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

Abstract

The invention discloses a calibration method of a second-order equivalent circuit model, an electronic device, calibration equipment of the second-order equivalent circuit model, a vehicle, a computer readable storage medium and a computer program product. The calibration method comprises the following steps: acquiring a test data set; determining a plurality of to-be-optimized parameters according to the temperature data, the discharge rate data and the SOC data; obtaining an all-condition optimization model; determining an applicable working condition according to the all-working-condition optimization model, the voltage data, the current data and the temperature data; and performing iterative optimization on the plurality of models to be optimized according to the applicable working conditions and the error indexes to determine an optimal solution of the parameters to be optimized so as to calibrate an optimal model of the second-order equivalent circuit model. According to the calibration method provided by the invention, the condition that the second-order equivalent current model is not applicable in part of working conditions is considered, collaborative optimization of model precision and coverage is realized, high precision of the second-order equivalent circuit model in an application range is ensured, and the model is prevented from being started in a non-application range.

Description

Technical Field

[0001] The present application relates to the technical field of model calibration, and more specifically, to a calibration method for a second-order equivalent circuit model, an electronic device, a calibration device for a second-order equivalent circuit model, a vehicle, a computer-readable storage medium, and a computer program product. Background Art

[0002] The second-order equivalent circuit model is one of the most widely used battery equivalent circuit models. The terminal voltage of the battery can be estimated based on the second-order equivalent circuit model.

[0003] However, the second-order equivalent circuit model is a highly lumped equivalent model that cannot fully reflect the electrochemical process inside the battery and cannot be applied to some operating conditions such as high current and low temperature. In related technologies, the parameters of the second-order equivalent circuit model are adjusted within the full range of operating conditions, with the minimum error of all operating conditions as the optimization goal. However, due to the existence of operating conditions beyond the applicable range of the model, the parameters obtained for some operating conditions (for example, the second-order equivalent circuit cannot be applied to some operating conditions such as high current and low temperature) often deviate from the optimal values ​​within the applicable range. Summary of the invention

[0004] Embodiments of the present application provide a calibration method for a second-order equivalent circuit model, an electronic device, a calibration device for a second-order equivalent circuit model, a vehicle, a computer-readable storage medium, and a computer program product.

[0005] The calibration method of the second-order equivalent circuit model provided in the embodiment of the present application includes: obtaining a test data set, the test data set includes voltage data, current data, temperature data, discharge rate data and SOC data of the battery cell under various operating conditions, and the SOC data includes the SOC estimation value of the battery cell; determining multiple parameters to be optimized based on the temperature data, discharge rate data and SOC data to construct a model to be optimized of multiple second-order equivalent circuit models corresponding to the multiple parameters to be optimized; obtaining a full-operating condition optimization model, the full-operating condition optimization model is a second-order equivalent circuit model that minimizes the SOC error index, and the SOC error index is positively correlated with "the difference between the SOC test value and the SOC estimation value determined by each model to be optimized under the same battery operating condition"; determining the applicable operating conditions of the second-order equivalent circuit model based on the full-operating condition optimization model, the voltage data, the current data and the temperature data; iteratively optimizing multiple models to be optimized based on the applicable operating conditions and the error index to determine the optimal solution of the parameters to be optimized; and calibrating the optimization model of the second-order equivalent circuit model based on the optimal solution of the parameters to be optimized.

[0006] The calibration method of the second-order equivalent circuit model provided in the embodiment of the present application takes into account the situation that the second-order equivalent current model is not applicable in some working conditions. By optimizing the model parameters and the conditions for judging the applicable range of the model, the coordinated optimization of the model accuracy and coverage is achieved, ensuring the high accuracy within the applicable range of the second-order equivalent circuit model, avoiding the model from starting within the inapplicable range, and avoiding the estimation errors of the SOC value and terminal voltage caused by the error correction.

[0007] In certain embodiments, the multiple models to be optimized are iteratively optimized based on the applicable operating conditions and the error index to determine the optimal solution for the parameters to be optimized, including: in the test data set, determining the sample ratio value of the number of samples that do not match the applicable operating conditions to the total number of samples to determine the deactivation rate index, and the deactivation rate index is positively correlated with the sample ratio value; with minimization of the synergy index as the goal, the multiple models to be optimized are iteratively optimized to determine the optimal solution for the parameters to be optimized, the synergy index is positively correlated with the SOC error index, and the synergy index is positively correlated with the deactivation rate index.

[0008] In certain embodiments, the multiple models to be optimized are iteratively optimized with minimization of the synergy index as the goal to determine the optimal solution of the parameters to be optimized, including: while ensuring that the SOC error index is lower than a first value and the deactivation rate index is lower than a second value, the multiple models to be optimized are iteratively optimized with minimization of the synergy index as the goal to determine the optimal solution of the parameters to be optimized.

[0009] In certain embodiments, with minimization of the synergy index as a goal, multiple models to be optimized are iteratively optimized to determine the optimal solution of the parameters to be optimized, 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 multiple weight values ​​to be optimized to construct a weight value model to be optimized of multiple fitness function models corresponding to the multiple weight values ​​to be optimized; while ensuring that the SOC error index is lower than a first value and the deactivation rate index is lower than a second value, with minimization of the numerical product of the SOC error index and the deactivation rate index as a goal, multiple target models are iteratively optimized to determine the optimal solution of the weight values ​​to be optimized; based on the optimal model of the fitness function model corresponding to the optimal solution of the weight values ​​to be optimized, with minimization of the synergy index as a goal, multiple models to be optimized are iteratively optimized to determine the optimal solution of the parameters to be optimized.

