises the following steps:
Obtaining a plurality of groups of candidate parameter values of battery parameters; wherein, the battery parameter is the parameter to be identified of the battery to be tested;
acquiring a single particle model corresponding to the battery to be tested, and acquiring a current-voltage curve of the battery to be tested; the single particle model is used for representing the relation among battery parameters of the battery to be tested, current of the battery to be tested and voltage of the battery to be tested, and the current-voltage curve is a curve formed by an actual current value of the battery to be tested and an actual voltage value of the battery to be tested;
performing iterative operation of a genetic algorithm based on the plurality of sets of candidate parameter values, the single particle model and the current-voltage curve;
and under the condition that the genetic algorithm reaches the iteration termination condition, obtaining the identification result of the battery parameter.
In one embodiment, the performing an iterative operation of a genetic algorithm based on the plurality of sets of candidate parameter values, the single particle model, and the current-voltage curve includes:
inputting the multiple groups of candidate parameter values and actual current values in the current-voltage curve into the single particle model for calculation to obtain calculated voltage values corresponding to each group of candidate parameter values;
According to the calculated voltage value corresponding to each group of candidate parameter values and the actual voltage value in the current-voltage curve, calculating to obtain a voltage error corresponding to each group of candidate parameter values;
and judging whether the genetic algorithm reaches an iteration termination condition or not based on the iteration times of the genetic algorithm and the minimum value of the voltage error.
In one embodiment, the method further comprises:
under the condition that the genetic algorithm does not reach the iteration termination condition, pairing the plurality of groups of candidate parameter values pairwise to obtain a plurality of pairs of candidate parameter values; wherein each pair of candidate parameter values consists of two sets of paired candidate parameter values;
for each pair of candidate parameter values, using the paired two sets of candidate parameter values as parent candidate parameter values, and exchanging part of parameter values in the two sets of parent candidate parameter values to obtain two sets of child candidate parameter values;
for each pair of candidate parameter values, respectively inputting the two sets of candidate parameter values of the child into the single particle model for calculation to obtain two voltage errors corresponding to the two sets of candidate parameter values of the child;
for each pair of candidate parameter values, screening two groups of candidate parameter values to be reserved from the two groups of candidate parameter values and the two groups of candidate parameter values according to the comparison results of the two voltage errors corresponding to the two groups of candidate parameter values and the two voltage errors corresponding to the two groups of candidate parameter values;
And taking all the candidate parameter values to be reserved as new multiple sets of candidate parameter values, and returning to the step of executing iterative operation of a genetic algorithm based on the multiple sets of candidate parameter values, the single particle model and the current-voltage curve so as to continue the iterative operation of the next round.
In one embodiment, the exchanging the partial parameter values of the two sets of parent candidate parameter values to obtain two sets of child candidate parameter values includes:
exchanging part of parameter values in the two groups of parent candidate parameter values to obtain two groups of initial child candidate parameter values;
and in the two groups of initial candidate parameter values, carrying out random value assignment on each candidate parameter value in each group of initial candidate parameter values according to the preset probability to obtain two groups of candidate parameter values.
In one embodiment, the obtaining the identification result of the battery parameter when the genetic algorithm reaches the iteration termination condition includes:
and under the condition that the genetic algorithm reaches an iteration termination condition, determining a group of candidate parameter values with the minimum voltage error as an identification result of the battery parameter.
In one embodiment, the obtaining a plurality of candidate parameter values of the battery parameter includes:
Generating M.N groups of candidate parameter values of the battery parameters through a central processing unit; wherein each candidate parameter value in the M x N sets of candidate parameter values corresponds to M N-dimensional sets;
the M N dimension groups are respectively sent to M computing modules in a field programmable gate array through the central processing unit;
the iterative operation of the genetic algorithm based on the plurality of sets of candidate parameter values, the single particle model and the current-voltage curve comprises the following steps:
inputting the received N groups of candidate parameter values and the actual current values in the current-voltage curve into the single particle model through each calculation module in the field programmable gate array to calculate to obtain calculated voltage values corresponding to the N groups of candidate parameter values, calculating to obtain voltage errors corresponding to the N groups of candidate parameter values according to the calculated voltage values corresponding to the N groups of candidate parameter values and the actual voltage values in the current-voltage curve, and returning the voltage errors corresponding to the N groups of candidate parameter values to the central processor;
and judging whether the genetic algorithm reaches an iteration termination condition or not based on the iteration times of the genetic algorithm and the minimum value of the voltage error by the central processing unit.
In one embodiment, the method further comprises:
under the condition that the genetic algorithm does not reach the iteration termination condition, the central processing unit pairs the plurality of groups of candidate parameter values pairwise to obtain a plurality of pairs of candidate parameter values; wherein each pair of candidate parameter values consists of two sets of paired candidate parameter values;
for each pair of candidate parameter values, using the paired two sets of candidate parameter values as parent candidate parameter values by a central processing unit, and exchanging part of parameter values in the two sets of parent candidate parameter values to obtain two sets of child candidate parameter values;
for each pair of candidate parameter values, respectively inputting the two sets of child candidate parameter values into the single particle model through a calculation module in a field programmable gate array for calculation to obtain two voltage errors corresponding to the two sets of child candidate parameter values;
for each pair of candidate parameter values, screening two sets of candidate parameter values to be reserved from the two sets of candidate parameter values and the two sets of candidate parameter values by a central processing unit according to the comparison result of the two voltage errors corresponding to the two sets of candidate parameter values and the two voltage errors corresponding to the two sets of candidate parameter values;
And taking all the candidate parameter values to be reserved as new multiple groups of candidate parameter values by the central processing unit, and returning to the step of executing iterative operation of a genetic algorithm based on the multiple groups of candidate parameter values, the single particle model and the current-voltage curve so as to continue the iterative operation of the next round.
