Calibration method, electronic apparatus, calibration device, vehicle, medium, and product

Abstract

Provided is a second-order equivalent circuit model calibration method. The method comprises: first, acquiring a test data set comprising voltage data, current data, temperature data, discharge rate data, and SOC data of a battery cell under a plurality of operating conditions; next, determining a plurality of parameters to be optimized according to the acquired temperature data, discharge rate data, and SOC data, so as to construct a plurality of models to be optimized for a plurality of second-order equivalent circuit models corresponding to the plurality of parameters to be optimized; then, acquiring, on the basis of the second-order equivalent circuit models, a global operating condition optimization model for minimizing an SOC error metric; then, determining applicable operating conditions of the second-order equivalent circuit models according to the global operating condition optimization model and the previously acquired voltage data, current data, and temperature data; then, performing iterative optimization on the plurality of models to be optimized according to the determined applicable operating conditions and the error metric, so as to determine an optimal solution for the parameters to be optimized; and finally, calibrating an optimization model for the second-order equivalent circuit models according to the optimal solution determined for the parameters to be optimized.

Description

Calibration method, electronic device, calibration equipment, vehicle, medium and product

[0001] Priority information

[0002] The present disclosure claims priority to and the benefit of the patent application with the patent application number 202411389664.1 filed with the China National Intellectual Property Office on September 30, 2024, and incorporates it herein by reference in its entirety. TECHNICAL FIELD

[0003] The present disclosure relates to the technical field of model calibration, and more particularly, to a calibration method of a second-order equivalent circuit model, an electronic device, a calibration equipment of a second-order equivalent circuit model, a vehicle, a computer readable storage medium and a computer program product. BACKGROUND

[0004] The second-order equivalent circuit model is one of the most widely used battery equivalent circuit models, and the terminal voltage of the battery can be estimated based on the second-order equivalent circuit model.

[0005] However, the second-order equivalent circuit model is a highly lumped equivalent model, which cannot fully reflect the electrochemical process inside the battery and cannot be applied to partial working conditions such as large current and low temperature. The parameters of the second-order equivalent circuit model are set in the full working condition range, and the minimum error in the full working condition is taken as the optimization objective. However, due to the existence of working conditions beyond the applicable range of the model, the parameters obtained in some working conditions (for example, the second-order equivalent circuit cannot be applied to partial working conditions such as large current and low temperature) often deviate from the optimal value in the applicable working condition range. SUMMARY

[0006] The present disclosure aims to at least solve one of the technical problems existing in the prior art. To this end, the present disclosure embodiment provides a calibration method of a second-order equivalent circuit model, an electronic device, a calibration equipment of a second-order equivalent circuit model, a vehicle, a computer readable storage medium and a computer program product.

[0007] The calibration method of the second-order equivalent circuit model provided by the embodiments of the present disclosure includes: obtaining a test data set, the test data set including voltage data, current data, temperature data, discharge rate data and SOC data of a battery under multiple working conditions, the SOC data including SOC estimated values of the battery cells; determining multiple to-be-optimized parameters according to the temperature data, the discharge rate data and the SOC data, to construct a to-be-optimized model of the multiple second-order equivalent circuit models corresponding to the multiple to-be-optimized parameters; obtaining a full-condition optimization model, the full-condition optimization model being a second-order equivalent circuit model with minimized SOC error indicators, the SOC error indicators being positively correlated with the difference between the SOC test values and the SOC estimated values determined by each to-be-optimized model under the same battery working condition; determining the applicable working condition of the second-order equivalent circuit model according to the full-condition optimization model, the voltage data, the current data and the temperature data; iteratively optimizing the multiple to-be-optimized models according to the applicable working condition and the error indicators, to determine the optimal solution of the to-be-optimized parameters; and calibrating an optimized model of the second-order equivalent circuit model according to the optimal solution of the to-be-optimized parameters.

[0008] The calibration method of the second-order equivalent circuit model provided by the embodiments of the present disclosure considers the case that the second-order equivalent circuit model is not applicable under some working conditions. By optimizing the model parameters and the model applicable range judgment condition, the model precision and coverage are synergistically optimized, the high precision of the second-order equivalent circuit model within the applicable range is ensured, and the SOC value and terminal voltage estimation error caused by false correction are avoided.

[0009] In some embodiments, the iteratively optimizing the multiple to-be-optimized models according to the applicable working condition and the error indicators to determine the optimal solution of the to-be-optimized parameters includes: determining a sample proportion value of the total sample quantity in the test data set that does not match the applicable working condition, to determine a deactivation rate indicator, the deactivation rate indicator being positively correlated with the sample proportion value; and iteratively optimizing the multiple to-be-optimized models to determine the optimal solution of the to-be-optimized parameters, with the minimization of a synergy indicator as the target, the synergy indicator being positively correlated with the SOC error indicator and the deactivation rate indicator.

[0010] In some embodiments, the iteratively optimizing the multiple to-be-optimized models to determine the optimal solution of the to-be-optimized parameters, with the minimization of a synergy indicator as the target, includes: iteratively optimizing the multiple to-be-optimized models to determine the optimal solution of the to-be-optimized parameters, with the minimization of a synergy indicator as the target, under the condition that the SOC error indicator is lower than a first value and the deactivation rate indicator is lower than a second value.

[0011] In some embodiments, the plurality of optimization models are iteratively optimized to determine the optimal solution of the optimization parameters with the minimization of the synergy index as the target, including: obtaining a fitness function model corresponding to the synergy index, the fitness function model being configured to determine the synergy index according to a weighted sum of the SOC error and the deactivation rate index; obtaining a plurality of optimization weight values to construct an optimization weight value model of the plurality of fitness function models corresponding to the plurality of optimization weight values; under the condition that the SOC error index is lower than a first value and the deactivation rate index is lower than a second value, iteratively optimizing the plurality of target models to determine the optimal solution of the optimization weight values with the minimization of the product of the values of the SOC error index and the deactivation rate index as the target; and iteratively optimizing the plurality of optimization models to determine the optimal solution of the optimization parameters with the minimization of the synergy index as the target according to the optimal model of the fitness function model corresponding to the optimal solution of the optimization weight values.