[0010] In some embodiments, the voltage data includes multiple voltage test values ​​of the battery cell at different time periods, the current data includes multiple current test values ​​of the battery cell at different time periods, and the temperature data includes multiple temperature test values ​​of the battery cell at the same time. The determining of the applicable operating condition of the second-order equivalent model based on the full-operating condition optimization model, the voltage data, the current data and the temperature data includes: when the battery operating conditions are the same, obtaining multiple voltage differences between the voltage test value and the voltage estimation value determined by the full-operating condition optimization model at different time periods to determine a modeling error sequence; determining an average current sequence at multiple time scales based on the multiple current test values ​​of the battery cell at different time periods; determining the applicable current operating condition of the second-order equivalent model based on the multiple average current sequences and the modeling error sequence; determining a voltage fluctuation rate sequence at multiple time scales based on the multiple voltage test values ​​of the battery cell at different time periods; determining the applicable voltage operating condition of the second-order equivalent model based on the multiple voltage fluctuation rate sequences and the modeling error sequence; and determining the applicable temperature operating condition of the second-order equivalent model based on the multiple temperature test values ​​of the battery cell at the same time.

[0011] In some embodiments, determining the applicable current operating conditions of the second-order equivalent model based on multiple average current sequences and the modeling error sequence includes: calculating the correlation coefficients of multiple average current sequences and the modeling error sequence in sequence; sorting the time scale factors corresponding to the average current sequences from small to large, 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; constructing a current scale vector based on the first target scale factor and p+1 average current sequences corresponding to consecutive p time scale factors adjacent to the first target scale factor, where p is an arbitrary positive integer; obtaining the maximum current value in the current scale vector; and determining the applicable current operating conditions of the second-order equivalent model based on the ratio of each current value in the current scale vector to the maximum current value.

[0012] In some embodiments, determining the current applicable operating condition of the second-order equivalent model based on the ratio of each value in the current scale vector to the maximum value includes: when the ratio of each current value in the current scale vector to the maximum current value is less than or equal to a first scaling factor, determining that the test current value sample corresponding to the current scale vector satisfies the current applicable operating condition of the second-order equivalent model.

[0013] In some embodiments, determining the voltage applicable operating condition of the second-order equivalent model based on multiple voltage fluctuation rate sequences and the modeling error sequence includes: calculating the correlation coefficients of multiple voltage fluctuation rate sequences and the modeling error sequence in sequence; sorting the time scale factors corresponding to the voltage fluctuation rate sequences from small to large, selecting 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; constructing a voltage scale vector based on the second target scale factor and q+1 voltage fluctuation rate sequences corresponding to q consecutive time scale factors adjacent to the second target scale factor, where q is an arbitrary positive integer; obtaining the maximum voltage value in the voltage scale vector; and determining the current applicable operating condition of the second-order equivalent model based on the ratio of each voltage value in the voltage scale vector to the maximum voltage value.

[0014] In certain embodiments, determining the current applicable operating condition of the second-order equivalent model based on the ratio of each voltage value in the voltage scale vector to the maximum voltage value includes: when the ratio of each voltage value in the voltage scale vector to the maximum voltage value is less than or equal to a second scaling factor, determining that the test voltage value sample corresponding to the voltage scale vector satisfies the voltage applicable operating condition of the second-order equivalent model.

[0015] In certain embodiments, determining the applicable temperature operating condition of the second-order equivalent model based on multiple temperature test values ​​of the battery cell in the same time period includes: when the maximum temperature difference between the multiple temperature test values ​​of the battery cell in the same time period is lower than a preset temperature difference, determining that the temperature measurement value sample satisfies the applicable temperature operating condition of the second-order equivalent model.

[0016] An embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein the memory is configured to store a computer program, and the processor implements the calibration method of the second-order equivalent circuit model when executing the computer program.

[0017] An embodiment of the present application provides a calibration device for a second-order equivalent circuit model, and the calibration device includes the above-mentioned electronic device.

[0018] An embodiment of the present application provides a vehicle, comprising the above-mentioned calibration device and a battery, wherein the calibration device is configured to calibrate a second-order equivalent circuit model of the battery.

[0019] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the above-mentioned calibration method is implemented.

[0020] An embodiment of the present application provides a computer program product, including a computer program / instruction, which implements the above-mentioned calibration method when the computer program / instruction is executed by a processor.

[0021] Embodiments of the present application disclose a calibration method for a second-order equivalent circuit model, an electronic device, a calibration device for 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 includes: obtaining a test data set, the test data set includes voltage data, current data, temperature data, discharge rate data and SOC data of the battery cell under various working conditions, and the SOC data includes the SOC estimation value of the battery cell; determining multiple parameters to be optimized according to the temperature data, discharge rate data and SOC data to construct a model to be optimized of multiple second-order equivalent circuit models corresponding to the multiple parameters to be optimized; obtaining a full-operating condition optimization model, the full-operating condition optimization model is a second-order equivalent circuit model that minimizes the SOC error index, and the SOC error index is positively correlated with "the difference between the SOC test value and the SOC estimation value determined by each model to be optimized under the same battery working condition"; determining the applicable working condition conditions of the second-order equivalent circuit model according to the full-operating condition optimization model, the voltage data, the current data and the temperature data; iteratively optimizing multiple models to be optimized according to the applicable working condition conditions and the error index to determine the optimal solution of the parameters to be optimized; and calibrating the optimization model of the second-order equivalent circuit model according to the optimal solution of the parameters to be optimized.

[0022] The calibration method of the second-order equivalent circuit model provided in the embodiment of the present application takes into account the situation that the second-order equivalent current model is not applicable in some working conditions. By optimizing the model parameters and the conditions for judging the applicable range of the model, the coordinated optimization of the model accuracy and coverage is achieved, ensuring the high accuracy within the applicable range of the second-order equivalent circuit model, avoiding the model from starting within the inapplicable range, and avoiding the estimation errors of the SOC value and terminal voltage caused by the error correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0024] Figure 1 It is a flowchart of a calibration method provided in certain embodiments of the present application;

[0025] Figure 2 It is a flowchart of a calibration method provided in certain embodiments of the present application;

[0026] Figure 3 It is a flowchart of a calibration method provided in certain embodiments of the present application;

[0027] Figure 4It is a flowchart of a calibration method provided in certain embodiments of the present application;

[0028] Figure 5 It is a flowchart of a calibration method provided in certain embodiments of the present application;

[0029] Figure 6 It is a flowchart of a calibration method provided in certain embodiments of the present application;

[0030] Figure 7 It is a flowchart of a calibration method provided in certain embodiments of the present application;

[0031] Figure 8 This is an implementation effect diagram of the second-order equivalent circuit model after calibration of this application. DETAILED DESCRIPTION

[0032] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are optional and are only used to explain the embodiments of the present application, and cannot be understood as limiting the embodiments of the present application.