In a second aspect, the present application provides a battery parameter identification apparatus. The device comprises:
the parameter acquisition module is used for acquiring a plurality of groups of candidate parameter values of the battery parameters; wherein, the battery parameter is the parameter to be identified of the battery to be tested;
the data acquisition module is used for acquiring a single particle model corresponding to the battery to be detected and acquiring a current-voltage curve of the battery to be detected; the single particle model is used for representing the relation among battery parameters of the battery to be tested, current of the battery to be tested and voltage of the battery to be tested, and the current-voltage curve is a curve formed by an actual current value of the battery to be tested and an actual voltage value of the battery to be tested;
the iterative operation module is used for carrying out iterative operation of a genetic algorithm based on the plurality of groups of candidate parameter values, the single particle model and the current-voltage curve;
And the result determining module is used for obtaining the identification result of the battery parameter under the condition that the genetic algorithm reaches the iteration termination condition.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
obtaining a plurality of groups of candidate parameter values of battery parameters; wherein, the battery parameter is the parameter to be identified of the battery to be tested;
acquiring a single particle model corresponding to the battery to be tested, and acquiring a current-voltage curve of the battery to be tested; the single particle model is used for representing the relation among battery parameters of the battery to be tested, current of the battery to be tested and voltage of the battery to be tested, and the current-voltage curve is a curve formed by an actual current value of the battery to be tested and an actual voltage value of the battery to be tested;
performing iterative operation of a genetic algorithm based on the plurality of sets of candidate parameter values, the single particle model and the current-voltage curve;
and under the condition that the genetic algorithm reaches the iteration termination condition, obtaining the identification result of the battery parameter.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
obtaining a plurality of groups of candidate parameter values of battery parameters; wherein, the battery parameter is the parameter to be identified of the battery to be tested;
acquiring a single particle model corresponding to the battery to be tested, and acquiring a current-voltage curve of the battery to be tested; the single particle model is used for representing the relation among battery parameters of the battery to be tested, current of the battery to be tested and voltage of the battery to be tested, and the current-voltage curve is a curve formed by an actual current value of the battery to be tested and an actual voltage value of the battery to be tested;
performing iterative operation of a genetic algorithm based on the plurality of sets of candidate parameter values, the single particle model and the current-voltage curve;
and under the condition that the genetic algorithm reaches the iteration termination condition, obtaining the identification result of the battery parameter.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
Obtaining a plurality of groups of candidate parameter values of battery parameters; wherein, the battery parameter is the parameter to be identified of the battery to be tested;
acquiring a single particle model corresponding to the battery to be tested, and acquiring a current-voltage curve of the battery to be tested; the single particle model is used for representing the relation among battery parameters of the battery to be tested, current of the battery to be tested and voltage of the battery to be tested, and the current-voltage curve is a curve formed by an actual current value of the battery to be tested and an actual voltage value of the battery to be tested;
performing iterative operation of a genetic algorithm based on the plurality of sets of candidate parameter values, the single particle model and the current-voltage curve;
and under the condition that the genetic algorithm reaches the iteration termination condition, obtaining the identification result of the battery parameter.
According to the battery parameter identification method, the device, the computer equipment and the storage medium, the battery parameter identification result is obtained after the iteration of the genetic algorithm is ended by acquiring a plurality of groups of candidate parameter values of the battery parameter, the single particle model corresponding to the battery to be tested and the current-voltage curve and performing iterative operation of the genetic algorithm based on the plurality of groups of candidate parameter values, the single particle model and the current-voltage curve. It can be understood that the identification of the battery parameters is realized by adopting a genetic algorithm based on a current-voltage curve in the actual charge-discharge process of the battery to be tested. Compared with the P2D model, which has the problems of complex equation, more parameters to be solved and the like, the genetic algorithm belongs to a heuristic algorithm, and the heuristic algorithm is utilized to solve the problems, so that the solving difficulty can be reduced, the solving process can be accelerated, and the identification efficiency of the battery parameters can be improved.
Drawings
FIG. 1 is a flow chart of a method for identifying battery parameters according to an embodiment;
FIG. 2 is a schematic diagram of an FPGA-based accelerated electrical parameter identification system in one embodiment;
FIG. 3 is a block diagram of a battery parameter identification device according to an embodiment;
FIG. 4 is a block diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a method for identifying battery parameters is provided. The method can be applied to the terminal, the server and a system comprising the terminal and the server, and is realized through interaction of the terminal and the server. In this embodiment, the method includes the steps of:
step S102, a plurality of candidate parameter values of the battery parameters are obtained.
Specifically, the battery parameter is a parameter to be identified of the battery to be tested. The battery to be tested may be a lithium ion battery. The number of battery parameters is typically plural, e.g 
Co (all ]>
Thus, each set of candidate parameter values obtained is combined from candidate parameter values of each battery parameter, e.g. one set of candidate parameter values may be {1, 9,..3 } common }>
The other set of candidate parameter values may be {2, 1,..8 } co }>
Candidate parameter values. The plurality of candidate parameter values represent battery parameters having different parameter value options.