[0012] In some embodiments, the voltage data includes a plurality of voltage test values of the battery at different time periods, the current data includes a plurality of current test values of the battery at different time periods, and the temperature data includes a plurality of temperature test values of the battery at the same time. The applicable working condition of the second-order equivalent circuit model is determined according to the full-working-condition optimization model, the voltage data, the current data, and the temperature data, including: obtaining a plurality of voltage difference values of the voltage test values and the voltage estimated values determined by the full-working-condition optimization model at different time periods to determine a modeling error sequence under the condition that the battery working conditions are the same; determining a plurality of average current sequences under a plurality of time scales according to the plurality of current test values of the battery at different time periods; determining the current applicable working condition of the second-order equivalent circuit model according to the plurality of average current sequences and the modeling error sequence; determining a plurality of voltage fluctuation rate sequences under a plurality of time scales according to the plurality of voltage test values of the battery at different time periods; determining the voltage applicable working condition of the second-order equivalent circuit model according to the plurality of voltage fluctuation rate sequences and the modeling error sequence; and determining the temperature applicable working condition of the second-order equivalent circuit model according to the plurality of temperature test values of the battery at the same time.

[0013] In some embodiments, the determining the current applicable operating condition of the second-order equivalent circuit model according to the plurality of average current sequences and the modeling error sequence comprises: sequentially calculating correlation coefficients of the plurality of average current sequences and the modeling error sequence; sorting time scale factors corresponding to the average current sequences from small to large, and selecting the average current sequence corresponding to the maximum correlation coefficient to determine a target current sequence and a first target time scale factor corresponding to the target current sequence; constructing a current scale vector according to the first target scale factor and p+1 average current sequences corresponding to p adjacent continuous time scale factors of the first target scale factor, p being any positive integer; obtaining a maximum current value in the current scale vector; and determining the current applicable operating condition of the second-order equivalent circuit model according to a proportional value of each current value in the current scale vector to the maximum current value.

[0014] In some embodiments, the determining the current applicable operating condition of the second-order equivalent circuit model according to the proportional value of each current value in the current scale vector to the maximum current value comprises: determining that a test current value sample corresponding to the current scale vector satisfies the current applicable operating condition of the second-order equivalent circuit model when the proportional value of each current value in the current scale vector to the maximum current value is less than or equal to a first scaling coefficient.

[0015] In some embodiments, the determining the voltage applicable operating condition of the second-order equivalent circuit model according to the plurality of voltage fluctuation rate sequences and the modeling error sequence comprises: sequentially calculating correlation coefficients of the plurality of voltage fluctuation rate sequences and the modeling error sequence; sorting time scale factors corresponding to the voltage fluctuation rate sequences from small to large, and selecting the voltage fluctuation rate sequence corresponding to the maximum correlation coefficient to determine a target voltage sequence and a second target time scale factor corresponding to the target voltage sequence; constructing a voltage scale vector according to the second target scale factor and q+1 voltage fluctuation rate sequences corresponding to q adjacent continuous time scale factors of the second target scale factor, q being any positive integer; obtaining a maximum voltage value in the voltage scale vector; and determining the current applicable operating condition of the second-order equivalent circuit model according to a proportional value of each voltage value in the voltage scale vector to the maximum voltage value.

[0016] In some embodiments, determining the current applicable operating condition of the second-order equivalent circuit model according to the proportional value of each voltage value in the voltage scale vector to the maximum voltage value comprises: determining that the test voltage value sample corresponding to the voltage scale vector satisfies the voltage applicable operating condition of the second-order equivalent circuit model when each voltage value in the voltage scale vector is less than or equal to a second scaling coefficient.

[0017] In some embodiments, determining the temperature applicable operating condition of the second-order equivalent circuit model according to the plurality of temperature test values of the battery in the same time period comprises: determining that the temperature test value sample satisfies the temperature applicable operating condition of the second-order equivalent circuit model when the maximum temperature difference between the plurality of temperature test values of the battery in the same time period is less than a preset temperature difference value.

[0018] The embodiment of the present disclosure provides an electronic device, which comprises a memory configured to store a computer program and a processor, wherein the processor implements the calibration method of the second-order equivalent circuit model when executing the computer program.

[0019] The embodiment of the present disclosure provides a calibration device of a second-order equivalent circuit model, which comprises the electronic device.

[0020] The embodiment of the present disclosure provides a vehicle, which comprises the calibration device and a battery, and the calibration device is configured to calibrate the second-order equivalent circuit model of the battery.

[0021] The embodiment of the present disclosure provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by one or more processors, the calibration method is implemented.

[0022] The embodiment of the present disclosure provides a computer program product, which comprises computer programs / instructions, and when the computer programs / instructions are executed by a processor, the calibration method is implemented.

[0023] The disclosure embodiment discloses a calibration method of a second-order equivalent circuit model, an electronic device, a calibration device of a second-order equivalent circuit model, a vehicle, a computer readable storage medium and a computer program product. The calibration method of the second-order equivalent circuit model comprises: obtaining a test data set, the test data set comprising voltage data, current data, temperature data, discharge rate data and SOC data of a battery under a plurality of working conditions, the SOC data comprising an SOC estimated value of the battery cell; determining a plurality of to-be-optimized parameters according to the temperature data, the discharge rate data and the SOC data, to construct a plurality of to-be-optimized models of the second-order equivalent circuit model corresponding to the plurality of to-be-optimized parameters; obtaining a full-condition optimization model, the full-condition optimization model being a second-order equivalent circuit model with a minimum SOC error index, the SOC error index being positively correlated with the difference between an SOC test value and an SOC estimated value determined by each to-be-optimized model under the same battery working condition; determining an applicable working condition of the second-order equivalent circuit model according to the full-condition optimization model, the voltage data, the current data and the temperature data; iteratively optimizing the plurality of to-be-optimized models according to the applicable working condition and the error index, to determine an optimal solution of the to-be-optimized parameters; and calibrating an optimized model of the second-order equivalent circuit model according to the optimal solution of the to-be-optimized parameters.

[0024] The calibration method of the second-order equivalent circuit model provided by the disclosure embodiment considers the case that the second-order equivalent circuit model is not applicable under partial working conditions. By optimizing the model parameters and the model applicable range judgment condition, the model precision and coverage are synergistically optimized, the high precision of the second-order equivalent circuit model within the applicable range is ensured, the model is prevented from being started in the inapplicable range, and the estimation error of the SOC value and the terminal voltage caused by the false correction is avoided.

