CN116660755A - Method and system for identifying faults of lithium ion battery - Google Patents
Method and system for identifying faults of lithium ion battery Download PDFInfo
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- CN116660755A CN116660755A CN202310701359.0A CN202310701359A CN116660755A CN 116660755 A CN116660755 A CN 116660755A CN 202310701359 A CN202310701359 A CN 202310701359A CN 116660755 A CN116660755 A CN 116660755A
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract 72
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract 72
- 238000000034 method Methods 0.000 title claims abstract 19
- 238000012360 testing method Methods 0.000 claims abstract 22
- 230000002159 abnormal effect Effects 0.000 claims abstract 9
- 238000002474 experimental method Methods 0.000 claims abstract 5
- 230000005856 abnormality Effects 0.000 claims abstract 3
- 238000005457 optimization Methods 0.000 claims 13
- 238000004088 simulation Methods 0.000 claims 4
- 238000000053 physical method Methods 0.000 claims 3
- 238000004364 calculation method Methods 0.000 claims 2
- 230000009977 dual effect Effects 0.000 claims 2
- 238000012423 maintenance Methods 0.000 claims 2
- 239000002245 particle Substances 0.000 claims 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims 1
- 210000001787 dendrite Anatomy 0.000 claims 1
- 229910052744 lithium Inorganic materials 0.000 claims 1
- 238000005259 measurement Methods 0.000 claims 1
- 238000002922 simulated annealing Methods 0.000 claims 1
- 239000007784 solid electrolyte Substances 0.000 claims 1
- 238000007619 statistical method Methods 0.000 claims 1
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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]
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Y02E60/10—Energy storage using batteries
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Abstract
The invention discloses a fault identification method and system of a lithium ion battery, wherein the method comprises the steps of carrying out a preset test experiment on the lithium ion battery to obtain test data, carrying out model parameter fitting and charge state estimation on an equivalent circuit structure of the lithium ion battery according to the test data, and establishing an equivalent circuit digital model; generating double-source data of the lithium ion battery according to the equivalent circuit digital model and the lithium ion battery physical system; the deviation similarity of double-source data of the operation preset time of the lithium ion battery is statistically analyzed, and the current operation state of the lithium ion battery is judged; if the lithium ion battery is abnormal, minimizing the parameter variation of the equivalent circuit digital model according to the optimized variable under the preset double-source deviation condition, and judging the fault type of the lithium ion battery according to the parameter variation to obtain a fault identification result. The embodiment realizes the effective identification of the fault type of the lithium ion battery and improves the accuracy and reliability of the abnormality judgment and fault type identification of the lithium ion battery.
Description
Technical Field
The invention relates to the field of fault identification of lithium ion batteries, in particular to a fault identification method and system of a lithium ion battery.
Background
The lithium ion battery is used as a flexible and convenient energy storage resource and is applied to various fields such as power grids, electric automobiles, aerospace and the like. However, the safety accidents of the lithium ion battery frequently occur, and the application reliability of the lithium ion battery is a key link for restricting the development of the lithium ion battery. Developing lithium ion battery operation state identification and fault location technology research has cost the hot spot problem in the current lithium ion battery field.
The existing methods for detecting abnormal states and locating faults around lithium ion batteries can be roughly divided into 2 kinds of mechanism methods and data methods. The mechanism method is characterized in that the relationship between an intrinsic mechanism and an extrinsic characteristic is established by analyzing the extrinsic characteristic of the lithium ion battery under different running states and faults of the lithium ion battery in the electrochemical reaction process of the lithium ion battery, and the intrinsic abnormality of the lithium ion battery is mapped by analyzing the change of the extrinsic characteristic, so that the abnormality calibration and the fault identification are realized; the data method is to excavate the fault cause of the lithium ion battery data characteristic analysis by a large amount of historical data of the lithium ion battery and apply a big data and artificial intelligence technology to form judgment and fault classification of abnormal lithium ion operation states, and the method can greatly simplify the abnormal calibration mode of the lithium ion battery without considering the complex mechanism process in the lithium ion battery, but the data quality determines the upper limit of the capability of the lithium ion battery, is greatly influenced by the data, and tends to obtain error results by incomplete and incomplete data, thereby causing adverse effects.
Disclosure of Invention
The invention provides a fault identification method and a fault identification system for a lithium ion battery, which can effectively identify the fault type of the lithium ion battery and improve the accuracy and reliability of abnormality judgment and fault type identification of the lithium ion battery.
In order to solve the above technical problems, an embodiment of the present invention provides a fault identification method for a lithium ion battery, including:
carrying out a preset test experiment on the lithium ion battery to obtain test data, carrying out model parameter fitting and charge state estimation on an equivalent circuit structure of the lithium ion battery according to the test data, and establishing an equivalent circuit digital model;
generating double-source data of the lithium ion battery according to the equivalent circuit digital model and the lithium ion battery physical system; the double-source data comprises simulation data and physical measurement data;
the deviation similarity of the double-source data of the operation preset time of the lithium ion battery is statistically analyzed, and the current operation state of the lithium ion battery is judged according to the deviation similarity;
and if the current running state of the lithium ion battery is an abnormal state, minimizing the parameter variation of the equivalent circuit digital model according to the optimized variable under the preset double-source deviation condition, and judging the fault type of the lithium ion battery according to the parameter variation to obtain a fault identification result.