Alternatively, multiple sets of candidate parameter values may be randomly generated according to constraints of various electrical parameters of the battery under test.
Step S104, obtaining a single particle model corresponding to the battery to be tested, and obtaining a current-voltage curve of the battery to be tested.
Specifically, a single particle model
For characterising the relationship between the battery parameters of the battery to be measured, the current of the battery to be measured and the voltage of the battery to be measured. The form of the single particle model is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the voltage of the battery to be tested, i.e. terminal voltage,/-)>
Representing the current of the battery to be measured, i.e. the external circuit current,
time of presentation->
Representing the battery parameters of the battery to be tested.
Optionally, a single particle model corresponding to the battery to be tested can be established according to a solid phase diffusion equation, a solid phase potential equation, a charge conservation equation and a Bulter-Volmer equation.
The current-voltage curve is a curve composed of an actual current value of the battery to be measured and an actual voltage value of the battery to be measured. Optionally, according to the actual charging and discharging process of the battery to be tested, the data of an actual current value, an actual voltage value, time and the like which change along with time can be obtained, so that a current-voltage curve is obtained.
And S106, performing iterative operation of a genetic algorithm based on the plurality of groups of candidate parameter values, the single particle model and the current-voltage curve.
Specifically, a genetic algorithm is adopted, and an identification result of battery parameters is solved based on a plurality of groups of candidate parameter values, a single particle model and a current-voltage curve. Firstly, a plurality of groups of candidate parameter values are used as an initial population of a genetic algorithm and an actual current value are input into a single particle model, and a calculated voltage value corresponding to each group of candidate parameter values is obtained. And then, according to the calculated voltage value corresponding to each group of candidate parameter values and the actual voltage value in the current-voltage curve, calculating to obtain the voltage error corresponding to each group of candidate parameter values. The voltage error is the difference between the calculated voltage value and the actual voltage value. And finally, judging whether the genetic algorithm reaches an iteration termination condition or not based on the current iteration times of the genetic algorithm and the minimum value in all voltage errors.
Step S108, under the condition that the genetic algorithm reaches the iteration termination condition, the identification result of the battery parameter is obtained.
Specifically, if the current iteration number of the genetic algorithm reaches the preset maximum iteration number (the minimum value of the voltage errors and the preset error threshold value are not considered at this time) or the minimum value of all the voltage errors is smaller than the preset error threshold value (the current iteration number of the genetic algorithm and the preset maximum iteration number are not considered at this time), three situations are classified: 1) The current iteration number of the genetic algorithm reaches the preset maximum iteration number, and the minimum value in the voltage error is smaller than the preset error threshold value; 2) The current iteration number of the genetic algorithm reaches the preset maximum iteration number, and the minimum value in the voltage error is larger than or equal to the preset error threshold value; 3) And if the current iteration number of the genetic algorithm does not reach the preset maximum iteration number and the minimum value in the voltage error is smaller than the preset error threshold value, terminating the iterative operation. And taking the candidate parameter values with the minimum voltage error as the identification result of the battery parameters.
In the method for identifying the battery parameters, the battery parameter identification result is obtained after the iteration of the genetic algorithm is terminated by acquiring a plurality of groups of candidate parameter values of the battery parameters, a single particle model corresponding to the battery to be tested and a current-voltage curve and performing iterative operation of the genetic algorithm based on the plurality of groups of candidate parameter values, the single particle model and the current-voltage curve. It can be understood that the identification of the battery parameters is realized by adopting a genetic algorithm based on a current-voltage curve in the actual charge-discharge process of the battery to be tested. Compared with the P2D model, which has the problems of complex equation, more parameters to be solved and the like, the genetic algorithm belongs to a heuristic algorithm, and the heuristic algorithm is utilized to solve the problems, so that the solving difficulty can be reduced, the solving process can be accelerated, and the identification efficiency of the battery parameters can be improved.
In one embodiment, the method further comprises the steps of:
step S202, under the condition that the genetic algorithm does not reach the iteration termination condition, pairing a plurality of groups of candidate parameter values pairwise to obtain a plurality of pairs of candidate parameter values; wherein each pair of candidate parameter values consists of two sets of paired candidate parameter values;
step S204, regarding each pair of candidate parameter values, using the paired two sets of candidate parameter values as parent candidate parameter values, and exchanging part of parameter values in the two sets of parent candidate parameter values to obtain two sets of child candidate parameter values;
step S206, inputting two sets of candidate parameter values of the child into the single particle model for calculation respectively aiming at each pair of candidate parameter values, so as to obtain two voltage errors corresponding to the two sets of candidate parameter values of the child;
step S208, for each pair of candidate parameter values, screening two sets of candidate parameter values to be reserved from the two sets of candidate parameter values and the two sets of candidate parameter values according to the comparison result of the two voltage errors corresponding to the two sets of candidate parameter values and the two voltage errors corresponding to the two sets of candidate parameter values;
step S210, taking all the candidate parameter values to be reserved as new multiple sets of candidate parameter values, and returning to the step of executing the iterative operation of the genetic algorithm based on the multiple sets of candidate parameter values, the single particle model and the current-voltage curve so as to execute the iterative operation of the next round.
Specifically, if the current iteration number of the genetic algorithm does not reach the preset maximum iteration number and the minimum value of all the voltage errors is greater than or equal to the preset error threshold, which indicates that the genetic algorithm does not reach the iteration termination condition, pairing the plurality of candidate parameter values in pairs to obtain a plurality of pairs of candidate parameter values. Wherein each pair of candidate parameter values consists of two sets of paired candidate parameter values.