[0025] Additional aspects and advantages of the disclosure will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0026] The above and / or additional aspects and advantages of the disclosure will become apparent and be readily appreciated from the description of the embodiments, taken in conjunction with the following drawings,

[0027] In which:

[0028] FIG. 1 is a flowchart of a calibration method provided by some embodiments of the disclosure;

[0029] FIG. 2 is a flowchart of a calibration method provided by some embodiments of the disclosure;

[0030] FIG. 3 is a flowchart of a calibration method provided by some embodiments of the disclosure;

[0031] FIG. 4 is a flowchart of a calibration method provided by some embodiments of the disclosure;

[0032] FIG. 5 is a flowchart of a calibration method according to some embodiments of the present disclosure;

[0033] FIG. 6 is a flowchart of a calibration method according to some embodiments of the present disclosure;

[0034] FIG. 7 is a flowchart of a calibration method according to some embodiments of the present disclosure;

[0035] FIG. 8 is an implementation effect diagram of a calibrated second-order equivalent circuit model according to the present disclosure. DETAILED DESCRIPTION

[0036] Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings, examples of which are shown in the drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and cannot be understood as a limitation of the present disclosure.

[0037] The second-order equivalent circuit model is one of the most widely used battery equivalent circuit models, and the terminal voltage of the battery can be estimated based on the second-order equivalent circuit model.

[0038] However, the second-order equivalent circuit model is a highly lumped equivalent model, which cannot fully reflect the electrochemical process inside the battery and cannot be applied to partial working conditions such as large current and low temperature. The parameters of the second-order equivalent circuit model are calibrated in the full working condition range, and the minimum error in the full working condition is taken as the optimization objective. However, due to the existence of working conditions beyond the applicable range of the model, the parameters obtained in some working conditions (for example, the second-order equivalent circuit cannot be applied to large current, low temperature, etc. Partial working conditions) often deviate from the optimal value in the applicable working condition range.

[0039] The second-order equivalent circuit model describes the terminal voltage U t (t) as:

[0040] wherein U ocvFor open-circuit voltage, R0 is used to describe the resistance of ohmic polarization, t can represent the time of battery discharging, I represents the discharging current of the battery, the first-order parameter R1 and τ1 are used to describe the electrochemical polarization, and the second-order parameter R2 and τ2 are used to describe the concentration polarization caused by solid-phase diffusion. The present patent mainly discusses how to determine R2 and τ2 by using an offline calibration method. The second-order parameter R2 and τ2 that need offline calibration are comprehensively affected by the state of charge SOC, the temperature T and the discharge rate C, and have high nonlinearity. The existing scheme sets the model parameters R2 and τ2 in the full working condition range, but does not consider the influence of the state of charge SOC, the temperature T and the discharge rate C on the model parameters, that is, does not consider the existence of working conditions beyond the applicable range of the model, resulting in that the parameters obtained in some working conditions (for example, the second-order equivalent circuit cannot be applied to some working conditions such as large current and low temperature) often deviate from the optimal value in the applicable working condition range.

[0041] To solve the above technical problems, the present disclosure provides a calibration method of a second-order equivalent circuit model. Compared with the existing method of optimizing parameters in the full working condition range with the error minimization as the target, the calibration method of the second-order equivalent circuit model provided by the present disclosure considers the case that the second-order equivalent circuit model is not applicable in some working conditions. By optimizing the model parameters and the model applicable range judgment condition, the collaborative optimization of model precision and coverage is realized, the high precision in the applicable range of the second-order equivalent circuit model is ensured, the model in the inapplicable range is avoided to start, and the estimation error of the SOC value (battery charge state) and the terminal voltage caused by the false correction is avoided.

[0042] The present disclosure also provides an electronic device including a memory configured to store a computer program and a processor that, when executing the computer program, implements the calibration method of the second-order equivalent circuit model provided by the present disclosure.

[0043] The present disclosure provides a calibration device of a second-order equivalent circuit model, and the calibration device provided by the present disclosure can include the electronic device provided by the present disclosure to implement the calibration method of the second-order equivalent circuit model provided by the present disclosure.

[0044] Referring to FIG. 1, the calibration method of the second-order equivalent circuit model provided by the present disclosure includes:

[0045] Step 01: Obtain a test data set, the test data set includes voltage data, current data, temperature data, discharge rate data and SOC data of the battery in a plurality of working conditions, and the SOC data includes an estimated SOC value of the battery cell;

[0046] Step 02: determining a plurality of to-be-optimized parameters according to the temperature data, the discharge rate data, and the SOC data, so as to construct a plurality of to-be-optimized models of the second-order equivalent circuit model corresponding to the plurality of to-be-optimized parameters;

[0047] Step 03: obtaining a full-condition optimization model, the full-condition optimization model being a second-order equivalent circuit model with minimized SOC error index, the SOC error index being positively correlated with the difference between the SOC test value and the SOC estimation value determined by each to-be-optimized model under the same battery condition;

[0048] Step 04: determining the applicable condition of the second-order equivalent circuit model according to the full-condition optimization model, the voltage data, the current data, and the temperature data;

[0049] Step 05: iteratively optimizing the plurality of to-be-optimized models according to the applicable condition and the error index, so as to determine the optimal solution of the to-be-optimized parameters;

[0050] Step 06: calibrating the optimized model of the second-order equivalent circuit model according to the optimal solution of the to-be-optimized parameters.

[0051] In some embodiments, the electronic device and the calibration device provided by the embodiments of the present disclosure can execute the computer program of "obtaining a test data set, the test data set including voltage data, current data, temperature data, discharge rate data, and SOC data of a battery under a plurality of conditions, the SOC data including an SOC test value of the battery; determining a plurality of to-be-optimized parameters according to the temperature data, the discharge rate data, and the SOC data, so as to construct a plurality of to-be-optimized models of the second-order equivalent circuit model; determining the applicable condition of the to-be-optimized model according to the full-condition optimization model, the voltage data, the current data, and the temperature data; and determining the optimized model from the plurality of to-be-optimized models according to the applicable condition and the error index".

[0052] Specifically, in step 01, the test data set can include various test data of the battery under a plurality of conditions, including voltage data, current data, temperature data, discharge rate data, and SOC data of the battery, the SOC data including an SOC estimation value of the battery.

[0053] In some embodiments of step 01, a corresponding sampling point (including a current sampling point, a voltage sampling point, and a temperature sampling point) can be arranged on the battery, a plurality of temperature conditions can be adopted to carry out a cycle experiment of the battery pack, and the temperature data, the voltage data, and the current data of each battery at different time points can be obtained through the sampling points, so as to determine the temperature, voltage, and current sequence of each battery.

[0054] According to the temperature data, the voltage data, and the current data of the battery at different time points obtained by sampling, the SOC estimation value of the battery can be calculated and determined.

[0055] In some embodiments of step 01, the ampere-hour integration method can be used to calculate the SOC estimation value of the battery cell at different times according to the temperature data, voltage data and current data of the battery cell obtained by sampling at different times.