According to the embodiment of the invention, a lithium ion battery is subjected to a preset test experiment to obtain test data, and an equivalent circuit structure of the lithium ion battery is subjected to model parameter fitting and charge state estimation according to the test data to establish an equivalent circuit digital model; generating double-source data of the lithium ion battery according to the equivalent circuit digital model and the lithium ion battery physical system; the double-source data comprises simulation data and physical measurement data; the deviation similarity of the double-source data of the operation preset time of the lithium ion battery is statistically analyzed, and the current operation state of the lithium ion battery is judged according to the deviation similarity; and if the current running state of the lithium ion battery is an abnormal state, minimizing the parameter variation of the equivalent circuit digital model according to the optimized variable under the preset double-source deviation condition, and judging the fault type of the lithium ion battery according to the parameter variation to obtain a fault identification result. The lithium ion battery equivalent circuit digital model simulation data and the lithium ion battery physical test data are utilized to form a lithium ion battery double-source data structure, abnormal operation conditions of the lithium ion battery can be judged by analyzing double-source data deviation, simulation model parameters are set by minimizing errors of the simulation model data and measurement data after the abnormal operation of the lithium ion battery is judged, and the accurate positioning faults of the lithium ion battery are obtained by analyzing the parameter changes of the model. Compared with the existing data-based lithium ion battery early warning method, the method has the advantages that the data quality requirement of the lithium ion battery is greatly reduced, and further, the reliability of fault location is realized, compared with the model-based lithium ion operation risk method, the dependence on model accuracy is reduced, the accuracy of abnormality judgment and fault location of the lithium ion battery is ensured, and the fault type of the lithium ion battery is effectively identified. In addition, compared with the existing digital mirror image or digital twin technology of the lithium ion battery based on digital-analog driving, the invention can realize effective simulation and accurate identification of fault conditions by analyzing the parameter change of the model, is beneficial to improving the operation safety and reliability of a lithium ion battery system, improves the accuracy and reliability of abnormality judgment and fault type identification of the lithium ion battery, and reduces the workload of operation, maintenance and overhaul.
As a preferred scheme, according to test data, an equivalent circuit structure of the lithium ion battery is subjected to model parameter fitting and charge state estimation, and an equivalent circuit digital model is built, specifically:
according to the equivalent circuit structure and the fault type of the lithium ion battery, selecting a to-be-determined parameter equivalent circuit digital model with a preset order; wherein, the fault type comprises internal short circuit, abnormal growth of solid electrolyte interface SEI film and lithium dendrite;
setting a function undetermined coefficient by a parameter optimization method according to the undetermined parameter equivalent circuit digital model and test data, and obtaining a function expression of model parameters and influence factors of the undetermined parameter equivalent circuit digital model; the function undetermined coefficient is determined according to the functional relation between the model parameters of the undetermined parameter equivalent circuit digital model and the influence factors; the parameter optimization method comprises a least square method, a particle swarm method and a simulated annealing method;
and estimating the state of charge of lithium ions by using the equivalent circuit digital model and the function expression of the undetermined parameters to obtain a state of charge value of the first lithium ion battery, and establishing an equivalent circuit digital model according to the state of charge value of the first lithium ion battery and the equivalent circuit digital model of the undetermined parameters.
As a preferred scheme, under the preset double-source deviation condition, according to an optimization variable, the parameter variation of the equivalent circuit digital model is minimized, specifically:
model parameters and influencing factors of the equivalent circuit digital model are used as optimization variables; wherein, the influencing factors comprise charge state, temperature and current charge-discharge multiplying power;
according to the optimization variable and a preset double-source deviation condition, an optimization objective function and a constraint condition are constructed;
carrying out particle swarm optimization calculation on the optimization variable, the objective function and the constraint condition to obtain a parameter value of the optimization variable in an abnormal state;
and calculating the parameter variation according to the parameter value of the optimized variable and the parameter value of the equivalent circuit digital model under the abnormal state.
As a preferred solution, the optimization objective function is specifically:
wherein J is an optimization objective function; x (t) = [ X ] i (t)|i＝1,2,…,n]Model parameters of an equivalent circuit digital model of the lithium ion battery before the lithium ion battery is judged to be in an abnormal state at the time t; x is X + (t)＝[x i + (t)|i＝1,2,…,n]Representing parameter variables to be optimized of an equivalent circuit digital model of the lithium ion battery after the lithium ion battery is judged to be in an abnormal state at the time t; n represents the number of optimization variables;
constraint conditions are specifically as follows:
s.t.α + ≤X + (t)≤β +
Wherein alpha is + And beta + Upper and lower boundary constraints of the optimization variables respectively; f (X) + (t)) represents an output voltage function, U M (t) represents the output voltage measurement value of the lithium ion battery physical system at the moment t;in order to preset the condition of the double source deviation,and sigma (t) respectively represent the average value and standard deviation of the bias values of the dual-source data of the lithium ion battery in the period L before the time t.
As a preferred scheme, according to the equivalent circuit digital model and the lithium ion battery physical system, generating dual-source data of the lithium ion battery, specifically:
collecting output current and output voltage of a lithium ion battery physical system in the actual running process, and calibrating the output voltage into physical measurement data;
and inputting the output current into the equivalent circuit digital model, and obtaining simulation analog data of the output voltage of the equivalent circuit digital model through simulation calculation.
As a preferred scheme, the method for statistically analyzing the deviation similarity of the double-source data of the operation preset time of the lithium ion battery specifically comprises the following steps:
running the lithium ion battery for a preset time, and counting all double-source data of the lithium ion battery under the preset time;
determining a time window according to the measurement error influence factors, and calculating the deviation value and the deviation similarity of the double-source data of the lithium ion battery in the time window according to all the double-source data of the lithium ion battery in the preset time; wherein the bias similarity includes a bias average and a standard deviation.
As a preferred scheme, the current running state of the lithium ion battery is judged according to the deviation similarity, specifically:
obtaining a normal interval range according to the deviation average value and the standard deviation;
according to the double-source data of the lithium ion battery in the current running state, calculating the current deviation value of the lithium ion battery;
if the current deviation value of the lithium ion battery is in the normal interval range, judging that the current running state of the lithium ion battery is a normal state;
if the current deviation value of the lithium ion battery is not in the normal interval range, judging that the current running state of the lithium ion battery is an abnormal state.
As a preferred scheme, carrying out a preset test experiment on the lithium ion battery to obtain test data, wherein the test data specifically comprises:
under different test environments and test conditions, performing a hybrid power pulse characteristic test and an open-circuit voltage test on the lithium ion battery to obtain test data;
the test data are output voltage data and output current data of the lithium ion battery under different temperatures, different charge and discharge multiplying powers and different charge states.
As a preferred scheme, after judging the fault type of the lithium ion battery according to the parameter variation, obtaining a fault identification result, the method further comprises the following steps:
According to the identification result, the lithium ion battery is maintained and overhauled;
carrying out a preset test experiment again on the lithium ion battery after maintenance and overhaul to obtain latest test data, carrying out model parameter fitting and charge state estimation on an equivalent circuit structure of the lithium ion battery according to the latest test data, and establishing a latest equivalent circuit digital model;
and the latest equivalent circuit digital model is used for identifying the faults of the lithium ion battery after maintenance and overhaul.