Then, in each pair of candidate parameter values, the paired two sets of candidate parameter values are taken as parent candidate parameter values, and in the two sets of parent candidate parameter values, partial parameter values are randomly selected for exchange, so that two new sets of candidate parameter values, namely two sets of child candidate parameter values, are generated;
and then, respectively inputting two sets of child candidate parameter values into a single particle model for calculation aiming at each pair of candidate parameter values to obtain two voltage errors corresponding to the two sets of child candidate parameter values, comparing the two voltage errors corresponding to the two sets of child candidate parameter values with the two voltage errors corresponding to the two sets of parent candidate parameter values, screening out the two sets of candidate parameter values with larger voltage errors or without meeting the constraint conditions from the two sets of child candidate parameter values and the two sets of parent candidate parameter values by combining the constraint conditions of each electrical parameter of the battery to be tested, and reserving the rest two sets of candidate parameter values.
And finally, taking all the candidate parameter values to be reserved as new multiple groups of candidate parameter values, and returning to the step of executing iterative operation of the genetic algorithm based on the multiple groups of candidate parameter values, the single particle model and the current-voltage curve so as to continue the iterative operation of the next round.
In this embodiment, the iterative optimization process based on the genetic algorithm is used to find the optimal parameter combination under the given constraint condition, so as to minimize the voltage error generated by the single particle model, and improve the accuracy of parameter identification. Meanwhile, in the pairing process, each pair of candidate parameter values consists of two paired candidate parameter values, so that the method is beneficial to introducing diversity, and an algorithm can search a plurality of directions at the same time, so that the situation of sinking into a local optimal solution is avoided.
In one embodiment, the "exchanging part of the parameter values in the two sets of parent candidate parameter values to obtain the two sets of child candidate parameter values" in step S204 may be specifically implemented by the following steps:
step S2042, exchanging part of parameter values in the two groups of parent candidate parameter values to obtain two groups of initial child candidate parameter values;
step S2044, in the two sets of initial candidate parameter values, performing random value assignment on each candidate parameter value in each set of initial candidate parameter values by using a preset probability to obtain two sets of candidate parameter values.
Specifically, first, based on the chromosome cross transformation mode, part of parameter values in the two sets of parent candidate parameter values are exchanged, so that two sets of initial child candidate parameter values are obtained. And then, based on the genetic variation mode, carrying out random value assignment on each candidate parameter value in each group of initial candidate parameter values of the offspring in the two groups of initial candidate parameter values of the offspring, and obtaining two groups of candidate parameter values of the offspring. Alternatively, the preset probability may be 3% or 5%, or the like. The random value may be a number between (2.5-3.0). For example, for a candidate parameter value in each set of initial offspring candidate parameter values, a random value of 2.6 is selected from (2.5-3.0), then the candidate parameter value accepts the random value with a probability of 5%, and the other 95% of the probability remains unchanged.
In this embodiment, in the genetic algorithm, the combination of different electrical parameters is automatically adjusted by means of chromosome cross transformation and genetic variation, so as to better adapt to a given optimization problem, and improve the probability of finding the optimal solution, thereby being beneficial to improving the accuracy of electrical parameter identification.
In one embodiment, step S102 includes the steps of:
Step S1022, generating M.times.N groups of candidate parameter values of the battery parameters by the CPU; wherein each candidate parameter value in the M x N sets of candidate parameter values corresponds to M N-dimensional arrays, M x N being an even number;
in step S1024, the central processor sends the M N-dimensional numbers to the M computing modules in the field programmable gate array, respectively.
Based on this, step S106 includes the steps of:
step S1062, inputting the received N sets of candidate parameter values and the actual current values in the current-voltage curves into a single particle model through each calculation module in the field programmable gate array to calculate to obtain calculated voltage values corresponding to the N sets of candidate parameter values, calculating to obtain voltage errors corresponding to the N sets of candidate parameter values according to the calculated voltage values corresponding to the N sets of candidate parameter values and the actual voltage values in the current-voltage curves, and returning the voltage errors corresponding to the N sets of candidate parameter values to the central processor;
step S1064, judging, by the CPU, whether the genetic algorithm reaches the iteration termination condition based on the iteration number of the genetic algorithm and the minimum value of the voltage error.
Specifically, referring to fig. 2, a Central Processing Unit (CPU) generates initial candidate parameter values, and there are m×n sets, i.e., each candidate parameter value corresponds to M N-dimensional sets. A Central Processing Unit (CPU) transmits the M arrays to each computing module in a Field Programmable Gate Array (FPGA), respectively. Each calculation module calculates a corresponding voltage error for the N sets of candidate parameter values and returns the voltage error to the central processing unit CPU. Namely, the CPU transmits each electrical parameter X0, X1...Xn-1 to be solved to each calculation module, and the FPGA returns a voltage error corresponding to each group of candidate parameter values.