[0056] In some embodiments of step 01, the battery pack can be subjected to cycle simulation experiments of multiple working condition types at environmental temperatures of -20°C, 0°C, 25°C and 40°C, respectively. Real measurement experiments of high-speed and urban working conditions are carried out at temperatures of -20°C, 0°C, 25°C and 40°C. The temperature, voltage and current sequences of each battery cell are recorded at a sampling frequency of 1 Hz or higher. The SOC value corresponding to each sampling point is obtained by using the ampere-hour integration method, and it is necessary to ensure that each working condition covers the SOC interval of 85% to 5%.

[0057] There are 4 environmental temperatures and 5 working condition types, and 2 groups of data can be obtained under the same environmental temperature and working condition type, a total of 40 groups of test data can be obtained.

[0058] In step 02, the variation law of the internal resistance parameter of the second-order equivalent circuit model with respect to SOC, temperature T and discharge rate C can be converted into a series of optimization parameters by using a corresponding modeling method, and then the optimization model corresponding to the optimization parameters is determined.

[0059] In some embodiments of step 02, the internal resistance parameter modeling method such as surface fitting or table interpolation can be used, for example, a three-dimensional interpolation table of the second-order parameters R2 and τ2 with respect to SOC, temperature T and discharge rate C is constructed according to the corresponding relationship of the second-order parameters R2 and τ2 with respect to SOC, temperature T and discharge rate C. The optimization parameters are the values of the second-order parameters R2 and τ2 corresponding to different values of SOC, temperature T and discharge rate C. The upper limit value and the lower limit value of the optimization parameters are determined according to a certain scaling ratio on the basis of the empirical value.

[0060] In some embodiments of step 02, taking the case of 4 groups of SOC, 5 groups of temperature T and 2 groups of discharge rate C, the optimization of the second-order parameters R2 is 40, and the optimization of the second-order parameters τ2 is 40. The initial value of the second-order parameters R2 and τ2 is 10 times the upper limit and 0.1 times the lower limit on the basis of the empirical value.

[0061] In step 03, the full-working-condition optimization model can be obtained, which is a second-order equivalent circuit model with the minimum SOC error index, and the value of the second-order parameter corresponding to the full-working-condition optimization model only considers the minimization of the SOC error index.

[0062] In some embodiments of step 03, the SOC error index can be determined according to the fitness function, and the fitness function corresponding to the SOC error index can be defined as

[0063] wherein, soc(i) is the SOC value of the ith sample point in the test data set obtained by ampere-hour integration, is the SOC estimation value of the ith sample point estimated by the corresponding to-be-optimized model under the given to-be-optimized parameter, and N is the number of samples used for offline parameter estimation. The max(.) term represents the maximum value in the sequence.

[0064] The SOC error index can be the value of the error index Fitness1 determined according to formula (2), and the value of Fitness1 is positively correlated with Minimizing Fitness1 is the goal of iterative optimization, and the optimal second-order parameter value under all working conditions is obtained, and then the optimal parameter value under all working conditions is determined to correspond to the all-working-condition optimization model.

[0065] In some embodiments of step 03, a heuristic optimization algorithm can be selected, and iterative optimization is performed with the goal of minimizing Fitness1 by setting heuristic algorithm parameters to obtain the current optimal parameter value. Taking the Grey Wolf Optimizer (GWO) algorithm as an example, the population size is set to 30, and the number of iterations is set to 500.

[0066] In step 04, the battery needs to work in a suitable current, voltage and temperature range, and the current, voltage and temperature range corresponding to part of the working conditions cannot ensure that the battery can work normally, so the current data, voltage data and temperature data need to meet the limit conditions for the battery to work normally.

[0067] According to the all-working-condition optimization model of the second-order equivalent circuit model and each test data in the test data set, the change rule of the battery estimation data obtained by the all-working-condition optimization model and the test data can be determined, and then according to the limit conditions that the test data needs to meet, the applicable working condition of the second-order equivalent circuit model can be determined.

[0068] It can be understood that the applicable working condition of the second-order equivalent circuit model needs to correspond to the limit condition that the test data needs to meet, that is, under the applicable working condition of the second-order equivalent circuit model, the battery works in a suitable range and ensures that the battery can work normally.

[0069] In step 05, while ensuring the applicable working condition of the second-order equivalent circuit model, it is also necessary to minimize the SOC error index as much as possible, that is, to realize the collaborative optimization of model accuracy and coverage.

[0070] In some embodiments of step 05, a heuristic optimization algorithm can be selected to simultaneously satisfy the applicable operating condition constraints and the SOC error index by setting the heuristic algorithm parameters to iteratively optimize the optimal solution of the to-be-optimized parameters, i.e., the optimal values of the second-order parameters R2 and τ2.

[0071] In step 06, the optimal values of the second-order parameters R2 and τ2 are brought into formula (1) to calibrate the optimal model of the second-order equivalent circuit model.

[0072] It can be understood that, unlike the existing calibration method in the full operating condition range, the calibration method of the embodiments of the present disclosure considers the case where the second-order equivalent circuit model is not applicable in partial operating conditions. By optimizing the model parameters and the model applicable range judgment conditions, the model precision and coverage are synergistically optimized. The high precision in the applicable range of the second-order equivalent circuit model is ensured, and the large SOC estimation error caused by the model starting in the inapplicable range is avoided.

[0073] Referring to FIG. 2, in some embodiments, step 05: iteratively optimizing a plurality of to-be-optimized models according to the applicable operating condition and the error index to determine the optimal solution of the to-be-optimized parameters, comprises:

[0074] Step 051: determining the sample proportion value of the number of samples that do not match the applicable operating condition to the total number of samples in the test data set to determine the deactivation rate index, the deactivation rate index is positively correlated with the sample proportion value;

[0075] Step 052: iteratively optimizing a plurality of to-be-optimized models to determine the optimal solution of the to-be-optimized parameters with the minimization of the synergy index as the target, the synergy index is positively correlated with the SOC error index, and the synergy index is positively correlated with the deactivation rate index.

[0076] Specifically, in step 051, each to-be-optimized model has a corresponding applicable operating condition, and the applicable operating condition corresponds to the limit conditions of the cell operating current, voltage and temperature.

[0077] In the test data, the samples that do not satisfy the limit conditions of the cell operating current, voltage and temperature corresponding to the applicable operating condition are the samples that do not match the applicable operating condition. The total number of samples for calibrating the second-order equivalent circuit model can be set as N, and the number of samples that do not match the applicable operating condition can be set as N_deactive. The deactivation rate index can be set as N_deactive / N.