[0033] The second-order equivalent circuit model is one of the most widely used battery equivalent circuit models. The terminal voltage of the battery can be estimated based on the second-order equivalent circuit model.

[0034] However, the second-order equivalent circuit model is a highly lumped equivalent model that cannot fully reflect the electrochemical process inside the battery and cannot be applied to some operating conditions such as high current and low temperature. In related technologies, the parameters of the second-order equivalent circuit model are adjusted within the full range of operating conditions, with the minimum error of all operating conditions as the optimization goal. However, due to the existence of operating conditions beyond the applicable range of the model, the parameters obtained for some operating conditions (for example, the second-order equivalent circuit cannot be applied to some operating conditions such as high current and low temperature) often deviate from the optimal values ​​within the applicable range.

[0035] The second-order equivalent circuit model converts the terminal voltage U t (t) is described as:

[0036]

[0037] Among them U ocvis the open circuit voltage, R0 is used to describe the resistance of ohmic polarization, t can represent the time for the battery to discharge, I represents the discharge current of the battery, the first-order parameters R1 and τ1 are used to describe the electrochemical polarization, and the second-order parameters R2 and τ2 are used to describe the concentration polarization caused by solid phase diffusion. This patent mainly discusses how to determine R2 and τ2 by offline calibration. The second-order parameters R2 and τ2 that need to be calibrated offline are affected by the combined influence of the state of charge SOC, temperature T and discharge rate C, and are highly nonlinear. The existing scheme adjusts the model parameters R2 and τ2 within the full range of operating conditions, but does not consider the influence of the state of charge SOC, temperature T and discharge rate C on the model parameters, that is, it does not consider the existence of operating conditions beyond the applicable range of the model, resulting in some operating conditions (for example, the second-order equivalent circuit cannot be applied to some operating conditions such as large current and low temperature). The parameters obtained often deviate from the optimal values ​​within the applicable operating range.

[0038] In order to solve the above technical problems, the embodiment of the present application provides a calibration method for a second-order equivalent circuit model. Compared with the existing method of optimizing parameters with the goal of minimizing errors within the full range of operating conditions, the calibration method for the second-order equivalent circuit model provided by the embodiment of the present application takes into account the situation where the second-order equivalent current model is not applicable under some operating conditions. By optimizing the model parameters and the conditions for distinguishing the applicable range of the model, the coordinated optimization of the model accuracy and coverage is achieved, ensuring the high accuracy of the applicable range of the second-order equivalent circuit model, avoiding the model from starting within the inapplicable range, and avoiding the estimation errors of the SOC value (battery state of charge) and the terminal voltage caused by the error correction.

[0039] An embodiment of the present application also provides an electronic device, which includes a memory and a processor. The memory is configured to store a computer program. When the processor executes the computer program, it implements the calibration method of the second-order equivalent circuit model provided in the embodiment of the present application.

[0040] An embodiment of the present application provides a calibration device for a second-order equivalent circuit model. The calibration device provided by the embodiment of the present application may include the electronic device provided by the embodiment of the present application to implement the calibration method for the second-order equivalent circuit model provided by the embodiment of the present application.

[0041] See also Figure 1 , the calibration method of the order equivalent circuit model provided in the embodiment of the present application includes:

[0042] Step 01: Obtain a test data set, which includes voltage data, current data, temperature data, discharge rate data, and SOC data of the battery cells under various working conditions. The SOC data includes the estimated SOC value of the battery cells.

[0043] Step 02: determining a plurality of parameters to be optimized according to the temperature data, the discharge rate data and the SOC data, so as to construct a plurality of second-order equivalent circuit models to be optimized corresponding to the plurality of parameters to be optimized;

[0044] Step 03: Obtain the full-operating condition optimization model. The full-operating condition optimization model is a second-order equivalent circuit model that minimizes the SOC error index. The SOC error index is positively correlated with "the difference between the SOC test value and the SOC estimated value determined by each model to be optimized under the same battery operating condition";

[0045] Step 04: Determine the applicable working conditions of the second-order equivalent circuit model based on the full-operating condition optimization model, voltage data, current data, and temperature data;

[0046] Step 05: According to the applicable working conditions and error indicators, multiple models to be optimized are iteratively optimized to determine the optimal solution of the parameters to be optimized;

[0047] Step 06: Calibrate the optimization model of the second-order equivalent circuit model according to the optimal solution of the parameters to be optimized.

[0048] In certain embodiments, the electronic device and calibration equipment provided in the embodiments of the present application can execute a 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 the battery cells under various operating conditions, the SOC data including the SOC test value of the battery cells; determining multiple parameters to be optimized based on the temperature data, discharge rate data and SOC data to construct multiple models to be optimized of the second-order equivalent circuit models; determining the applicable operating conditions of the model to be optimized based on the full operating condition optimization model, voltage data, current data and temperature data; determining the optimal model from multiple models to be optimized based on the applicable operating conditions and error indicators".

[0049] Specifically, in step 01, the test data set may include various test data of battery cells under various working conditions, including voltage data, current data, temperature data, discharge rate data and SOC data of the battery cells, and the SOC data includes an estimated SOC value of the battery cells.

[0050] In some embodiments of step 01, corresponding sampling points (including current sampling points, voltage sampling points and temperature sampling points) can be set on the battery cells, and a cycle experiment of the battery pack can be carried out using multiple temperature conditions. The temperature data, voltage data and current data of each battery cell in the battery pack at different times are obtained through the sampling points to determine the temperature, voltage and current sequence of each battery cell.

[0051] Based on the sampled temperature data, voltage data and current data of the battery cell at different times, the SOC estimation value of the battery cell can be calculated and determined.