On the basis of the above embodiment, the method further comprises the steps of:
step S302, under the condition that the genetic algorithm does not reach the iteration termination condition, pairing a plurality of groups of candidate parameter values in pairs by the central processing unit to obtain a plurality of pairs of candidate parameter values; wherein each pair of candidate parameter values consists of two sets of paired candidate parameter values;
step S304, regarding each pair of candidate parameter values, using the paired two sets of candidate parameter values as parent candidate parameter values through a central processing unit, and exchanging part of parameter values in the two sets of parent candidate parameter values to obtain two sets of child candidate parameter values;
step S306, for each pair of candidate parameter values, respectively inputting the two sets of child candidate parameter values into a single particle model through a calculation module in the field programmable gate array for calculation to obtain two voltage errors corresponding to the two sets of child candidate parameter values;
step S308, for each pair of candidate parameter values, screening out two sets of candidate parameter values to be reserved from the two sets of candidate parameter values and the two sets of candidate parameter values by the central processing unit according to the comparison result of the two voltage errors corresponding to the two sets of candidate parameter values and the two voltage errors corresponding to the two sets of candidate parameter values;
Step S310, taking all the candidate parameter values to be reserved as new multiple groups of candidate parameter values by the central processing unit, and returning to the step of executing iterative operation of the genetic algorithm based on the multiple groups of candidate parameter values, the single particle model and the current-voltage curve so as to continue the iterative operation of the next round.
In the embodiment, the FPGA is adopted to accelerate the battery parameter identification process, and the solving process of the single particle model is accelerated according to the advantages of pipelining and parallel computing of the FPGA, so that guidance is provided for the BMS to monitor and manage the battery state in real time.
In one embodiment, as shown in fig. 2, the Central Processing Unit (CPU) also writes the current-voltage curve of the battery under test to a BRAM (Block RAM) module of the FPGA.
In one embodiment, the two sets of initial child candidate parameter values are obtained by exchanging, by a Central Processing Unit (CPU), part of the parameter values in the two sets of parent candidate parameter values;
and carrying out random value assignment on each candidate parameter value in each initial child candidate parameter value in the two sets of initial child candidate parameter values by a Central Processing Unit (CPU) according to preset probability to obtain two sets of child candidate parameter values.
In one embodiment, a set of candidate parameter values with minimal voltage error is determined by a Central Processing Unit (CPU) as a result of identification of battery parameters in the event that a genetic algorithm reaches an iteration termination condition.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a battery parameter identification device for realizing the above related battery parameter identification method. The implementation of the solution provided by the device is similar to that described in the above method, so the specific limitation in the embodiments of the identification device for one or more battery parameters provided below may be referred to the limitation of the identification method for the battery parameters hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 3, there is provided a battery parameter identification device 20, comprising:
a parameter obtaining module 202, configured to obtain a plurality of sets of candidate parameter values of the battery parameter; the battery parameters are parameters to be identified of the battery to be tested.
The data acquisition module 204 is configured to acquire a single particle model corresponding to the battery to be measured, and acquire a current-voltage curve of the battery to be measured; the single particle model is used for representing the relation among the battery parameter of the battery to be tested, the current of the battery to be tested and the voltage of the battery to be tested, and the current-voltage curve is a curve formed by the actual current value of the battery to be tested and the actual voltage value of the battery to be tested.
The iterative operation module 206 is configured to perform iterative operation of the genetic algorithm based on the multiple sets of candidate parameter values, the single particle model, and the current-voltage curve.
The result determining module 208 is configured to obtain a result of identifying the battery parameter when the genetic algorithm reaches the iteration termination condition.
In the battery parameter identification device, the battery parameter identification result is obtained after the iteration of the genetic algorithm is terminated by acquiring a plurality of groups of candidate parameter values of the battery parameter, a single particle model corresponding to the battery to be tested and a current-voltage curve and performing iterative operation of the genetic algorithm based on the plurality of groups of candidate parameter values, the single particle model and the current-voltage curve. It can be understood that the identification of the battery parameters is realized by adopting a genetic algorithm based on a current-voltage curve in the actual charge-discharge process of the battery to be tested. Compared with the P2D model, which has the problems of complex equation, more parameters to be solved and the like, the genetic algorithm belongs to a heuristic algorithm, and the heuristic algorithm is utilized to solve the problems, so that the solving difficulty can be reduced, the solving process can be accelerated, and the identification efficiency of the battery parameters can be improved.
In one embodiment, the iterative operation module 206 is specifically configured to input a plurality of sets of candidate parameter values and actual current values in the current-voltage curve into the single particle model for calculation, so as to obtain calculated voltage values corresponding to each set of candidate parameter values; according to the calculated voltage value corresponding to each group of candidate parameter values and the actual voltage value in the current-voltage curve, calculating to obtain a voltage error corresponding to each group of candidate parameter values; and judging whether the genetic algorithm reaches an iteration termination condition or not based on the iteration times of the genetic algorithm and the minimum value of the voltage error.
In one embodiment, the apparatus further comprises:
the parameter pairing module is used for pairing a plurality of groups of candidate parameter values pairwise under the condition that the genetic algorithm does not reach the iteration termination condition, so as to obtain a plurality of pairs of candidate parameter values; wherein each pair of candidate parameter values consists of two sets of paired candidate parameter values.
And the parameter exchange module is used for taking the paired two groups of candidate parameter values as parent candidate parameter values aiming at each pair of candidate parameter values, and exchanging part of parameter values in the two groups of parent candidate parameter values to obtain two groups of child candidate parameter values.
The model calculation module is used for inputting the two sets of candidate parameter values of the child into the single particle model for calculation according to each pair of candidate parameter values, and obtaining two voltage errors corresponding to the two sets of candidate parameter values of the child.
And the parameter screening module is used for screening two groups of candidate parameter values to be reserved from the two groups of candidate parameter values and the two groups of candidate parameter values according to the comparison result of the two voltage errors corresponding to the two groups of candidate parameter values and the two voltage errors corresponding to the two groups of candidate parameter values.