[0078] In step 052, a fitness function corresponding to the synergy index is established. While considering the applicable range of the to-be-optimized model, it is also necessary to reduce the model error as much as possible, that is, to realize the collaborative optimization of model accuracy and coverage. Therefore, the synergy index is positively correlated with the SOC error index and the deactivation rate index. The fitness function corresponding to the newly established synergy index can be defined as the weighted sum of the error term and the deactivation rate index:

[0079] Fitness = Fitness1 + weight * N_deactive / N …… (3)

[0080] wherein Fitness1 is the SOC error index determined according to formula (2), and weight is a weight value for balancing the final model accuracy and model coverage.

[0081] The value of the synergy index Fitness determined according to formula (3) is iteratively optimized with the goal of minimizing Fitness to determine the optimal solution of the to-be-optimized parameters, that is, the optimal values of the second-order parameters R2 and τ2.

[0082] Referring to FIG. 3, in some embodiments, step 052: iteratively optimizing a plurality of to-be-optimized models with the goal of minimizing the synergy index to determine the optimal solution of the to-be-optimized parameters, comprises:

[0083] Step 0521: iteratively optimizing a plurality of to-be-optimized models with the goal of minimizing the synergy index to determine the optimal solution of the to-be-optimized parameters, under the condition that the SOC error index is lower than a first value and the deactivation rate index is lower than a second value.

[0084] Specifically, in step 0521, the first value can be an upper limit value of the error index, and the second value can be an upper limit value of the deactivation rate index. The upper limit value Threshold1 of the SOC error index and the upper limit value Threshold2 of the deactivation rate index are determined according to actual needs.

[0085] The value of the error index Fitness1 determined according to formula (3) is lower than Threshold1, and at the same time, it is also necessary to ensure that N_deactive / N is lower than Threshold2.

[0086] Referring to FIG. 4, in some embodiments, step 052: determining an optimal model from a plurality of to-be-optimized models with the goal of minimizing the synergy index, comprises:

[0087] Step 0522: obtaining a fitness function model corresponding to the synergy index, the fitness function model being configured to determine the synergy index according to the weighted sum value of the SOC error and the deactivation rate index;

[0088] Step 0523: Obtain a plurality of to-be-optimized weight values to construct a to-be-optimized weight value model of a plurality of fitness function models corresponding to the plurality of to-be-optimized weight values;

[0089] Step 0524: In the case of ensuring that the SOC error index is lower than the first value and the deactivation rate index is lower than the second value, taking the minimization of the product of the values of the SOC error index and the deactivation rate index as the goal, iteratively optimizing the plurality of target models to determine the optimal solution of the to-be-optimized weight values;

[0090] Step 0525: According to the optimal model of the fitness function model corresponding to the optimal solution of the to-be-optimized weight values, taking the minimization of the synergy index as the goal, iteratively optimizing the plurality of to-be-optimized models to determine the optimal solution of the to-be-optimized parameters.

[0091] Specifically, in step 0522, the fitness function model corresponding to the synergy index can refer to formula (3). In formula (3), the value of the synergy index Fitness is the weighted sum of the value of the SOC error index Fitness1 and the value of the deactivation rate index N_deactive / N, and weight is the weight value. The value of weight is different, the fitness function model corresponding to the synergy index is different.

[0092] In step 0523, W given to-be-optimized weight values can be obtained, denoted as weight∈{weight i ,i=1,2,…W}. The W given to-be-optimized weight values are brought into formula (3) to obtain the to-be-optimized weight value model of the W given fitness function models.

[0093] In step 0524, a heuristic optimization algorithm can be selected, heuristic algorithm parameters can be set, a plurality of to-be-optimized weight values weight∈{weight i ,i=1,2,…W} values are given, and iterative optimization is sequentially performed.

[0094] The error index Fitness1 and the model deactivation rate index N_deactive / N under different weights are obtained. The upper limit value Threshold1 of the error index and the upper limit value Threshold2 of the model deactivation rate index are determined according to actual requirements, and the minimization of the product of the error index Fitness1 and the model deactivation rate index N_deactive / N is taken as the goal, and iterative optimization is performed. Finally, the optimal solution of the to-be-optimized weight values is determined by the following formula (4): weight=argmin(Fitness1·N_deactive / N)

[0095] In step 0525, the optimal solution of the to-be-optimized weight value weight is brought into formula (3), that is, the optimal model of the fitness function model can be obtained. According to the optimal model of the fitness function model, the minimization of the synergy index is taken as the goal, and the multiple to-be-optimized models are iteratively optimized to determine the optimal solution of the to-be-optimized parameters.

[0096] In some embodiments, weight∈{10 20 30 40 50 60 70 80 90 100 120 150 180 200}, the error index upper limit value Threshold1=6, and the model shutdown rate index upper limit value Threshold2=0.5.

[0097] Referring to FIG. 5, in certain embodiments, the voltage data includes multiple voltage test values of the battery at different time periods, the current data includes multiple current test values of the battery at different time periods, and the temperature data includes multiple temperature test values of the battery at the same time. In step 04, the applicable working condition of the second-order equivalent model is determined according to the full-working-condition optimization model, the voltage data, the current data, and the temperature data, including:

[0098] In step 041, multiple voltage difference values between the voltage test values and the voltage estimated values determined by the full-working-condition optimization model at different time periods are obtained under the condition that the battery working condition is the same, to determine a modeling error sequence.

[0099] In step 0421, multiple average current sequences under multiple time scales are determined according to the multiple current test values of the battery at different time periods.

[0100] In step 0422, the current applicable working condition of the second-order equivalent model is determined according to the multiple average current sequences and the modeling error sequence.

[0101] In step 0431, multiple voltage fluctuation rate sequences under multiple time scales are determined according to the multiple voltage test values of the battery at different time periods.

[0102] In step 0432, the voltage applicable working condition of the second-order equivalent model is determined according to the multiple voltage fluctuation rate sequences and the modeling error sequence.

[0103] In step 044, the temperature applicable working condition of the second-order equivalent model is determined according to the multiple temperature test values of the battery at the same time.

