Battery process simulation test method

Abstract

The invention provides a battery process simulation test method, which comprises the following steps of: S1, inputting battery model parameters required by a second-order equivalent circuit and an initial state of a battery for pre-processing to obtain a basic second-order equivalent circuit simulation model, and introducing a series of calibration parameters on the basis of the basic second-order equivalent circuit model to obtain a simulation result of the battery model; errors are dynamically corrected in the simulation calculation process, the problem that an original model is insufficient in precision in the aspects of SOC change rate, voltage response and the like is effectively solved, and the accuracy and reliability of simulation results are remarkably improved. Aiming at complex and diversified process requirements in a battery test scene, a process simulation function supporting multiple process steps and multi-condition logic is constructed, an actual test process can be truly restored, and powerful data support is provided for process debugging, test time prediction and electric energy consumption evaluation.

Description

Technical Field

[0001] This invention relates to the field of battery testing technology, and more specifically, to a battery process simulation testing method. Background Technology

[0002] In the research, development, production, and quality control of batteries, battery testing is a crucial step in evaluating their performance, lifespan, and safety. The development of testing processes typically relies on specialized process editing software, which sets up a series of ordered steps (such as charging, discharging, resting, and cycle control) to achieve precise charge and discharge management of the battery. A complex testing process often involves dozens or even hundreds of steps, encompassing various conditional judgments, jump logic, parameter adjustments, and nested loops to meet the needs of different testing objectives (such as capacity testing, cycle life testing, and rate performance testing).

[0003] Currently, mainstream process editing software possesses basic syntax and standardization checking functions, capable of identifying explicit errors such as parameter out-of-bounds errors, incorrect step formatting, and unsupported instructions, ensuring that process documents meet the basic requirements for equipment execution. However, these software programs generally lack the ability to analyze and verify the correctness of the internal logic of the testing process. For example, they cannot identify problems such as infinite loops caused by improper condition judgment settings, errors in step jump logic, omissions of key steps in the test process, or redundant operations.

[0004] These logical flaws are typically not detected during the process development phase and can only be identified by analyzing the collected test data after the testing process has actually run. This not only renders the data from tested battery samples invalid, resulting in a waste of testing resources (such as test channel occupancy and environmental chamber usage), but also prolongs the R&D cycle and increases human, material, and energy costs. Furthermore, due to the lack of simulation and prediction capabilities for the process flow, engineers find it difficult to accurately predict key indicators such as the time required for the entire testing cycle, power consumption, and temperature control energy consumption before testing begins, thereby affecting the scheduling optimization of testing equipment, energy management, and project progress planning. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a battery process simulation test method that improves simulation accuracy, in order to address the shortcomings of the above-mentioned technical solutions.

[0006] This invention provides a battery process simulation testing method, the method comprising the following steps:

[0007] S1, input the battery model parameters and battery initial state required for the second-order equivalent circuit and preprocess them to obtain the basic second-order equivalent circuit simulation model. The battery model parameters include the OCV-SOC curve function, internal resistance, and capacitance and resistance of the two RC branches. The battery initial state includes the initial SOC value and initial temperature.

[0008] S2, acquire at least one real historical data of the process step, and use the second-order equivalent circuit simulation model to perform simulation to generate simulation data. Divide the interval into an interval every Δsoc, compare the time difference between the simulation data and the real historical data in each interval, and generate a calibration parameter set for the current battery sample by iteratively adjusting the compensation parameters of each SOC interval.

[0009] S3, receive a test process file in JSON or other format written by external process writing software, parse the test process file and create a mapped object in the program, construct a process object model containing process main information and multiple process step sets, and schedule the execution conditions, jump relationships, cut-off conditions and variable assignments of each process step through the process executor.

[0010] S4. The simulation solver selects the corresponding simulation strategy according to the step type defined in the test process file and performs numerical simulation based on the physical model.

[0011] S5. An adaptive step-size iterative algorithm is used to correct the calculation error of the numerical simulation in step S4. The time difference between the current moment and the next moment is dynamically configured to reduce the error caused by the current approximation.

[0012] S6 collects time series data generated during the simulation process, automatically analyzes logical errors in the process steps according to preset rules, and outputs structured simulation data for manual secondary analysis and verification.

[0013] In the battery process simulation and testing method of the present invention, the step S2 is at least one of constant current charging, constant voltage charging, constant power charging, and constant resistance charging.