And the parameter updating module is used for taking all candidate parameter values to be reserved as new multiple groups of candidate parameter values, and returning to the step of executing iterative operation of the genetic algorithm based on the multiple groups of candidate parameter values, the single particle model and the current-voltage curve so as to continue the iterative operation of the next round.
In one embodiment, the parameter exchange module is specifically configured to exchange some parameter values in the two sets of parent candidate parameter values to obtain two sets of initial child candidate parameter values; and in the two groups of initial candidate parameter values, carrying out random value assignment on each candidate parameter value in each group of initial candidate parameter values according to the preset probability to obtain two groups of candidate parameter values.
In one embodiment, the result determining module 208 is specifically configured to determine a set of candidate parameter values with minimum voltage error as the identification result of the battery parameter when the genetic algorithm reaches the iteration termination condition.
In one embodiment, the parameter obtaining module 202 is specifically configured to generate, by the central processing unit, m×n sets of candidate parameter values of the battery parameters; wherein each candidate parameter value in the M x N sets of candidate parameter values corresponds to M N-dimensional sets; and respectively transmitting the M N dimension numbers to M computing modules in a field programmable gate array through the central processing unit.
The iterative operation module 206 is specifically configured to input, through each calculation module in the field programmable gate array, the received N sets of candidate parameter values and actual current values in the current-voltage curve into the single particle model to perform calculation, obtain calculated voltage values corresponding to the N sets of candidate parameter values, calculate, according to the calculated voltage values corresponding to the N sets of candidate parameter values and the actual voltage values in the current-voltage curve, obtain voltage errors corresponding to the N sets of candidate parameter values, and return the voltage errors corresponding to the N sets of candidate parameter values to the central processor; and judging whether the genetic algorithm reaches an iteration termination condition or not based on the iteration times of the genetic algorithm and the minimum value of the voltage error by the central processing unit.
In one embodiment, the parameter pairing module is specifically configured to pair the plurality of sets of candidate parameter values in pairs by using the central processing unit under the condition that the genetic algorithm does not reach an iteration termination condition, so as to obtain a plurality of pairs of candidate parameter values; wherein each pair of candidate parameter values consists of two sets of paired candidate parameter values.
The parameter exchange module is specifically configured to exchange, by using the central processing unit, two paired sets of candidate parameter values as parent candidate parameter values for each pair of candidate parameter values, and exchange part of parameter values in the two sets of parent candidate parameter values, so as to obtain two sets of child candidate parameter values.
The model calculation module is specifically configured to, for each pair of candidate parameter values, input the two sets of candidate parameter values of the child into the single particle model through a calculation module in the field programmable gate array for calculation, and obtain two voltage errors corresponding to the two sets of candidate parameter values of the child.
The parameter screening module is specifically configured to screen, for each pair of candidate parameter values, two sets of candidate parameter values to be retained from the two sets of candidate parameter values and the two sets of candidate parameter values by using the central processing unit according to a comparison result of two voltage errors corresponding to the two sets of candidate parameter values and two voltage errors corresponding to the two sets of candidate parameter values.
The parameter updating module is specifically configured to take all candidate parameter values to be retained as new multiple sets of candidate parameter values by using the central processing unit, and return to execute an iterative operation of a genetic algorithm based on the multiple sets of candidate parameter values, the single particle model and the current-voltage curve, so as to continue the iterative operation of the next round.
It should be noted that, when the battery parameter identification apparatus 20 provided in the above embodiment implements the corresponding function, only the division of the above functional modules is used for illustration, and in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the identification device 20 for battery parameters provided in the above embodiment and the method embodiment for identifying battery parameters belong to the same concept, and detailed implementation processes thereof are shown in the method embodiment and are not repeated here.
According to one aspect of the present application, the present embodiment also provides a computer program product comprising a computer program comprising program code for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication section. When the computer program is executed by the processor, the electrochemical parameter identification method provided by the embodiment of the application is executed.
In addition, the embodiment of the invention also provides a computer device, which comprises a processor and a memory, wherein the memory stores a computer program, the processor can execute the computer program stored in the memory, and when the computer program is executed by the processor, the identification method of the battery parameter provided by any embodiment can be realized.
For example, FIG. 4 illustrates a computer device provided by an embodiment of the invention, the device including a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In an embodiment of the present invention, the apparatus further includes: a computer program stored in the memory 1150 and executable on the processor 1120, which when executed by the processor 1120, performs the processes of the electrochemical parameter identification method embodiments described above.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In an embodiment of the invention, represented by bus 1110, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits, including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
The processor 1120 may be a microprocessor or any conventional processor. The steps of the method disclosed in connection with the embodiments of the present invention may be performed directly by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules may be located in a random access Memory (Random Access Memory, RAM), flash Memory (Flash Memory), read-Only Memory (ROM), programmable ROM (PROM), erasable Programmable ROM (EPROM), registers, and so forth, as are known in the art. The readable storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The transceiver 1130 may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 is configured to transmit the data processed by the processor 1120 to the other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, for example: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It should be appreciated that in embodiments of the present invention, the memory 1150 may further comprise memory located remotely from the processor 1120, such remotely located memory being connectable to a server through a network. One or more portions of the above-described networks may be an ad hoc network (ad hoc network), an intranet, an extranet (extranet), a Virtual Private Network (VPN), a Local Area Network (LAN), a Wireless Local Area Network (WLAN), a Wide Area Network (WAN), a Wireless Wide Area Network (WWAN), a Metropolitan Area Network (MAN), the Internet (Internet), a Public Switched Telephone Network (PSTN), a plain old telephone service network (POTS), a cellular telephone network, a wireless fidelity (Wi-Fi) network, and a combination of two or more of the above-described networks. For example, the cellular telephone network and wireless network may be a global system for mobile communications (GSM) system, a Code Division Multiple Access (CDMA) system, a Worldwide Interoperability for Microwave Access (WiMAX) system, a General Packet Radio Service (GPRS) system, a Wideband Code Division Multiple Access (WCDMA) system, a Long Term Evolution (LTE) system, an LTE Frequency Division Duplex (FDD) system, an LTE Time Division Duplex (TDD) system, a long term evolution-advanced (LTE-a) system, a Universal Mobile Telecommunications (UMTS) system, an enhanced mobile broadband (Enhance Mobile Broadband, embbb) system, a mass machine type communication (massive Machine Type of Communication, mctc) system, an ultra reliable low latency communication (Ultra Reliable Low Latency Communications, uirllc) system, and the like.