In order to solve the same technical problems, the embodiment of the invention further provides a fault identification system of a lithium ion battery, which comprises: the method comprises the steps of establishing a model module, a dual-source data module, an abnormality judging module and a fault identifying module;
the model building module is used for carrying out a preset test experiment on the lithium ion battery to obtain test data, carrying out model parameter fitting and charge state estimation on an equivalent circuit structure of the lithium ion battery according to the test data, and building an equivalent circuit digital model;
the double-source data module is used for generating double-source data of the lithium ion battery according to the equivalent circuit digital model and the lithium ion battery physical system; the double-source data comprises simulation data and physical measurement data;
The abnormality judging module is used for statistically analyzing the deviation similarity of the double-source data of the operation preset time of the lithium ion battery and judging the current operation state of the lithium ion battery according to the deviation similarity;
the fault identification module is used for minimizing the parameter variation of the equivalent circuit digital model according to the optimized variable under the preset double-source deviation condition when the current running state of the lithium ion battery is in an abnormal state, and judging the fault type of the lithium ion battery according to the parameter variation to obtain a fault identification result.
Drawings
Fig. 1: a schematic flow chart of an embodiment of a fault identification method of a lithium ion battery provided by the invention;
fig. 2: the invention provides a fault identification method structure schematic diagram of one embodiment of a fault identification method of a lithium ion battery;
fig. 3: an equivalent circuit structure diagram of an embodiment of a fault identification method of a lithium ion battery is provided by the invention;
fig. 4: the invention provides a schematic diagram for judging the abnormality of a lithium ion battery in one embodiment of a fault identification method of the lithium ion battery;
fig. 5: a flow chart for calculating model parameters for a particle swarm optimization method of an embodiment of a fault identification method of a lithium ion battery provided by the invention;
Fig. 6: the invention provides a structural schematic diagram of one embodiment of a fault identification system of a lithium ion battery;
fig. 7: the invention provides an execution flow diagram of one embodiment of a fault identification system of a lithium ion battery.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a flow chart of a fault identification method of a lithium ion battery according to an embodiment of the invention is shown, wherein the structure of the fault identification method is shown in fig. 2. The fault identification method is suitable for the lithium ion battery, the abnormal operation condition of the lithium ion battery is judged by analyzing double-source data deviation through a double-source data structure of the lithium ion battery, the fault type of the lithium ion battery is effectively identified by minimizing the parameter variation of the equivalent circuit digital model, and the accuracy and reliability of the abnormality judgment and the fault type identification of the lithium ion battery are improved. The fault identification method comprises steps 101 to 104, wherein the steps are as follows:
Step 101: and carrying out a preset test experiment on the lithium ion battery to obtain test data, carrying out model parameter fitting and charge state estimation on an equivalent circuit structure of the lithium ion battery according to the test data, and establishing an equivalent circuit digital model.
In the embodiment, a digital model of an equivalent circuit of the lithium ion battery is established, a hybrid power pulse characteristic test and an open-circuit voltage test are carried out on the lithium ion battery under different environments and test conditions, an equivalent circuit structure of the lithium ion battery is assumed, model parameters are set by using test data and a parameter fitting method, and a state-of-charge estimation method based on the equivalent circuit model of the lithium ion battery is designed.
Optionally, performing a preset test experiment on the lithium ion battery to obtain test data, which specifically includes: under different test environments and test conditions, performing a hybrid power pulse characteristic test and an open-circuit voltage test on the lithium ion battery to obtain test data; the test data are output voltage data and output current data of the lithium ion battery under different temperatures, different charge and discharge multiplying powers and different charge states.
In this embodiment, under different test environments and test conditions, a hybrid power pulse characteristic test and an open-circuit voltage test are performed on a lithium ion battery to obtain output voltage data and output current data, i.e., test data, of the lithium ion battery under different conditions of different temperatures, charge-discharge multiplying powers, different states of charge and the like.
Optionally, according to the test data, performing model parameter fitting and charge state estimation on an equivalent circuit structure of the lithium ion battery, and establishing an equivalent circuit digital model, wherein the method specifically comprises the following steps 11 to 13:
step 11: according to the equivalent circuit structure and the fault type of the lithium ion battery, selecting a to-be-determined parameter equivalent circuit digital model with a preset order; wherein the fault types include internal short circuit, abnormal growth of a solid electrolyte interface SE I film and lithium dendrite;
in this embodiment, the capability of the lithium ion battery circuit equivalent circuit digital model to reflect common faults of the lithium ion battery is combined, and according to fault identification requirements (such as internal short circuit, abnormal growth of a solid electrolyte interface SE I film, lithium dendrite and other fault types (abnormal problems) under specific application, the lithium ion battery equivalent circuit digital models with different orders are selected to obtain output voltages of the equivalent circuit digital model, namely, according to the equivalent circuit structure and the fault types of the lithium ion battery, the undetermined parameter equivalent circuit digital model with preset orders is selected. Such as: the Rint lithium ion battery equivalent circuit only shows a voltage source and one series internal resistance, so that the Rint lithium ion battery equivalent circuit can only reflect faults of internal resistance change, such as short circuit and aging; besides the internal resistance of Rint model, the Thevenin second-order equivalent circuit can simulate the electrochemical polarization and concentration polarization processes of the lithium ion battery and has the capability of reflecting faults such as lithium dendrites of the lithium ion battery.
As an example of the present embodiment, taking a second-order dyvenin equivalent circuit digital model as an example, the equivalent circuit structure, as shown in fig. 3, can obtain the output voltage of the second-order dyvenin equivalent circuit digital model as follows:
wherein U is ocv (t) is the battery open-circuit voltage of lithium ion at t time, τ 1 (t) and τ 2 (t) the time delay constants of the electrochemical polarization process and the concentration polarization process of the lithium ion battery at the t moment are R b (t)×C b (t) and R th (t)×C th (t)，R 0 (t) is the equivalent internal resistance of the lithium ion battery at the moment t, R b (t) and C b (t) electrochemical polarized internal resistance and capacitance of lithium ion battery at t moment respectively, R th (t) and C th (t) is concentration polarization internal resistance/capacitance of the lithium ion battery at the moment t, I 0 (t) is the output current of the lithium ion battery at the moment t, U L And (t) is the output terminal voltage of the lithium ion battery at the time t.