[0052] In some embodiments of step 01, the ampere-hour integration method may be used to calculate and determine the SOC estimation value of the battery cell at different times based on the sampled temperature data, voltage data, and current data of the battery cell at different times.

[0053] In some embodiments of step 01, cycle simulation experiments of various operating conditions can be performed on the battery pack at ambient temperatures of -20°C, 0°C, 25°C, and 40°C. Actual measurement experiments of high-speed and urban operating conditions are performed at temperatures of -20°C, 0°C, 25°C, and 40°C. The temperature, voltage, and current sequence of each battery cell are recorded at a sampling frequency of more than 1 Hz. The SOC value corresponding to each sampling point is obtained using the ampere-hour integration method, and it is necessary to ensure that each operating condition covers the SOC range of 85% to 5%.

[0054] There are 4 ambient temperatures and 5 operating conditions. Two sets of data can be obtained by testing under the same ambient temperature and operating condition, and a total of 40 sets of test data can be obtained.

[0055] In step 02, a corresponding modeling method can be used to convert the variation pattern of the internal resistance parameters of the second-order equivalent circuit model with respect to SOC, temperature T and discharge rate C into a series of parameters to be optimized, and then determine the models to be optimized corresponding to the multiple parameters to be optimized.

[0056] 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 about SOC, temperature T and discharge rate C can be constructed according to the corresponding relationship between the second-order parameters R2 and τ2 about SOC, temperature T and discharge rate C. The parameters to be optimized are the second-order parameters R2 and τ2 values ​​corresponding to different values ​​of SOC, temperature T and discharge rate C. The upper limit and lower limit of the parameters to be optimized are determined according to a certain scaling ratio based on the empirical value.

[0057] In some embodiments of step 02, for example, SOC is divided into 4 groups, temperature T is divided into 5 groups, and discharge rate C is divided into 2 groups, the number of second-order parameters R2 to be optimized is 40, and the number of second-order parameters τ2 to be optimized is 40. The initial values ​​of the second-order parameters R2 and τ2 are taken as 10 times as the upper limit and 0.1 times as the lower limit based on the empirical values.

[0058] In step 03, a full-operating-condition optimization model can be obtained. The full-operating-condition optimization model is a second-order equivalent circuit model that minimizes the SOC error index. The second-order parameter values ​​corresponding to the full-operating-condition optimization model only consider the minimization of the SOC error index.

[0059] In some embodiments of step 03, the SOC error index may be determined according to a fitness function. The fitness function corresponding to the SOC error index may be defined as:

[0060]

[0061] Where, soc(i) is the SOC value obtained by ampere-hour integration at the i-th sample point in the test data set, is the estimated SOC value of the i-th sample point estimated by the corresponding optimized model under the given parameters to be optimized, and N is the number of samples used for offline parameter estimation. The max(.) term means taking the maximum value in the sequence.

[0062] The SOC error index can be the value of the error index Fitness1 determined according to formula (2), and the value of Fitness1 is Iterative optimization is performed with the goal of minimizing Fitness1 to obtain the optimal second-order parameter values ​​under all working conditions, and then the full-condition optimization model corresponding to the optimal parameter values ​​under all working conditions is determined.

[0063] In some embodiments of step 03, a heuristic optimization algorithm may be selected, and the heuristic algorithm parameters may be set to perform iterative optimization with the goal of minimizing Fitness1 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.

[0064] In step 04, the battery cell needs to operate within a suitable current, voltage and temperature range. The current, voltage and temperature range corresponding to some operating conditions cannot ensure that the battery cell can operate normally. Therefore, the current data, voltage data and temperature data need to meet the restriction conditions for the battery cell to operate normally.

[0065] According to the full-operating condition optimization model of the second-order equivalent circuit model and the test data in the test data set, the change law of the battery cell estimation data and test data obtained by the full-operating condition optimization model can be determined, and then according to the constraints that the test data needs to meet, the applicable operating conditions of the second-order equivalent circuit model can be determined.

[0066] It is understandable that the applicable operating conditions of the second-order equivalent circuit model need to correspond to the restrictions that the test data needs to meet, that is, under the applicable operating conditions of the second-order equivalent circuit model, the battery cell operates within an appropriate range and ensures that the battery cell can work normally.

[0067] In step 05, while ensuring the applicable operating conditions of the second-order equivalent circuit model, it is also necessary to reduce the SOC error index as much as possible, that is, to achieve coordinated optimization of model accuracy and coverage.

[0068] In some embodiments of step 05, a heuristic optimization algorithm can be selected. By setting the heuristic algorithm parameters, iterative optimization is performed with the goal of simultaneously satisfying the applicable operating condition constraints and the SOC error index to determine the optimal solution for the parameters to be optimized, that is, the optimal values ​​of the second-order parameters R2 and τ2.

[0069] In step 06, the optimal values ​​of the second-order parameters R2 and τ2 are substituted into formula (1), and the optimal model of the second-order equivalent circuit model can be calibrated.

[0070] It can be understood that, compared with the existing calibration method in the full operating range, the calibration method of the embodiment of the present application takes into account the situation that the second-order equivalent current model is not applicable in some operating conditions. By optimizing the model parameters and the conditions for judging the applicable range of the model, the coordinated optimization of the model accuracy and coverage is achieved. The high accuracy of the second-order equivalent circuit model within the applicable range is ensured, and the model is prevented from starting in an inapplicable range, which avoids the large SOC estimation error.

[0071] Reference Figure 2 In some embodiments, step 05: iteratively optimizing multiple models to be optimized according to applicable working conditions and error indicators to determine the optimal solution of the parameters to be optimized, including:

[0072] Step 051: In the test data set, determine the sample ratio value of the number of samples that do not match the applicable working condition to the total number of samples to determine the deactivation rate index, and the deactivation rate index is positively correlated with the sample ratio value;

[0073] Step 052: Taking minimization of the synergy index as the goal, multiple models to be optimized are iteratively optimized to determine the optimal solution of the parameters to be optimized. The synergy index is positively correlated with the SOC error index, and the synergy index is positively correlated with the deactivation rate index.