It should be appreciated that the memory 1150 in embodiments of the present invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-Only Memory (ROM), programmable ROM (PROM), erasable Programmable EPROM (EPROM), electrically Erasable EPROM (EEPROM), or Flash Memory (Flash Memory).
The volatile memory includes: random access memory (Random Access Memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (ddr SDRAM), enhanced SDRAM (Enhanced SDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRAM). Memory 1150 described in embodiments of the present invention includes, but is not limited to, the above and any other suitable types of memory.
In an embodiment of the invention, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an extended set thereof.
Specifically, the operating system 1151 includes various system programs, such as: a framework layer, a core library layer, a driving layer and the like, which are used for realizing various basic services and processing tasks based on hardware. The applications 1152 include various applications such as: a Media Player (Media Player), a Browser (Browser) for implementing various application services. A program for implementing the method of the embodiment of the present invention may be included in the application 1152. The application 1152 includes: applets, objects, components, logic, data structures, and other computer system executable instructions that perform particular tasks or implement particular abstract data types.
In addition, the embodiment of the invention further provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the processes of the above-mentioned electrochemical parameter identification method embodiment are implemented, and the same technical effects can be achieved, so that repetition is avoided, and no further description is provided herein.
The computer-readable storage medium includes: persistent and non-persistent, removable and non-removable media are tangible devices that may retain and store instructions for use by an instruction execution device. The computer-readable storage medium includes: electronic storage, magnetic storage, optical storage, electromagnetic storage, semiconductor storage, and any suitable combination of the foregoing. The computer-readable storage medium includes: phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), non-volatile random access memory (NVRAM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassette storage, magnetic tape disk storage or other magnetic storage devices, memory sticks, mechanical coding (e.g., punch cards or bump structures in grooves with instructions recorded thereon), or any other non-transmission medium that may be used to store information that may be accessed by a computing device. In accordance with the definition in the present embodiments, the computer-readable storage medium does not include a transitory signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a pulse of light passing through a fiber optic cable), or an electrical signal transmitted through a wire.
In the several embodiments provided herein, it should be understood that the disclosed apparatus, devices, and methods may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one position, or may be distributed over a plurality of network units. Some or all of the units can be selected according to actual needs to solve the problem to be solved by the scheme of the embodiment of the invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the embodiments of the present invention is essentially or partly contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (including: a personal computer, a server, a data center or other network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the storage medium includes various media as exemplified above that can store program codes.
In the description of the embodiments of the present invention, those skilled in the art should appreciate that the embodiments of the present invention may be implemented as a method, an apparatus, a device, and a storage medium. Thus, embodiments of the present invention may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, embodiments of the invention may also be implemented in the form of a computer program product in one or more computer-readable storage media having computer program code embodied therein.
Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer diskette, hard disk, random Access Memory (RAM), read-only Memory (ROM), erasable programmable read-only Memory (EPROM), flash Memory (Flash Memory), optical fiber, compact disc read-only Memory (CD-ROM), optical storage device, magnetic storage device, or any combination thereof. In embodiments of the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.
The computer program code embodied in the computer readable storage medium may be transmitted using any appropriate medium, including: wireless, wire, fiber optic cable, radio Frequency (RF), or any suitable combination thereof.
Computer program code for carrying out operations of embodiments of the present invention may be written in assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or in one or more programming languages, including an object oriented programming language such as: java, smalltalk, C ++, also include conventional procedural programming languages, such as: c language or similar programming language. The computer program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computers may be connected via any sort of network, including: a Local Area Network (LAN) or a Wide Area Network (WAN), which may be connected to the user's computer or to an external computer.