Step 12: setting a function undetermined coefficient by a parameter optimization method according to the undetermined parameter equivalent circuit digital model and test data, and obtaining a function expression of model parameters and influence factors of the undetermined parameter equivalent circuit digital model; the function undetermined coefficient is determined according to the functional relation between the model parameters of the undetermined parameter equivalent circuit digital model and the influence factors; the parameter optimization method comprises a least square method, a particle swarm method and a simulated annealing method;
In this embodiment, the functional relationship between the parameters of the equivalent circuit digital model of the lithium ion battery and the influencing factors thereof is the functional relationship between the parameters of the equivalent circuit digital model of the undetermined parameters and the influencing factors such as the state of charge, the temperature, the charge-discharge multiplying power and the like, such as a quadratic function, a cubic function and the like, and the to-be-set coefficients in the functions of the equivalent circuit model parameters and the influencing factors are determined; which is a kind ofIn the model parameters U ocv (t)、R 0 (t)、R b (t)、C b (t)、R th (t) and C th (t) are undetermined parameters of the equivalent circuit digital model of the lithium ion battery at the moment t, and the undetermined parameters are the battery charge state S of the lithium ion at the moment t soc (T), temperature T (T), and current charge-discharge rate C r And (t) and other factors, namely:
based on the test data of the lithium ion battery obtained by the test in the step 101 and the lithium ion battery equivalent circuit model set in the step 12, the function coefficients are set by using a least square method or other optimization methods, such as particle swarm, simulated annealing and other optimization methods, so that the function expression of the parameters of the lithium ion battery equivalent circuit model and the influence factors thereof, namely the function expression of the model parameters of the undetermined parameter equivalent circuit digital model and the influence factors thereof, is obtained.
Step 13: and estimating the state of charge of lithium ions by using the equivalent circuit digital model and the function expression of the undetermined parameters to obtain a state of charge value of the first lithium ion battery, and establishing an equivalent circuit digital model according to the state of charge value of the first lithium ion battery and the equivalent circuit digital model of the undetermined parameters.
In this embodiment, according to the undetermined parameter digital model, model parameters and function expression of the equivalent circuit of the lithium ion battery in steps 11 and 12, the state of charge value of the lithium ion battery is calculated by adopting the state of charge estimation of the lithium ion battery, so as to complete the establishment of the digital model of the equivalent circuit of the lithium ion battery. The lithium ion state of charge estimation method is based on Kalman state optimal estimation theory or ampere-hour integral and the like.
It should be noted that, if the equivalent circuit digital model parameters are affected by the charge state, the temperature, the charge-discharge multiplying power and other factors in step 12, the charge state value is calculated in step 13 and is applied to the model parameter calculation of the undetermined parameter equivalent circuit digital model, thereby completing the establishment of the equivalent circuit digital model of the lithium ion battery.
Step 102: generating double-source data of the lithium ion battery according to the equivalent circuit digital model and the lithium ion battery physical system; wherein the dual source data includes simulation data and physical measurement data.
In this embodiment, the generation of the dual-source data of the lithium ion battery is to input the output current of the physical system of the lithium ion battery into the digital model of the equivalent circuit of the lithium ion battery, calculate the simulation data of the equivalent circuit model of the lithium ion battery and the physical measurement data (such as the output voltage of the lithium ion battery) of the physical system, and generate the dual-source data of the simulation and actual measurement of the lithium ion battery.
Optionally, step 102 specifically includes steps 1021 through 1022, where each step specifically includes:
step 1021: and collecting output current and output voltage of a lithium ion battery physical system in the actual running process, and calibrating the output voltage into physical measurement data.
In this embodiment, the output current and output voltage of the physical system of the lithium ion battery in the actual running process are collected, the collected output voltage is calibrated to be the experimental source data, namely the physical measurement data, of the lithium ion battery, and the collected output voltage is recorded as U at the time t M (t)。
Step 1022: and inputting the output current into the equivalent circuit digital model, and obtaining simulation analog data of the output voltage of the equivalent circuit digital model through simulation calculation.
In this embodiment, the acquired output current of the physical system of the lithium ion battery is loaded into the digital model of the equivalent circuit of the lithium ion battery obtained in step 101, and simulation analog data of the output voltage of the digital model of the equivalent circuit of the lithium ion battery is obtained through simulation calculation, calibrated as simulation source data of the lithium ion battery, and marked as U at time t L (t)；
Step 103: and (3) carrying out statistical analysis on the deviation similarity of the double-source data of the operation preset time of the lithium ion battery, and judging the current operation state of the lithium ion battery according to the deviation similarity.
In this embodiment, the abnormality of the lithium ion battery is determined, the similarity of the double-source data deviation of the lithium ion battery under long-time operation is analyzed, and the operation condition of the lithium ion battery is determined.
Optionally, step 103 specifically includes steps 1031 to 1032, where each step specifically includes the following steps:
step 1031: running the lithium ion battery for a preset time, and counting all double-source data of the lithium ion battery under the preset time; determining a time window according to the measurement error influence factors, and calculating the deviation value and the deviation similarity of the double-source data of the lithium ion battery in the time window according to all the double-source data of the lithium ion battery in the preset time; wherein the bias similarity includes a bias average and a standard deviation.
Step 1032: obtaining a normal interval range according to the deviation average value and the standard deviation; according to the double-source data of the lithium ion battery in the current running state, calculating the current deviation value of the lithium ion battery; if the current deviation value of the lithium ion battery is in the normal interval range, judging that the current running state of the lithium ion battery is a normal state; if the current deviation value of the lithium ion battery is not in the normal interval range, judging that the current running state of the lithium ion battery is an abnormal state.