[0104] Specifically, in step 041, the modeling error sequence can be determined according to the full-working-condition optimization model, and e(t) is calculated according to the following formula:

[0105] wherein v(t) can represent the voltage of the discrete voltage sampling point in the test data set, The end voltage determined by the full-condition optimization model can be expressed as

[0106] In step 0421, the average current is calculated for the discrete current sampling points i(t) in the test data set by the following formula

[0107] wherein a is a time scale factor, The initial value of is 0, that is, By selecting multiple time scale factors, the corresponding average current sequences under multiple time scales can be obtained

[0108] In step 0422, according to the corresponding relationship between the average current sequence and the modeling error sequence e(t), and according to the restriction condition that the working current of the battery cell needs to meet, the current applicable working condition condition of the second-order equivalent circuit model can be determined.

[0109] In step 0431, for the discrete voltage sampling points v(t), the corresponding average voltage is

[0110] wherein a is a time scale factor, The initial value of is 3200, that is, The voltage fluctuation rate is described by the following formula (9)

[0111] By selecting multiple time scale factors, the corresponding voltage fluctuation rate sequences variance under multiple time scales can be obtained k ={variance α (t)),t=1,2,…N,α=α k}……(10)

[0112] In step 0432, according to the corresponding relationship between the voltage fluctuation rate sequence and the modeling error sequence e(t), and according to the restriction condition that the working voltage of the battery cell needs to meet, the voltage applicable working condition condition of the second-order equivalent circuit model can be determined.

[0113] In step 044, the battery cell can be provided with multiple temperature sampling points, and the multiple temperature sampling points can collect the temperature of the battery cell at the same time. According to the multiple temperature test values of the battery cell at the same time, the temperature restriction condition that the battery cell needs to meet can be determined, and the temperature applicable working condition condition of the second-order equivalent model can be determined.

[0114] Referring to FIG. 6, in some embodiments, step 0422: determining the current applicable operating condition of the second-order equivalent model according to the plurality of average current sequences and the modeling error sequence, comprises:

[0115] Step 04221: sequentially calculating the correlation coefficients of the plurality of average current sequences and the modeling error sequence;

[0116] Step 04222: sorting the time scale factors corresponding to the average current sequences from small to large, and selecting the average current sequence corresponding to the maximum correlation coefficient to determine the target current sequence and the first target time scale factor corresponding to the target current sequence;

[0117] Step 04223: constructing a current scale vector according to the first target scale factor and the p+1 average current sequences corresponding to the adjacent continuous p time scale factors of the first target scale factor, p being any positive integer;

[0118] Step 04224: obtaining the maximum current value in the current scale vector;

[0119] Step 04225: determining the current applicable operating condition of the second-order equivalent model according to the ratio of each current value in the current scale vector to the maximum current value.

[0120] Specifically, in step 04221, the Pearson correlation coefficients of the average current sequences and the terminal voltage modeling error sequence e(t) are sequentially calculated.

[0121] In step 04222, the time scale factors a are sorted from small to large according to the scale factor a, and the time scale factor a corresponding to the maximum Pearson correlation coefficient is selected m , and is recorded as the first target time scale factor, and the average current sequence corresponding to the time scale factor a m is the target current sequence.

[0122] In step 04223, the p+1 average current sequences corresponding to the time scale factor a m and the adjacent continuous p time scale factors are selected to construct a current scale vector.

[0123] In some embodiments, p can be 2n, n being a positive integer, the n scale factors on both sides of the time scale factor a m and the time scale factor a m can be selected to form a current scale vector a I = [a m-n , …, a m-1 , a m , a m+1 , …, a m+n ] used for model applicable range determination.

[0124] In step 04224, a second-order model based on a multi-scale average current range determination condition is constructed, and the maximum value of the average current sequence under each current scale is obtained in sequence

[0125] Wherein, α∈α I .

[0126] In step 04225, And The ratio value needs to take a suitable value to ensure the stability of the current value size.

[0127] In some embodiments, step 4225: determining the current application condition of the second-order equivalent model according to the ratio of each value in the current scale vector to the maximum value, comprising:

[0128] In the case where the ratio of each current value in the current scale vector to the maximum current value is less than or equal to the first scaling coefficient, it is determined that the test current value sample corresponding to the current scale vector satisfies the current application condition of the second-order equivalent model.

[0129] Specifically, the current application condition needs to satisfy that the ratio of each current value in the current scale vector to the maximum current value is less than the first scaling coefficient k α , satisfying formula (12) can satisfy the current application condition of the second-order equivalent model.

[0130] In some embodiments, the time scale factor set is α∈{0.99, 0.995, 0.999, 0.9995, 0.9999, 0.99995}.

[0131] In some embodiments, n=1 is taken, then α I =[α m-1 ,α m ,α m+1 ].

[0132] In some embodiments, k α is a 3-dimensional vector, and the element value is a random number between [0, 1]. When performing the above step 052, the value of k α can be adjusted to realize the minimization of the coordination index.

[0133] Referring to FIG. 7, in some embodiments, step 0432: determining the voltage application condition of the second-order equivalent model according to the plurality of voltage fluctuation rate sequences and the modeling error sequence, comprising:

[0134] Step 04321: sequentially calculating the correlation coefficients of the plurality of voltage fluctuation rate sequences and the modeling error sequence;

[0135] Step 04322: Sort the time scale factors corresponding to the voltage fluctuation rate sequences from small to large, select the voltage fluctuation rate sequence corresponding to the maximum correlation coefficient to determine the target voltage sequence and the second target time scale factor corresponding to the target voltage sequence;

[0136] Step 04323: Construct a voltage scale vector according to the second target scale factor and the q+1 voltage fluctuation rate sequences corresponding to the adjacent continuous q time scale factors of the second target scale factor, q is any positive integer;

[0137] Step 04324: Obtain the maximum voltage value in the voltage scale vector;

[0138] Step 04325: Determine the current applicable operating condition of the second-order equivalent model according to the ratio of each voltage value in the voltage scale vector to the maximum voltage value.

[0139] Specifically, in step 04321, the Pearson correlation coefficient between the voltage fluctuation rate sequence variance k and the terminal voltage modeling error sequence e(t) is calculated in turn.

[0140] In step 04322, the time scale factors a are sorted from small to large, and the time scale factor a corresponding to the maximum Pearson correlation coefficient is selected o , and is recorded as the second target time scale factor, and the voltage fluctuation rate sequence corresponding to the time scale factor a o is the target voltage sequence.

[0141] In step 04323, q+1 voltage fluctuation rate sequences corresponding to the time scale factor a o and the adjacent continuous q time scale factors are selected to construct a voltage scale vector.

[0142] In some embodiments, q can be 2r, r is a positive integer, the time scale factor a o and the time scale factor a o r scale factors on both sides can be selected to form a voltage fluctuation rate scale vector a V =[a o-r ,…a o , a o+1 ,…,a o+r ].