The embodiments of the present invention describe the provided methods, apparatuses, devices through flowcharts and/or block diagrams.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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/acts specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The foregoing is merely a specific implementation of the embodiments of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the embodiments of the present invention, and the changes or substitutions are covered by the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A method for identifying battery parameters, comprising:
obtaining a plurality of groups of candidate parameter values of battery parameters; the battery parameters are parameters to be identified of the battery to be tested, and the plurality of groups of candidate parameter values are required to meet constraint conditions of various electrical parameters of the battery to be tested;
acquiring a single particle model corresponding to the battery to be tested, and acquiring a current-voltage curve of the battery to be tested; the single particle model is used for representing the relation among battery parameters of the battery to be tested, current of the battery to be tested and voltage of the battery to be tested, and the current-voltage curve is a curve formed by an actual current value of the battery to be tested and an actual voltage value of the battery to be tested;
Performing iterative operation of a genetic algorithm based on the plurality of sets of candidate parameter values, the single particle model and the current-voltage curve;
under the condition that the genetic algorithm reaches an iteration termination condition, obtaining an identification result of the battery parameter;
wherein the obtaining a plurality of sets of candidate parameter values of the battery parameter includes:
generating M.N groups of candidate parameter values of the battery parameters through a central processing unit; wherein each candidate parameter value in the M x N sets of candidate parameter values corresponds to M N-dimensional sets; the M N dimension groups are respectively sent to M computing modules in a field programmable gate array through the central processing unit;
the iterative operation of the genetic algorithm based on the plurality of sets of candidate parameter values, the single particle model and the current-voltage curve comprises the following steps:
inputting the received N groups of candidate parameter values and the actual current values in the current-voltage curve into the single particle model through each calculation module in the field programmable gate array to calculate to obtain calculated voltage values corresponding to the N groups of candidate parameter values, calculating to obtain voltage errors corresponding to the N groups of candidate parameter values according to the calculated voltage values corresponding to the N groups of candidate parameter values and the actual voltage values in the current-voltage curve, and returning the voltage errors corresponding to the N groups of candidate parameter values to the central processor; and judging whether the genetic algorithm reaches an iteration termination condition or not based on the iteration times of the genetic algorithm and the minimum value of the voltage error by the central processing unit.
2. The method as recited in claim 1, further comprising:
under the condition that the genetic algorithm does not reach the iteration termination condition, pairing the plurality of groups of candidate parameter values pairwise to obtain a plurality of pairs of candidate parameter values; wherein each pair of candidate parameter values consists of two sets of paired candidate parameter values;
for each pair of candidate parameter values, using the paired two sets of candidate parameter values as parent candidate parameter values, and exchanging part of parameter values in the two sets of parent candidate parameter values to obtain two sets of child candidate parameter values;
for each pair of candidate parameter values, respectively inputting the two sets of candidate parameter values of the child into the single particle model for calculation to obtain two voltage errors corresponding to the two sets of candidate parameter values of the child;
for each pair of candidate parameter values, screening two groups of candidate parameter values to be reserved from the two groups of candidate parameter values and the two groups of candidate parameter values according to the comparison results of the two voltage errors corresponding to the two groups of candidate parameter values and the two voltage errors corresponding to the two groups of candidate parameter values;
and taking all the candidate parameter values to be reserved as new multiple sets of candidate parameter values, and returning to the step of executing iterative operation of a genetic algorithm based on the multiple sets of candidate parameter values, the single particle model and the current-voltage curve so as to continue the iterative operation of the next round.
3. The method of claim 2, wherein said swapping the partial parameter values of the two sets of parent candidate parameter values to obtain two sets of child candidate parameter values comprises:
exchanging part of parameter values in the two groups of parent candidate parameter values to obtain two groups of initial child candidate parameter values;
and in the two groups of initial candidate parameter values, carrying out random value assignment on each candidate parameter value in each group of initial candidate parameter values according to the preset probability to obtain two groups of candidate parameter values.
4. The method according to claim 1, wherein obtaining the identification result of the battery parameter in the case that the genetic algorithm reaches an iteration termination condition comprises:
and under the condition that the genetic algorithm reaches an iteration termination condition, determining a group of candidate parameter values with the minimum voltage error as an identification result of the battery parameter.
5. A battery parameter identification device, comprising:
the parameter acquisition module is used for acquiring a plurality of groups of candidate parameter values of the battery parameters; the battery parameters are parameters to be identified of the battery to be tested, and the plurality of groups of candidate parameter values are required to meet constraint conditions of various electrical parameters of the battery to be tested;
The data acquisition module is used for acquiring a single particle model corresponding to the battery to be detected and acquiring a current-voltage curve of the battery to be detected; the single particle model is used for representing the relation among battery parameters of the battery to be tested, current of the battery to be tested and voltage of the battery to be tested, and the current-voltage curve is a curve formed by an actual current value of the battery to be tested and an actual voltage value of the battery to be tested;
the iterative operation module is used for carrying out iterative operation of a genetic algorithm based on the plurality of groups of candidate parameter values, the single particle model and the current-voltage curve;
the result determining module is used for obtaining the identification result of the battery parameter under the condition that the genetic algorithm reaches the iteration termination condition;
the parameter acquisition module is specifically used for generating M-by-N groups of candidate parameter values of the battery parameters through the central processing unit; wherein each candidate parameter value in the M x N sets of candidate parameter values corresponds to M N-dimensional sets; the M N dimension groups are respectively sent to M computing modules in a field programmable gate array through the central processing unit;
the iterative operation module is specifically configured to input, through each calculation module in the field programmable gate array, the received N sets of candidate parameter values and actual current values in the current-voltage curve into the single particle model to perform calculation, obtain calculated voltage values corresponding to the N sets of candidate parameter values, calculate, according to the calculated voltage values corresponding to the N sets of candidate parameter values and the actual voltage values in the current-voltage curve, obtain voltage errors corresponding to the N sets of candidate parameter values, and return the voltage errors corresponding to the N sets of candidate parameter values to the central processor; and judging whether the genetic algorithm reaches an iteration termination condition or not based on the iteration times of the genetic algorithm and the minimum value of the voltage error by the central processing unit.
6. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 4 when the computer program is executed.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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