In this embodiment, a dual-source data deviation value Δu (t) = |u of the lithium ion battery voltage is calculated M (t)-U L And (t) representing the difference between the physical system of the lithium ion battery and the equivalent circuit digital model, wherein the equivalent circuit digital model of the lithium ion battery is based on the output voltage of the lithium ion battery in the normal state and the state of the physical system of the lithium ion battery is unknown because the equivalent circuit digital model of the lithium ion battery is established in the normal state of the lithium ion battery. If the physical system is normal, the dual-source data deviation value delta U (t) is basically unchanged and smaller; if the physical system is abnormal, the double-source data deviation value delta U (t) is increased, so that the running state of the lithium ion battery can be detected by analyzing the change condition of the double-source data deviation value delta U (t) of the lithium ion battery.
When judging the running state of the lithium ion battery, the influence of modeling errors of a digital model of the lithium ion battery, aging factors of the lithium ion battery, measurement errors of signals such as current and voltage and the like need to be considered, and the influence factor is based on the measurement errorsDetermining a time window L by using factors (such as modeling errors, aging factors of the lithium ion battery, measuring errors of current and voltage, and the like), and calculating an average value (deviation average value) of double-source data deviation values delta U (t) of the lithium ion battery in a period from t-L to t when the abnormal state of the lithium ion battery is judged at the moment t Standard deviation->And obtaining a normal interval range according to the deviation average value and the standard deviation. If the double-source data deviation value of the lithium ion battery at the time t is in the normal interval range, if: />Judging that the lithium ion battery is in a normal running state; otherwise, judging that the lithium ion battery is in an abnormal operation state, and when the lithium ion battery is in the abnormal operation state, continuing to locate the fault type of the lithium ion battery, wherein the schematic diagram of the abnormal judgment of the lithium ion battery is shown in fig. 4.
Step 104: and if the current running state of the lithium ion battery is an abnormal state, minimizing the parameter variation of the equivalent circuit digital model according to the optimized variable under the preset double-source deviation condition, and judging the fault type of the lithium ion battery according to the parameter variation to obtain a fault identification result.
In this embodiment, location and identification of a fault type of the lithium ion battery under an abnormal state of the lithium ion battery are established, when the lithium ion battery is determined to be in the abnormal state, by optimizing parameters of a digital model of an equivalent circuit of the lithium ion battery, under a condition of taking into account conventional deviation levels of double-source data of the lithium ion battery (preset double-source deviation condition), the variable quantity of the parameters of the digital model of the equivalent circuit is minimized, the fault type of the lithium ion battery is determined according to the variable condition (variable quantity of the parameters) of the digital model of the equivalent circuit of the lithium ion battery, the fault location of the lithium ion battery is completed, and the fault type of the lithium ion battery is determined.
Optionally, step 104 specifically includes steps 1041 to 1045, where each step specifically includes the following steps:
step 1041: model parameters and influencing factors of the equivalent circuit digital model are used as optimization variables; wherein, influencing factors include charge state, temperature and current charge-discharge multiplying power.
In this embodiment, parameters and influencing factors of an equivalent model of a lithium ion battery are taken as optimization variables, and as an example of this embodiment, a second-order Thevenin equivalent circuit digital model is taken as an example, wherein the model parameters U + ocv (t)、R + 0 (t)、R + b (t)、C + b (t)、R + th (t)、C + th (t) lithium ion battery state of charge S + soc (T), temperature T + (t) Current Charge-discharge multiplying factor C + r And (t) and other factors are optimization variables.
Step 1042: according to the optimization variable and a preset double-source deviation condition, an optimization objective function and a constraint condition are constructed;
in the embodiment, the minimized equivalent circuit digital model parameter variation is taken as the constraint conditions of the optimization objective function, the optimization variable and the double-source data deviation range.
Optionally, the objective function is optimized, specifically:
wherein J is an optimization objective function; x (t) = [ X ] i (t)|i＝1,2,…,n]Model parameters of an equivalent circuit digital model of the lithium ion battery before the lithium ion battery is judged to be in an abnormal state at the time t; x is X + (t)＝[x i + (t)|i＝1,2,…,n]Representing parameter variables to be optimized of an equivalent circuit digital model of the lithium ion battery after the lithium ion battery is judged to be in an abnormal state at the time t; n represents the number of optimization variables;
optionally, the constraint conditions specifically are:
s.t.α + ≤X + (t)≤β +
wherein alpha is + And beta + Upper and lower boundary constraints of the optimization variables respectively; f (X) + (t)) represents an output voltage function, U M (t) represents the output voltage measurement value of the lithium ion battery physical system at the moment t;in order to preset the condition of the double source deviation,and sigma (t) respectively represent the average value and standard deviation of the bias values of the dual-source data of the lithium ion battery in the period L before the time t.
Taking a second-order Thevenin equivalent circuit digital model as an example of the embodiment, the optimization objective function J and the constraint conditions are as follows:
s.t.α + ≤X + (t)≤β +
wherein X (t) = [ X ] 1 (t),x 2 (t),x 3 (t),x 4 (t),x 5 (t),x 6 (t),x 7 (t),x 8 (t),x 9 (t)]＝[U ocv (t),R 0 (t),R b (t),C b (t),R th (t),C th (t),S soc (t),T(t),C r (t)]Representing the equivalent circuit digital model parameters of the lithium ion battery before the lithium ion battery is judged to be in an abnormal state at the time t; x is X + (t)＝[x + 1 (t),x + 2 (t),x + 3 (t),x + 4 (t),x + 5 (t),x + 6 (t),x + 7 (t),x + 8 (t),x + 9 (t)]＝[U + ocv (t),R + 0 (t),R + b (t),C + b (t),R + th (t),C + th (t),S + soc (t),T + (t),C + r (t)]Representing parameter variables to be optimized of a lithium ion battery equivalent circuit digital model after the lithium ion battery is judged to be in an abnormal state at the time t; alpha + And beta + Respectively representing upper and lower boundary constraints of the optimization variables; f (X) + (t)) represents the output voltage function of the lithium ion battery equivalent circuit digital model.
Step 1043: and carrying out particle swarm optimization calculation on the optimization variable, the objective function and the constraint condition to obtain the parameter value of the optimization variable in the abnormal state.