[0074] Specifically, in step 051, each model to be optimized has corresponding applicable operating conditions, and the applicable operating conditions correspond to the limiting conditions of the battery cell operating current, voltage and temperature.

[0075] In the test data, samples that do not meet the restrictions on the current, voltage and temperature of the cell operating conditions corresponding to the applicable operating conditions are samples that do not match the applicable operating conditions. The total number of samples used to calibrate the second-order equivalent circuit model can be set to N, and the number of samples that do not match the applicable operating conditions can be set to N_deactive. The deactivation rate indicator can be set to N_deactive / N.

[0076] In step 052, a fitness function corresponding to the collaborative index is established. While considering the applicable scope of the model to be optimized, it is also necessary to reduce the model error as much as possible, that is, to achieve collaborative optimization of model accuracy and coverage. Therefore, the collaborative index is positively correlated with the SOC error index and the deactivation rate index. The fitness function corresponding to the newly established collaborative index can be defined as the weighted sum of the error term and the deactivation rate index:

[0077] Fitness=Fitness1+weight*N_deactive / N (3)

[0078] Among them, Fitness1 is the SOC error index determined according to formula (2), and weight is the weight value used to balance the final model accuracy and model coverage.

[0079] According to the value of the collaborative indicator Fitness determined by formula (3), iterative optimization is performed with the goal of minimizing Fitness to determine the optimal solution of the parameters to be optimized, that is, the optimal values ​​of the second-order parameters R2 and τ2.

[0080] Reference Figure 3 In some embodiments, step 052: taking minimization of the synergy index as the goal, iteratively optimizing multiple models to be optimized to determine the optimal solution of the parameters to be optimized includes:

[0081] Step 0521: While ensuring that the SOC error index is lower than the first value and the deactivation rate index is lower than the second value, multiple models to be optimized are iteratively optimized with minimization of the synergy index as the goal to determine the optimal solution for the parameters to be optimized.

[0082] Specifically, in step 0521, the first value may be an upper limit value of the error index, and the second value may 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.

[0083] According to the value of the error indicator Fitness1 determined by formula (3), it is necessary to ensure that Fitness1 is lower than Threshold1, and it is also necessary to ensure that N_deactive / N is lower than Threshold2.

[0084] Reference Figure 4 In some embodiments, step 052: taking minimization of the synergy index as the goal, determining the optimal model from multiple models to be optimized includes:

[0085] Step 0522: Obtain a fitness function model corresponding to the coordination index, where the fitness function model is configured to determine the coordination index according to a weighted sum of an SOC error and a deactivation rate index;

[0086] Step 0523: obtaining a plurality of weight values ​​to be optimized, so as to construct a weight value model to be optimized of a plurality of fitness function models corresponding to the plurality of weight values ​​to be optimized;

[0087] Step 0524: Under the condition that the SOC error index is lower than the first value and the deactivation rate index is lower than the second value, the multiple target models are iteratively optimized with the goal of minimizing the numerical product of the SOC error index and the deactivation rate index to determine the optimal solution of the weight value to be optimized;

[0088] Step 0525: Based on the optimal model of the fitness function model corresponding to the optimal solution of the weight value to be optimized, with minimization of the synergy index as the goal, multiple models to be optimized are iteratively optimized to determine the optimal solution of the parameters to be optimized.

[0089] Specifically, in step 0522, the fitness function model corresponding to the collaborative index can refer to formula (3). In formula (3), the value of the collaborative 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. Different values ​​of weight result in different fitness function models corresponding to the collaborative index.

[0090] In step 0523, W given weight values ​​to be optimized can be obtained, denoted as weight∈{weight i , i = 1, 2, ... W. Substituting the W given weight values ​​to be optimized into formula (3), we can obtain the weight value models to be optimized of the W given fitness function models.

[0091] In step 0524, a heuristic optimization algorithm can be selected, and the heuristic algorithm parameters can be set. A plurality of weight values ​​to be optimized weight∈{weight i ,i=1,2,…W} values, and perform iterative optimization in turn.

[0092] Obtain the error index Fitness1 and the model deactivation rate index N_deactive / N under different weights. Determine the upper limit value Threshold1 of the error index and the upper limit value Threshold2 of the model deactivation rate index according to actual needs, and perform iterative optimization with the goal of minimizing the product of the error index Fitness1 and the model deactivation rate index N_deactive / N. Finally, the optimal solution for the weight value to be optimized is determined by the following formula (4):

[0093] weight=argmin(Fitness1·N_deactive / N)

[0094]

[0095] In step 0525, the optimal solution of the weight value to be optimized is substituted into formula (3), and 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 multiple models to be optimized are iteratively optimized to determine the optimal solution of the parameters to be optimized.

[0096] In some embodiments, weight∈{10 20 30 40 50 60 70 80 90 100 120 150 180200}, the error indicator upper limit value Threshold1=6, and the model deactivation rate indicator upper limit value Threshold2=0.5.

[0097] Reference Figure 5 In some embodiments, the voltage data includes multiple voltage test values ​​of the battery cell at different time periods, the current data includes multiple current test values ​​of the battery cell at different time periods, and the temperature data includes multiple temperature test values ​​of the battery cell at the same time. Step 04: Determine the applicable operating conditions of the second-order equivalent model according to the full-operating condition optimization model, the voltage data, the current data, and the temperature data, including:

[0098] Step 041: Under the same battery operating conditions, multiple voltage differences between the voltage test value and the voltage estimation value determined by the full operating condition optimization model at different time periods are obtained to determine a modeling error sequence;

[0099] Step 0421: determining an average current sequence at multiple time scales according to multiple current test values ​​of the battery cell at different time periods;

[0100] Step 0422: determining the current applicable working condition of the second-order equivalent model according to multiple average current sequences and modeling error sequences;

[0101] Step 0431: determining a voltage fluctuation rate sequence at multiple time scales according to multiple voltage test values ​​of the battery cell at different time periods;

[0102] Step 0432: determining the voltage applicable working condition of the second-order equivalent model according to multiple voltage fluctuation rate sequences and modeling error sequences;

[0103] Step 044: Determine the applicable temperature operating conditions of the second-order equivalent model based on multiple temperature test values ​​of the battery cell at the same time.