[0143] In step 04324, the maximum value of each voltage fluctuation rate under each scale is obtained in turn to construct the second-order model applicable range discrimination condition based on multi-scale voltage fluctuation rate

[0144] wherein a∈a V .

[0145] In step 04325, With variance β The proportional value of (t) needs to be chosen appropriately to ensure the stability of voltage fluctuation rate.

[0146] In some implementations, step 04325: determining the applicable current conditions for the second-order equivalent model based on the ratio of each voltage value in the voltage scale vector to the maximum voltage value, including:

[0147] If the ratio of each voltage value in the voltage scale vector to the maximum voltage value is less than or equal to the second scaling factor, it is determined that the test voltage value sample corresponding to the voltage scale vector satisfies the voltage applicable operating conditions of the second-order equivalent model.

[0148] Specifically, the applicable voltage conditions must satisfy the requirement that the ratio of each voltage value to the maximum voltage value in the voltage scale vector is less than the second scaling factor k. β If formula (14) is satisfied, the voltage applicable operating conditions of the second-order equivalent model can be met.

[0149] In some implementations, step 044: determining the applicable temperature conditions for the second-order equivalent model based on multiple temperature test values ​​of the battery cell during the same time period, including:

[0150] If the maximum temperature difference between multiple temperature test values ​​of the battery cell at the same time period is lower than the preset temperature difference, it is determined that the temperature measurement value sample meets the temperature applicable operating conditions of the second-order equivalent model.

[0151] In some embodiments, when r = 1, the working condition constraint scale vector α V =[α o-1 ,α o ,α o+1 ].

[0152] In some embodiments, k β It is a 3-dimensional vector whose elements are random numbers between [0, 1]. When performing step 052 above, k can be adjusted... β The value of is determined by minimizing the collaborative index.

[0153] In some implementations, step 044: determining the applicable temperature conditions for the second-order equivalent model based on multiple temperature test values ​​of the battery cell at the same time, including:

[0154] Specifically, in step 044, if the maximum temperature difference between multiple temperature test values ​​of the battery cell at the same time is less than a certain value, then the temperature applicable operating conditions of the model to be optimized are determined to be met.

[0155] temperature sampling data T of different time t m (t), m = 1, 2, … M, wherein M is the number of temperature sampling points on the battery cell. The temperature difference sequence Delta_T(t) is obtained according to the following formula: Delta_T(t) = max(T m (t) - T n (t)) …… (15)

[0156] wherein m, n e {1, 2, … M}. The model based on the internal temperature difference is applicable to the discrimination condition: Delta_T(t) <= Delta_T0 …… (16)

[0157] wherein Delta_T0 is a preset temperature difference.

[0158] In some embodiments, the value range of Delta_T0 is [0, 20]. When performing the above step 052, the value of Delta_T0 can be adjusted to achieve the minimization of the coordination index.

[0159] In step 04, the applicable range of the to-be-optimized model needs to satisfy the applicable working condition conditions of formula (12), formula (14) and formula (16) at the same time, that is,

[0160] In some embodiments, the time coefficient τ2 and the resistance coefficient R2 of the second-order parameter, and the upper limit value of the time coefficient τ2 and the resistance coefficient R2 of the node in the to-be-optimized parameter are 10 times of the empirical value, and the lower limit value is 0.1 times of the empirical value.

[0161] The implementation effect of the calibration method provided by the embodiment of the present disclosure is shown in FIG. 8. As can be known from FIG. 8, the optimized model determined by calibration can guarantee the SOC estimation accuracy of the model, and under the corresponding model coverage, the model convergence with the initial error can be guaranteed.

[0162] The embodiment of the present disclosure also provides a vehicle, which can include the calibration device and the battery of the above-mentioned embodiment, and the calibration device is configured to calibrate the second-order equivalent circuit model of the battery, so that the determined SOC value and the terminal voltage value of the battery are more accurate.

[0163] The embodiment of the present disclosure provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by one or more processors, the above-mentioned calibration method is realized.

[0164] The embodiment of the present disclosure provides a computer program product, which includes computer programs / instructions, and when the computer programs / instructions are executed by a processor, the above-mentioned calibration method is realized.

[0165] The technical effects of the computer-readable storage medium and the computer program product provided by the embodiments of the present disclosure include all the technical effects of the calibration method of the embodiments of the present disclosure, which are not described here.

[0166] In the description of the specification, the description referring to the terms "certain embodiments", "one example", "exemplarily" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present disclosure. In the specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0167] Any process or method descriptions in flow charts or described herein in other ways can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for performing specific logic functions or steps in the process, and the various embodiments of the present disclosure include additional implementations in which the order of steps can be changed, including use of an alternate order, additional or fewer steps performed, or performed in parallel, depending on the functionality involved, as will be understood by those skilled in the art.

[0168] Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above-described embodiments are optional and cannot be understood as a limitation of the present disclosure.

Claims

1. A method of calibrating a second order equivalent circuit model, wherein, The method comprises: acquiring a test data set, the test data set comprising voltage data, current data, temperature data, discharge rate data and SOC data of a battery cell under various working conditions, the SOC data comprising an SOC estimation value of the battery cell; determining a plurality of to-be-optimized parameters according to the temperature data, the discharge rate data and the SOC data, to construct a to-be-optimized model of a plurality of second-order equivalent circuit models corresponding to the plurality of to-be-optimized parameters; acquiring a full-condition optimization model, the full-condition optimization model being a second-order equivalent circuit model with minimized SOC error index, the SOC error index being positively correlated with "a difference between the SOC test value and the SOC estimation value determined by each to-be-optimized model under the same battery working condition"; determining applicable working condition conditions of the second-order equivalent circuit model according to the full-condition optimization model, the voltage data, the current data and the temperature data; iteratively optimizing a plurality of to-be-optimized models according to the applicable working condition conditions and the error index, to determine an optimal solution of the to-be-optimized parameters; calibrating an optimized model of the second-order equivalent circuit model according to the optimal solution of the to-be-optimized parameters.

2. The calibration method of claim 1, wherein, The iteratively optimizing a plurality of to-be-optimized models according to the applicable working condition conditions and the error index, to determine an optimal solution of the to-be-optimized parameters, comprises: determining a sample proportion value of a total sample quantity and a sample quantity that does not match the applicable working condition conditions in the test data set, to determine a deactivation rate index, the deactivation rate index being positively correlated with the sample proportion value; iteratively optimizing a plurality of to-be-optimized models to determine an optimal solution of the to-be-optimized parameters, with minimization of a synergy index as a target, the synergy index being positively correlated with the SOC error index and the deactivation rate index.