In this embodiment, for the constructed optimization objective function and constraint conditions, the optimization method is used to calculate the parameter variable to be optimized of the lithium ion battery equivalent circuit digital model after the lithium ion battery is determined to be in an abnormal state, and calculate the parameter value of the optimized variable in the abnormal state, that is, the parameter variable X to be optimized of the lithium ion battery equivalent circuit digital model after the lithium ion battery is determined to be in the abnormal state + (t), wherein the optimization method comprises but is not limited to a particle swarm optimization method, and the particle swarm optimization method calculates a flow chart of model parameters after the lithium ion battery judges that the operation is abnormal, as shown in fig. 5. Setting particle swarm algorithm parameters, initializing particle positions represented by equivalent model parameters of the lithium ion battery, calculating fitness function (objective function) represented by each particle, re-initializing particles which do not meet constraint, sequencing fitness function values of the iteration, taking a minimum value of the fitness function, judging whether the fitness function value is smaller than a historical minimum fitness function value, judging whether a cut-off condition is met if the fitness function value is not smaller than the historical minimum fitness function value, and updating the historical minimum fitness function value and the historical self minimum value of each particle when the cut-off condition is met. If it is smaller than the history minimum And updating the historical minimum fitness function value and the historical own minimum value of each particle, judging whether the cutoff condition is met, if the cutoff condition is not met, continuing to use a particle swarm algorithm updating formula to update the particle position, obtaining a new particle position, calculating the fitness function (objective function) represented by each particle, reinitializing the particles which do not meet the constraint, sequencing the fitness function value of the iteration, taking the fitness function minimum value, and judging whether the fitness function minimum value is smaller than the historical minimum fitness function value until the cutoff condition is met.
Step 1044: and calculating the parameter variation according to the parameter value of the optimized variable and the parameter value of the equivalent circuit digital model under the abnormal state.
In this embodiment, the deviation rate of the parameters of the equivalent circuit digital model of the lithium ion battery before and after the lithium ion battery is judged to be in an abnormal state, namely, the parameter variation delta (t), the optimization variable in the abnormal state is the parameter value of the equivalent circuit digital model of the lithium ion battery after the lithium ion battery is judged to be in the abnormal state, and the parameter value of the equivalent circuit digital model is the parameter value of the equivalent circuit digital model of the lithium ion battery before the lithium ion battery is judged to be in the abnormal state, namely, the parameter variation delta (t) = |X (t) -X + (t)|/X(t)。
Step 1045: and judging the fault type of the lithium ion battery according to the parameter variation to obtain a fault identification result.
In this embodiment, the fault type of the lithium ion battery is determined according to the parameter variation (deviation rate), for example: internal resistance parameter R of lithium ion battery 0 The deviation ratio of (t) is close to 1, i.e. the internal resistance of the lithium ion battery calculated in step 1044 is close to 0, which indicates that the lithium ion battery may have internal short circuit. The larger the deviation rate is, the more obvious the fault is, if other faults are indicated, such as lithium dendrites of a lithium ion battery, the electrochemical polarization parameter R in the equivalent model is required b The deviation ratio of (t) is close to 1. Other fault types are determined, and so on.
Optionally, after determining the fault type of the lithium ion battery according to the parameter variation, obtaining the fault identification result, the method further includes: according to the identification result, the lithium ion battery is maintained and overhauled; carrying out a preset test experiment again on the lithium ion battery after maintenance and overhaul to obtain latest test data, carrying out model parameter fitting and charge state estimation on an equivalent circuit structure of the lithium ion battery according to the latest test data, and establishing a latest equivalent circuit digital model; and the latest equivalent circuit digital model is used for identifying the faults of the lithium ion battery after maintenance and overhaul.
In this embodiment, the fault type of the lithium ion battery is determined according to the parameter variation (deviation rate), and the lithium ion battery is maintained and overhauled based on the identification result of the fault type of the lithium ion battery. And after maintenance and overhaul, re-acquiring lithium ion battery test data under different environments and test conditions, and establishing a new lithium ion battery digital model module for subsequent abnormality judgment and fault positioning of the lithium ion battery.
It should be noted that, in order to integrate two types of methods (a mechanism method and a data method), and simultaneously, by means of the current digital twin thought and a diagnosis mode of process control, a method for detecting an abnormal state of a lithium ion battery based on hybrid driving of data and a model is proposed, but still in a starting stage, the method is mostly used for calibrating the abnormal state of the lithium ion battery, is not used for determining the type of battery fault, and has insufficient development and application strength for the technology. Therefore, the invention provides the fault identification method of the lithium ion battery around the problems of abnormality detection and fault location of the lithium ion battery, and establishes a foundation for large-scale safety application of the lithium ion battery by taking the deviation of a physical system of the lithium ion battery and a digital model as an access point and locating the fault of the lithium ion battery based on double-source data.
According to the embodiment of the invention, the lithium ion battery equivalent circuit digital model simulation data and the lithium ion battery physical test data are utilized to form a lithium ion battery double-source data structure, the abnormal operation condition of the lithium ion battery can be judged by analyzing the double-source data deviation, after the abnormal operation of the lithium ion battery is judged, the simulation model parameters are set by minimizing the errors of the simulation model data and the measurement data, and the accurate positioning faults of the lithium ion battery are obtained by analyzing the model parameter changes. Compared with the existing data-based lithium ion battery early warning method, the method has the advantages that the data quality requirement of the lithium ion battery is greatly reduced, and further, the reliability of fault location is realized, compared with the model-based lithium ion operation risk method, the dependence on model accuracy is reduced, the accuracy of abnormality judgment and fault location of the lithium ion battery is ensured, and the fault type of the lithium ion battery is effectively identified. In addition, compared with the existing digital mirror image or digital twin technology of the lithium ion battery based on digital-analog driving, the invention can realize effective simulation and accurate identification of fault conditions by analyzing the parameter change of the model, is beneficial to improving the operation safety and reliability of a lithium ion battery system, improves the accuracy and reliability of abnormality judgment and fault type identification of the lithium ion battery, and reduces the workload of operation, maintenance and overhaul. The application example of the lithium ion battery is identified by utilizing the positioning of the fault types of the lithium ion battery with double source data, the digital-analog driving capability of the lithium ion battery is fully exerted, the self-adaptive judgment of the operation safety state of the lithium ion battery and the autonomous identification of the fault risk type are effectively and simply realized, the reliability and the intelligence of the application of the lithium ion battery are improved, and a new path is brought to the safety landing application and the convenience maintenance of the large-scale and large-capacity lithium ion battery.