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

[0105]

[0106] Among them, v(t) can be expressed as the voltage of discrete voltage sampling points in the test data set, It can be expressed as the terminal voltage determined by the full-operating condition optimization model.

[0107] In step 0421, for the discrete current sampling point i(t) in the test data set, the average current is calculated by the following formula:

[0108]

[0109] Where α is the time scale factor, The initial value is 0, that is By selecting multiple time scale factors, the corresponding average current series under multiple time scales can be obtained.

[0110]

[0111] In step 0422, according to the average current sequence The corresponding relationship with the modeling error sequence e(t), and then according to the restriction conditions that the working current of the battery cell needs to meet, the current applicable working conditions of the second-order equivalent circuit model can be determined.

[0112] In step 0431, for discrete voltage sampling points v(t), the corresponding average voltage for

[0113]

[0114] Where α is the time scale factor, The initial value is 3200. The voltage fluctuation rate is described by the following formula (9):

[0115]

[0116] By selecting multiple time scale factors, the corresponding voltage fluctuation rate series under multiple time scales can be obtained.

[0117] variance k ={variance α (t)),t=1,2,…N,α=α k} (10)

[0118] In step 0432, according to the voltage fluctuation rate sequence The corresponding relationship with the modeling error sequence e(t) and the constraints that the working voltage of the battery cell needs to meet can then determine the voltage applicable working conditions of the second-order equivalent circuit model.

[0119] In step 044, the battery cell may be provided with a plurality of temperature sampling points, and the plurality of temperature sampling points may collect the temperature of the battery cell at the same time. The temperature limit conditions that the battery cell needs to meet may be determined based on the plurality of temperature test values ​​of the battery cell at the same time, thereby determining the applicable temperature operating conditions of the second-order equivalent model.

[0120] Reference Figure 6 In some embodiments, step 0422: determining the current applicable working condition of the second-order equivalent model according to multiple average current sequences and modeling error sequences includes:

[0121] Step 04221: sequentially calculating correlation coefficients of multiple average current sequences and modeling error sequences;

[0122] Step 04222: sort the time scale factors corresponding to the average current sequence from small to large, select the average current sequence corresponding to the maximum correlation coefficient, so as to determine the target current sequence and the first target time scale factor corresponding to the target current sequence;

[0123] Step 04223: construct a current scale vector according to the first target scale factor and p+1 average current sequences corresponding to the consecutive p time scale factors adjacent to the first target scale factor, where p is any positive integer;

[0124] Step 04224: Obtain the maximum current value in the current scale vector;

[0125] Step 04225: Determine the current applicable operating conditions of the second-order equivalent model according to the ratio of each current value in the current scale vector to the maximum current value.

[0126] Specifically, in step 04221, the average current sequence is calculated in sequence Pearson correlation coefficient of the error sequence e(t) modeled with the terminal voltage.

[0127] In step 04222, the scale factors α are sorted from small to large, and the time scale factor α corresponding to the maximum value of the Pearson correlation coefficient is selected. m , and recorded as the first target time scale factor, the time scale factor α m The corresponding average current sequence is the target current sequence.

[0128] In step 04223, select the time scale factor α m And the p+1 average current sequences corresponding to the adjacent p consecutive time size factors construct the current scale vector.

[0129] In some embodiments, p can be 2n, n can be a positive integer, and the time scale factor α can be selected mand the time scale factor α m The n scale factors on both sides constitute the current scale vector α used to determine the applicable range of the model I =[α m-n ,…,α m-1 ,α m ,α m+1 ,…,α m+n ].

[0130] In step 04224, the applicable range judgment condition of the second-order model based on the multi-scale average current is constructed, and the maximum value of the average current sequence under each current scale is obtained in turn.

[0131]

[0132] Among them, α∈α I .

[0133] In step 04225, and The ratio value needs to be taken as appropriate to ensure the stability of the current value.

[0134] In some embodiments, step 4225: determining the current applicable operating condition of the second-order equivalent model according to the ratio of each value in the current scale vector to the maximum value includes:

[0135] When the ratio of each current value to the maximum current value in the current scale vector is less than or equal to the first scaling factor, 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 model.

[0136] Specifically, the current applicable working 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 factor k α , satisfying formula (12) can satisfy the current applicable working condition of the second-order equivalent model.

[0137]

[0138] In some embodiments, the set of time scale factors is α∈{0.99, 0.995, 0.999, 0.9995, 0.9999, 0.99995}.

[0139] In some embodiments, if n=1, then α I =[α m-1 ,α m ,α m+1 ].

[0140] In some embodiments, k αis a 3-dimensional vector whose element values ​​are random numbers between [0,1]. When executing step 052 above, you can adjust k α The value of is achieved by minimizing the collaborative index.

[0141] Reference Figure 7 In some embodiments, step 0432: determining the voltage applicable working condition of the second-order equivalent model according to a plurality of voltage fluctuation rate sequences and modeling error sequences, includes:

[0142] Step 04321: sequentially calculating correlation coefficients of multiple voltage fluctuation rate sequences and modeling error sequences;

[0143] Step 04322: sort the time scale factors corresponding to the voltage fluctuation rate sequence from small to large, select the voltage fluctuation rate sequence corresponding to the maximum correlation coefficient, so as to determine the target voltage sequence and the second target time scale factor corresponding to the target voltage sequence;

[0144] Step 04323: construct a voltage scale vector according to the second target scale factor and q+1 voltage fluctuation rate sequences corresponding to the q consecutive time scale factors adjacent to the second target scale factor, where q is any positive integer;

[0145] Step 04324: Obtain the maximum voltage value in the voltage scale vector;

[0146] 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.