3. The calibration method of claim 2, wherein, The iteratively optimizing a plurality of to-be-optimized models to determine an optimal solution of the to-be-optimized parameters, with minimization of a synergy index as a target, comprises: iteratively optimizing a plurality of to-be-optimized models to determine an optimal solution of the to-be-optimized parameters, with minimization of a synergy index as a target, under the condition that the SOC error index is lower than a first value and the deactivation rate index is lower than a second value.

4. The calibration method according to claim 2 or 3, wherein, The iteratively optimizing a plurality of to-be-optimized models to determine an optimal solution of the to-be-optimized parameters, with minimization of a synergy index as a target, comprises: acquiring an adaptive function model corresponding to the synergy index, the adaptive function model being configured to determine the synergy index according to a weighted sum value of the SOC error and the deactivation rate index; acquiring a plurality of to-be-optimized weight values, to construct a to-be-optimized weight value model of a plurality of adaptive function models corresponding to the plurality of to-be-optimized weight values; iteratively optimizing a plurality of target models to determine an optimal solution of the to-be-optimized weight values, with minimization of a value product of the SOC error index and the deactivation rate index as a target, under the condition that the SOC error index is lower than a first value and the deactivation rate index is lower than a second value; According to the optimal model of the fitness function model corresponding to the optimal solution of the to-be-optimized weight value, taking minimization of the collaborative index as a target, a plurality of to-be-optimized models are iteratively optimized to determine the optimal solution of the to-be-optimized parameter.

5. The calibration method of any one of claims 1-4, wherein, The voltage data includes a plurality of voltage test values of the battery at different time periods, the current data includes a plurality of current test values of the battery at different time periods, the temperature data includes a plurality of temperature test values of the battery at the same time, and the applicable working condition of the second-order equivalent circuit model is determined according to the full-working-condition optimization model, the voltage data, the current data and the temperature data, including: In the case of the same battery working condition, a plurality of voltage difference values of the voltage test value and the voltage estimation value determined by the full-working-condition optimization model at different time periods are obtained to determine a modeling error sequence; According to a plurality of current test values of the battery at different time periods, an average current sequence under a plurality of time scales is determined; According to a plurality of the average current sequence and the modeling error sequence, a current applicable working condition of the second-order equivalent circuit model is determined; According to a plurality of voltage test values of the battery at different time periods, a voltage fluctuation rate sequence under a plurality of time scales is determined; According to a plurality of the voltage fluctuation rate sequence and the modeling error sequence, a voltage applicable working condition of the second-order equivalent circuit model is determined; According to a plurality of temperature test values of the battery at the same time, a temperature applicable working condition of the second-order equivalent circuit model is determined.

6. The calibration method of claim 5, wherein, The current applicable working condition of the second-order equivalent circuit model is determined according to a plurality of the average current sequence and the modeling error sequence, including: The correlation coefficients of a plurality of the average current sequence and the modeling error sequence are calculated in sequence; The time scale factors corresponding to the average current sequence are sorted from small to large, and the average current sequence corresponding to the maximum correlation coefficient is selected to determine a target current sequence and a first target time scale factor corresponding to the target current sequence; A current scale vector is constructed according to the first target scale factor and p+1 average current sequences corresponding to the first target scale factor and adjacent continuous p time scale factors, p being any positive integer; The maximum current value in the current scale vector is obtained; The current applicable working condition of the second-order equivalent circuit model is determined according to the proportion of each current value in the current scale vector to the maximum current value.

7. The calibration method of claim 6, wherein, The current applicable working condition of the second-order equivalent circuit model is determined according to the proportion of each current value in the current scale vector to the maximum value, including: In the case that the proportion of each current value in the current scale vector to the maximum current value is less than or equal to a first scaling coefficient, it is determined that the test current value sample corresponding to the current scale vector satisfies the current applicable working condition of the second-order equivalent circuit model.

8. The calibration method of claim 5, wherein, The voltage applicable working condition of the second-order equivalent circuit model is determined according to a plurality of the voltage fluctuation rate sequence and the modeling error sequence, including: The correlation coefficients of a plurality of the voltage fluctuation rate sequence and the modeling error sequence are calculated in sequence; The time scale factors corresponding to the voltage fluctuation rate sequence are sorted from small to large, and the voltage fluctuation rate sequence corresponding to the maximum correlation coefficient is selected to determine a target voltage sequence and a second target time scale factor corresponding to the target voltage sequence; A voltage scale vector is constructed according to the second target scale factor and q+1 voltage fluctuation rate sequences corresponding to the adjacent continuous q time scale factors of the second target scale factor, q being any positive integer; A maximum voltage value in the voltage scale vector is obtained; A current applicable working condition of the second-order equivalent circuit model is determined according to a proportional value of each voltage value in the voltage scale vector to the maximum voltage value.

9. The calibration method of claim 8, wherein, The determination of the current applicable working condition of the second-order equivalent circuit model according to the proportional value of each voltage value in the voltage scale vector to the maximum voltage value includes: In a case where the proportional value of each voltage value in the voltage scale vector to the maximum voltage value is less than or equal to a second scaling coefficient, it is determined that a test voltage value sample corresponding to the voltage scale vector satisfies a voltage applicable working condition of the second-order equivalent circuit model.

10. The calibration method of claim 5, wherein, The determination of the temperature applicable working condition of the second-order equivalent circuit model according to the multiple temperature test values of the battery cell in the same period includes: In a case where a maximum temperature difference between the multiple temperature test values of the battery cell in the same period is lower than a preset temperature difference value, it is determined that the temperature test value sample satisfies the temperature applicable working condition of the second-order equivalent circuit model.

11. An electronic device, wherein, The electronic device includes a memory configured to store a computer program and a processor, and the processor, when executing the computer program, implements the calibration method of the second-order equivalent circuit model according to any one of claims 1-10.

12. A calibration device of a second order equivalent circuit model, wherein, The calibration device includes the electronic device according to claim 11.

13. A vehicle, wherein, The vehicle includes the calibration device according to claim 12 and a battery, and the calibration device is configured to calibrate a second-order equivalent circuit model of the battery.

14. A computer readable storage medium, wherein, The computer readable storage medium stores a computer program, and when the computer program is executed by one or more processors, the calibration method according to any one of claims 1-10 is implemented.

15. A computer program product comprising computer programs / instructions, wherein, The computer program / instruction is executed by the processor to implement the calibration method according to any one of claims 1-10.

Images