Example two
Accordingly, referring to fig. 6, fig. 6 is a schematic structural diagram of a second embodiment of a fault identification system for a lithium ion battery according to the present invention. The execution flow diagram of the fault identification system of the lithium ion battery is shown in fig. 7. As shown in fig. 6, the fault identification system of the lithium ion battery includes a modeling module 601, a dual-source data module 602, an abnormality judgment module 603, and a fault identification module 604;
the model building module 601 is configured to perform a preset test experiment on a lithium ion battery to obtain test data, perform model parameter fitting and charge state estimation on an equivalent circuit structure of the lithium ion battery according to the test data, and build an equivalent circuit digital model;
in this embodiment, the model building module 601 is a lithium ion battery digital modeling module S1, performs a hybrid power pulse characteristic test and an open circuit voltage test on a lithium ion battery under different environments and test conditions, and builds a lithium ion battery equivalent circuit digital model and a state of charge estimation method by using a parameter fitting mode based on test data.
The dual-source data module 602 is configured to generate dual-source data of the lithium ion battery according to the equivalent circuit digital model and the lithium ion battery physical system; the double-source data comprises simulation data and physical measurement data;
In this embodiment, the dual-source data module 602 is the dual-source data generating module S2, and calculates simulation data of the lithium ion battery equivalent circuit digital model and measurement data of the physical system based on the output current of the physical system of the lithium ion battery, thereby completing the dual-source data generating process.
The anomaly judgment module 603 is configured to statistically analyze deviation similarity of dual-source data of a preset operation time of the lithium ion battery, and judge a current operation state of the lithium ion battery according to the deviation similarity;
in this embodiment, the abnormality determination module 603 is a battery abnormality determination module S3, which analyzes the similarity of the double-source data deviation of the lithium ion battery during long-time operation, and determines the abnormal situation of the lithium ion battery.
The fault identification module 604 is configured to minimize a parameter variation of the equivalent circuit digital model according to the optimization variable under a preset double-source deviation condition if the current operation state of the lithium ion battery is an abnormal state, and determine a fault type of the lithium ion battery according to the parameter variation, so as to obtain a fault identification result.
In this embodiment, the fault identification module 604 is a battery fault positioning and identification module S4, optimizes parameters of an equivalent circuit digital model of the lithium ion battery when the lithium ion battery is abnormal, minimizes a variation of the parameters of the equivalent circuit digital model under the condition of a conventional deviation level of double source data of the lithium ion battery, and determines a fault form of the lithium ion battery according to the variation of the parameters.
By implementing the embodiment of the invention, the digital twin simulation of the lithium ion battery system can be realized, the operation state evaluation of the lithium ion battery is completed, the accurate evaluation of the safety risk and the fault type of the lithium ion battery is formed, and the application reliability and the maintenance convenience of the lithium ion battery energy storage power station are beneficial. The lithium ion battery equivalent circuit digital model simulation data and the lithium ion battery physical test data are utilized to form a lithium ion battery double-source data structure, abnormal operation conditions of the lithium ion battery can be judged by analyzing double-source data deviation, simulation model parameters are set by minimizing errors of simulation model data and measurement data after the abnormal operation of the lithium ion battery is judged, and the accurate positioning faults of the lithium ion battery fault type are obtained by analyzing the parameter changes of the model. Compared with the existing data-based lithium ion battery early warning method, the method has the advantages that the data quality requirement of the lithium ion battery is greatly reduced, and further, the reliability of fault location is realized. In addition, compared with the existing digital mirror image or digital twin technology of the lithium ion battery based on digital-analog driving, the method can realize effective simulation and accurate identification of fault conditions by analyzing the parameter change of the model, is beneficial to improving the running safety and reliability of a lithium ion battery system and reduces the workload of operation, maintenance and overhaul.
The fault identification system of the lithium ion battery can implement the fault identification method of the lithium ion battery in the method embodiment. The options in the method embodiments described above are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the content of the above method embodiments, and in this embodiment, no further description is given.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present application, and are not to be construed as limiting the scope of the application. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present application are intended to be included in the scope of the present application.
Claims (10)
1. The fault identification method of the lithium ion battery is characterized by comprising the following steps of:
carrying out a preset test experiment on a lithium ion battery to obtain test data, carrying out model parameter fitting and charge state estimation on an equivalent circuit structure of the lithium ion battery according to the test data, and establishing an equivalent circuit digital model;
Generating double-source data of the lithium ion battery according to the equivalent circuit digital model and a lithium ion battery physical system; wherein the dual source data includes simulation data and physical measurement data;
the deviation similarity of the double-source data of the operation preset time of the lithium ion battery is statistically analyzed, and the current operation state of the lithium ion battery is judged according to the deviation similarity;
and if the current running state of the lithium ion battery is an abnormal state, minimizing the parameter variation of the equivalent circuit digital model according to an optimized variable under the preset double-source deviation condition, and judging the fault type of the lithium ion battery according to the parameter variation to obtain a fault identification result.
2. The method for identifying faults of a lithium ion battery according to claim 1, wherein the step of performing model parameter fitting and state of charge estimation on an equivalent circuit structure of the lithium ion battery according to the test data to establish an equivalent circuit digital model comprises the following steps:
selecting a to-be-determined parameter equivalent circuit digital model of a preset order according to the equivalent circuit structure of the lithium ion battery and the fault type; wherein the fault type comprises internal short circuit, abnormal growth of solid electrolyte interface SEI film and lithium dendrite;
Setting a function undetermined coefficient by a parameter optimization method according to the undetermined parameter equivalent circuit digital model and the test data, and obtaining a function expression of model parameters and influence factors of the undetermined parameter equivalent circuit digital model; the function undetermined coefficient is determined according to the functional relation between the model parameters of the undetermined parameter equivalent circuit digital model and the influence factors; the parameter optimization method comprises a least square method, a particle swarm method and a simulated annealing method;
and estimating the state of charge of lithium ions by using the undetermined parameter equivalent circuit digital model and the function expression to obtain a first lithium ion battery state of charge value, and establishing the equivalent circuit digital model according to the first lithium ion battery state of charge value and the undetermined parameter equivalent circuit digital model.