[0147] Specifically, in step 04321, the voltage fluctuation rate sequence variance is calculated in sequence k Pearson correlation coefficient of the error sequence e(t) modeled with the terminal voltage.

[0148] In step 04322, the time scale factor α is sorted from small to large, and the time scale factor α corresponding to the maximum value of the Pearson correlation coefficient is selected. o , and recorded as the second target time scale factor, time scale factor α o The corresponding voltage fluctuation rate sequence is the target voltage sequence.

[0149] In step 04323, select the time scale factor α o And the q+1 voltage fluctuation rate sequences corresponding to the adjacent q consecutive time size factors construct the voltage scale vector.

[0150] In some embodiments, q can be 2r, r can be a positive integer, and the time scale factor α can be selected o and the time scale factor α oThe r scale factors on both sides constitute the voltage fluctuation rate scale vector α V =[α o-r ,…α o ,α o+1 ,…,α o+r ].

[0151] In step 04324, the applicable range judgment condition of the second-order model based on multi-scale voltage fluctuation rate is constructed, and the maximum value of each voltage fluctuation rate at each scale is obtained in turn.

[0152]

[0153] Among them, α∈α V .

[0154] In step 04325, With variance β The ratio value of (t) needs to be appropriate to ensure the stability of voltage fluctuation rate.

[0155] In some embodiments, step 04325: determining the current applicable working condition of the second-order equivalent model according to the ratio of each voltage value to the maximum voltage value in the voltage scale vector includes:

[0156] When the ratio of each voltage value to the maximum voltage value in the voltage scale vector 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 working condition of the second-order equivalent model.

[0157] Specifically, the voltage applicable working condition needs to satisfy that the ratio of each voltage value in the voltage scale vector to the maximum voltage value is less than the second scaling factor k β , satisfying formula (14) can satisfy the voltage applicable working condition of the second-order equivalent model.

[0158]

[0159] In some embodiments, step 044: determining the applicable temperature operating condition of the second-order equivalent model according to multiple temperature test values ​​of the battery cell in the same period of time includes:

[0160] When the maximum temperature difference between multiple temperature test values ​​of the battery cell in the same period of time is lower than the preset temperature difference, it is determined that the temperature measurement value sample meets the temperature applicable working condition conditions of the second-order equivalent model.

[0161] In some embodiments, taking r = 1, the operating condition limit scale vector α V =[α o-1 ,α o ,α o+1 ].

[0162] In some embodiments, k β is a 3-dimensional vector whose element values ​​are random numbers between [0,1]. When executing step 052 above, you can adjust k β The value of is achieved by minimizing the collaborative index.

[0163] In some embodiments, step 044: determining the applicable temperature operating condition of the second-order equivalent model according to multiple temperature test values ​​of the battery cell at the same time, includes:

[0164] Specifically, in step 044, if the maximum temperature difference between multiple temperature test values ​​of the battery cell at the same time is lower than , it is determined that the temperature applicable operating condition of the model to be optimized is met.

[0165] Temperature sampling data T at different times t m (t), m = 1, 2, ... M, where M is the number of temperature sampling points on the battery cell.

[0166] The temperature difference series is obtained according to the following formula

[0167] Delta_T(t)=max(T m (t)-T n (t)) (15) Where m,n∈{1,2,…M}. Then the applicable judgment condition of the model based on internal temperature difference is

[0168] Delta_T(t)≤Delta_T0 (16)

[0169] Wherein, Delta_T0 is the preset temperature difference.

[0170] In some embodiments, the value range of Delta_T0 is [0, 20]. When executing the above step 052, the value of Delta_T0 can be adjusted to achieve minimization of the coordination index.

[0171] In step 04, the applicable scope judgment condition of the model to be optimized must simultaneously meet the applicable working conditions of formula (12), formula (14) and formula (16), that is,

[0172]

[0173] In some embodiments, the time coefficient τ2 and the resistance coefficient R2 of the second-order parameters, the upper limit value of the time coefficient τ2 and the resistance coefficient R2 at the node in the parameters to be optimized is 10 times the empirical value, and the lower limit value is 0.1 times the empirical value.

[0174] The implementation effect of the calibration method provided in the embodiment of the present application is as follows: Figure 8 As shown, from Figure 8 It can be seen that the optimized model determined by calibration can ensure the SOC estimation accuracy of the model, and under the corresponding model coverage, the convergence of the model with initial error can be guaranteed.

[0175] An embodiment of the present application also provides a vehicle, which may include the calibration device and battery of the above 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 terminal voltage value of the battery are more accurate.

[0176] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the above-mentioned calibration method is implemented.

[0177] An embodiment of the present application provides a computer program product, including a computer program / instruction, which implements the above-mentioned calibration method when the computer program / instruction is executed by a processor.

[0178] The technical effects of the computer-readable storage medium and computer program product provided by the embodiments of the present application include all the technical effects of the calibration method of the embodiments of the present application, which are not elaborated here one by one.

[0179] In the description of this specification, the descriptions with reference to the terms "certain embodiments", "in an example", "exemplarily", etc., mean that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0180] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0181] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are optional and cannot be understood as limitations to the present application. A person skilled in the art can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A calibration method for a second-order equivalent circuit model, characterized in that: The method comprises: Acquire a test data set, wherein the test data set includes voltage data, current data, temperature data, discharge rate data, and SOC data of a battery cell under various operating conditions, wherein the SOC data includes an estimated SOC value of the battery cell; Determine a plurality of parameters to be optimized according to the temperature data, the discharge rate data, and the SOC data, so as to construct a plurality of second-order equivalent circuit models to be optimized corresponding to the plurality of parameters to be optimized; Obtaining a full-operating-condition optimization model, wherein the full-operating-condition optimization model is a second-order equivalent circuit model that minimizes an SOC error index, wherein the SOC error index is positively correlated with "a difference between the SOC test value and the SOC estimation value determined by each of the models to be optimized under the same battery operating conditions"; Determining applicable operating conditions of the second-order equivalent circuit model according to the full-operating-condition optimization model, the voltage data, the current data, and the temperature data; According to the applicable working conditions and the error index, iteratively optimize the multiple models to be optimized to determine the optimal solution of the parameters to be optimized; The optimization model of the second-order equivalent circuit model is calibrated according to the optimal solution of the parameter to be optimized.

2. The calibration method according to claim 1, characterized in that: The iterative optimization of the multiple models to be optimized according to the applicable working conditions and the error index to determine the optimal solution of the parameters to be optimized includes: In the test data set, determining a sample ratio value of the number of samples that do not match the applicable working condition to the total number of samples to determine a deactivation rate index, wherein the deactivation rate index is positively correlated with the sample ratio value; Taking minimization of the synergy index as the goal, multiple models to be optimized are iteratively optimized to determine the optimal solution of the parameters to be optimized, the synergy index is positively correlated with the SOC error index, and the synergy index is positively correlated with the deactivation rate index.

3. The calibration method according to claim 2, characterized in that: Taking minimization of the synergy index as the goal, iteratively optimizing the multiple models to be optimized to determine the optimal solution of the parameters to be optimized includes: Under the condition that the SOC error index is ensured to be lower than a first value and the deactivation rate index is lower than a second value, the multiple models to be optimized are iteratively optimized with minimization of the synergy index as the goal to determine the optimal solution of the parameters to be optimized.

4. The calibration method according to claim 3, characterized in that: Taking minimization of the synergy index as the goal, iteratively optimizing the multiple models to be optimized to determine the optimal solution of the parameters to be optimized includes: Acquire a fitness function model corresponding to a coordination index, wherein the fitness function model is configured to determine the coordination index according to a weighted sum of the SOC error and the deactivation rate index; Acquire a plurality of weight values ​​to be optimized to construct a weight value model to be optimized of a plurality of fitness function models corresponding to the plurality of weight values ​​to be optimized; 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, the multiple target models are iteratively optimized with minimizing the numerical product of the SOC error index and the deactivation rate index as the goal to determine the optimal solution of the weight value to be optimized; According to the optimal model of the fitness function model corresponding to the optimal solution of the weight value to be optimized, with minimization of the synergy index as the goal, multiple models to be optimized are iteratively optimized to determine the optimal solution of the parameters to be optimized.

5. The calibration method according to claim 2, characterized in that: The voltage data includes multiple voltage test values ​​of the battery cell at different time periods, the current data includes multiple current test values ​​of the battery cell at different time periods, and the temperature data includes multiple temperature test values ​​of the battery cell at the same time. The applicable operating condition of the second-order equivalent model is determined according to the full-operating condition optimization model, the voltage data, the current data, and the temperature data, including: Under the same battery operating conditions, obtaining multiple voltage differences between the voltage test value and the voltage estimation value determined by the full operating condition optimization model at different time periods to determine a modeling error sequence; Determining average current sequences at multiple time scales according to multiple current test values ​​of the battery cell at different time periods; Determining the current applicable working condition of the second-order equivalent model according to the plurality of average current sequences and the modeling error sequence; Determining voltage fluctuation rate sequences at multiple time scales according to multiple voltage test values ​​of the battery cell at different time periods; Determining the voltage applicable working condition of the second-order equivalent model according to the plurality of voltage fluctuation rate sequences and the modeling error sequence; According to a plurality of temperature test values ​​of the battery cell at the same time, the temperature applicable working condition of the second-order equivalent model is determined.

6. The calibration method according to claim 5, characterized in that: The determining of the current applicable working condition of the second-order equivalent model according to the plurality of average current sequences and the modeling error sequence includes: sequentially calculating correlation coefficients of a plurality of the average current sequences and the modeling error sequence; Sort the time scale factors corresponding to the average current sequence from small to large, select the average current sequence corresponding to the maximum value of the correlation coefficient, so as 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 consecutive time scale factors adjacent to the first target scale factor, where p is any positive integer; Obtaining a maximum current value in the current scale vector; The current applicable working condition of the second-order equivalent model is determined according to the ratio of each current value in the current scale vector to the maximum current value.

7. The calibration method according to claim 6, characterized in that: The determining of the current applicable working condition of the second-order equivalent model according to the ratio of each value in the current scale vector to the maximum value includes: When 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 factor, it is determined that the test current value sample corresponding to the current scale vector satisfies the current applicable operating condition of the second-order equivalent model.

8. The calibration method according to claim 5, characterized in that: The step of determining the voltage applicable operating condition of the second-order equivalent model according to the plurality of voltage fluctuation rate sequences and the modeling error sequence comprises: sequentially calculating correlation coefficients of a plurality of voltage fluctuation rate sequences and the modeling error sequence; Sort the time scale factors corresponding to the voltage fluctuation rate sequence from small to large, select the voltage fluctuation rate sequence corresponding to the maximum value of the correlation coefficient, so as 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 consecutive time scale factors adjacent to the second target scale factor, where q is any positive integer; Obtaining a maximum voltage value in the voltage scale vector; The current applicable working condition of the second-order equivalent model is determined according to the ratio of each voltage value in the voltage scale vector to the maximum voltage value.

9. The calibration method according to claim 8, characterized in that: The determining of the current applicable working 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 includes: When 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 condition of the second-order equivalent model.

10. The calibration method according to claim 5, characterized in that: Determining the applicable temperature operating condition of the second-order equivalent model according to multiple temperature test values ​​of the battery cell in the same period of time includes: When the maximum temperature difference between multiple temperature test values ​​of the battery cell in the same period of time is lower than the preset temperature difference, it is determined that the temperature measurement value sample meets the temperature applicable working condition condition of the second-order equivalent model.

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

12. A calibration device for a second-order equivalent circuit model, characterized in that: The calibration device comprises the electronic device according to claim 11.

13. A vehicle, characterized in that: The vehicle comprises the calibration device of claim 12 and a battery, the calibration device being configured to calibrate a second-order equivalent circuit model of the battery.

14. A computer-readable storage medium, characterized in that: 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 to 10 is implemented.

15. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the calibration method according to any one of claims 1 to 10 is implemented.

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