3. The method for identifying faults of a lithium ion battery according to claim 2, wherein under a preset double-source deviation condition, the parameter variation of the equivalent circuit digital model is minimized according to an optimization variable, specifically:
taking model parameters of the equivalent circuit digital model and the influencing factors as the optimization variables; wherein the influencing factors comprise charge state, temperature and current charge-discharge multiplying power;
Constructing an optimization objective function and constraint conditions according to the optimization variables and the preset double-source deviation conditions;
performing particle swarm optimization calculation on the optimization variable, the objective function and the constraint condition to obtain a parameter value of the optimization variable in an abnormal state;
and calculating the parameter variation according to the parameter value of the optimized variable in the abnormal state and the parameter value of the equivalent circuit digital model.
4. The method for identifying a fault of a lithium ion battery according to claim 3, wherein the optimization objective function is specifically:
wherein J is the optimization objective function; x (t) = [ X ] i (t)|i＝1,2,…,n]Indicating the lithium ion battery before the lithium ion battery is judged to be in abnormal state at the time tModel parameters of the equivalent circuit digital model; x is X + (t)＝[x i + (t)
I=1, 2, …, n ] represents a parameter variable to be optimized of an equivalent circuit digital model of the lithium ion battery after the lithium ion battery is judged to be in an abnormal state at a time t; n represents the number of the optimization variables;
the constraint conditions are specifically as follows:
s.t.α + ≤X + (t)≤β +
wherein alpha is + And beta + Upper and lower boundary constraints of the optimization variables are respectively set; f (X) + (t)) represents an output voltage function, U M (t) represents the measured value of the output voltage of the physical system of the lithium ion battery at the moment t; For the preset double source bias condition, +.>And sigma (t) respectively represent an average value and a standard deviation of the deviation values of the dual-source data of the lithium ion battery in the period L before the time t.
5. The method for identifying faults of a lithium ion battery according to claim 1, wherein the generating the dual-source data of the lithium ion battery according to the equivalent circuit digital model and a lithium ion battery physical system specifically comprises:
collecting output current and output voltage of the lithium ion battery physical system in the actual running process, and calibrating the output voltage as the physical measurement data;
and inputting the output current into the equivalent circuit digital model, and obtaining simulation analog data of the output voltage of the equivalent circuit digital model through simulation calculation.
6. The method for identifying faults of a lithium ion battery according to claim 1, wherein the statistical analysis of deviation similarity of double-source data of the lithium ion battery in operation for a preset time is specifically as follows:
running the lithium ion battery for the preset time, and counting all double-source data of the lithium ion battery under the preset time;
determining a time window according to the measurement error influence factors, and calculating the deviation value and the deviation similarity of the double-source data of the lithium ion battery in the time window according to all the double-source data of the lithium ion battery in the preset time; wherein the bias similarity includes a bias average and a standard deviation.
7. The method for identifying a fault of a lithium ion battery according to claim 6, wherein the judging the current operation state of the lithium ion battery according to the deviation similarity specifically comprises:
obtaining a normal interval range according to the deviation average value and the standard deviation;
calculating the current deviation value of the lithium ion battery according to the double-source data of the lithium ion battery in the current running state;
if the current deviation value of the lithium ion battery is in the normal interval range, judging that the current running state of the lithium ion battery is a normal state;
and if the current deviation value of the lithium ion battery is not in the normal interval range, judging that the current running state of the lithium ion battery is the abnormal state.
8. The method for identifying faults of a lithium ion battery according to claim 1, wherein the step of performing a preset test experiment on the lithium ion battery to obtain test data comprises the following specific steps:
under different test environments and test conditions, performing a hybrid power pulse characteristic test and an open circuit voltage test on the lithium ion battery to obtain test data;
the test data are output voltage data and output current data of the lithium ion battery under the conditions of different temperatures, different charge and discharge multiplying powers and different charge states.
9. The method for identifying a fault of a lithium ion battery according to claim 1, wherein after the determining the fault type of the lithium ion battery according to the parameter variation, obtaining a fault identification result, further comprises:
according to the identification result, the lithium ion battery is maintained and overhauled;
carrying out a preset test experiment on the lithium ion battery after maintenance and overhaul to obtain latest test data, carrying out model parameter fitting and charge state estimation on an equivalent circuit structure of the lithium ion battery according to the latest test data, and establishing a latest equivalent circuit digital model;
and using the latest equivalent circuit digital model for fault identification of the lithium ion battery after maintenance and overhaul.
10. A fault identification system for a lithium ion battery, comprising: the method comprises the steps of establishing a model module, a dual-source data module, an abnormality judging module and a fault identifying module;
the model building module is used for carrying out a preset test experiment on the lithium ion battery to obtain test data, carrying out model parameter fitting and charge state estimation on an equivalent circuit structure of the lithium ion battery according to the test data, and building an equivalent circuit digital model;
The double-source data module is used for generating double-source data of the lithium ion battery according to the equivalent circuit digital model and a lithium ion battery physical system; wherein the dual source data includes simulation data and physical measurement data;
the abnormality judging module is used for statistically analyzing the deviation similarity of the double-source data of the lithium ion battery in the operation preset time and judging the current operation state of the lithium ion battery according to the deviation similarity;
and the fault identification module is used for minimizing the parameter variation of the equivalent circuit digital model according to the optimized variable under the preset double-source deviation condition when the current running state of the lithium ion battery is an abnormal state, and judging the fault type of the lithium ion battery according to the parameter variation to obtain a fault identification result.
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| CN119337745A (en) * | 2024-12-20 | 2025-01-21 | 中汽数据(天津)有限公司 | A power battery simulation method, device and medium based on AI parameter identification |
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| CN119322271A (en) * | 2024-12-18 | 2025-01-17 | 天津开发区畅洋工贸有限公司 | New energy automobile battery fault diagnosis system |
| CN119337745A (en) * | 2024-12-20 | 2025-01-21 | 中汽数据(天津)有限公司 | A power battery simulation method, device and medium based on AI parameter identification |
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