[0014] In the battery process simulation testing method of the present invention, in step S2, the real historical data is obtained from a specific SOC range, including real data of constant current charging, constant voltage charging, constant power charging, and constant resistance charging. At the same time, the second-order equivalent circuit simulation model is used to simulate the data during this period to generate simulation data. If the simulation time is longer than the real data, an SOC compensation parameter is introduced in this range to increase the offset of the SOC calculated value in the simulation. The offset is adjusted iteratively until the deviation between the simulation result and the real data is within the allowable error range. Then, the next range is entered for calculation and comparison, and finally, the calibration parameters unique to the battery are generated.

[0015] In the battery process simulation and testing method of the present invention, the formula for calculating the calibration parameter in step S2 is cal=(1-curSoc)*10*timeStep*base*e timeOffset;cal represents the calibration parameter value, curSoc represents the starting soc of the interval, timeStep represents the time step of the calculation, base represents the base coefficient, and timeOffset represents the time deviation between the simulation data and the real data.

[0016] In the battery process simulation test method of the present invention, in step S4, the simulation solver pre-programs processing methods for constant current charging, constant voltage charging, constant power charging, and constant resistance charging. Based on the constant current charging, constant voltage charging, constant power charging, and constant resistance charging defined in the test process file, the corresponding method is selected to execute the simulation, and numerical simulation is performed based on the physical model.

[0017] In the battery process simulation and testing method described in this invention, the physical model includes calculating the battery terminal voltage, and the specific calculation formula is as follows:

[0018] Among them, V T This indicates the battery terminal voltage, V. OC R represents the open-circuit voltage, R0 represents the battery internal resistance, I is the input current, and R P1 I is the polarization impedance 1, R P2 I is the polarization impedance 2, C P1 Polarization capacitor 1, C P2 Here, Δt1 is the step time t, which is the cumulative charging and discharging time of a single step; Δt2 is the step time t, which is the cumulative charging and discharging time of a single step.

[0019] In the battery process simulation and testing method described in this invention, the physical model further includes calculating the SOC change using the ampere-hour integration method, with the specific calculation formula as follows: Where ΔSOC is the change in battery state of charge, I is the current, Δt is the duration of the current, and capacity is the rated capacity of the battery.

[0020] In the battery process simulation and testing method described in this invention, the physical model further includes calculating the polarization voltage using a discretized form, with the specific calculation formula as follows: Where prevUc is the polarization voltage at the current time point, Rp is the polarization impedance, and Cp is the polarization capacitance. Let I be the polarization voltage at the next time point, and let I be the current between the two time points.

[0021] The battery process simulation and testing method of this invention introduces a series of calibration parameters based on a basic second-order equivalent circuit model and dynamically corrects errors during the simulation calculation process. This effectively overcomes the shortcomings of the original model in terms of accuracy in areas such as SOC change rate and voltage response, significantly improving the accuracy and reliability of the simulation results. Addressing the complex and diverse process requirements in battery testing scenarios, a process simulation function supporting multiple steps and multi-condition logic has been constructed. This function can realistically reproduce the actual testing process, providing strong data support for process debugging, test time prediction, and energy consumption assessment. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating an embodiment of the battery process simulation and testing method of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] like Figure 1 The diagram shown is a flowchart illustrating an embodiment of a battery process simulation testing method according to the present invention. A battery process simulation testing method is provided, the method comprising the following steps:

[0026] In step S1, the battery model parameters and initial state of the battery required for the second-order equivalent circuit are preprocessed to obtain a basic second-order equivalent circuit simulation model. The battery model parameters include the OCV-SOC curve function, internal resistance, and the capacitance and resistance of the two RC branches. The initial state of the battery includes the initial SOC value and the initial temperature.

[0027] In step S2, at least one real historical data of the process step is obtained, and simulation is performed using the second-order equivalent circuit simulation model to generate simulation data. Each interval Δsoc is divided into an interval, and the time difference between the simulation data and the real historical data in each interval is compared. The compensation parameters of each SOC interval are adjusted iteratively to generate a calibration parameter set for the current battery sample.

[0028] In step S3, a test process file in JSON or other format written by external process writing software is received, the test process file is parsed and a mapped object is created in the program, a process object model containing process main information and multiple process step sets is constructed, and the execution conditions, jump relationships, cut-off conditions and variable assignments of each process step are scheduled through the process executor.

[0029] In step S4, the simulation solver selects the corresponding simulation strategy according to the step type defined in the test process file and performs numerical simulation based on the physical model.

[0030] In step S5, an adaptive step-size iterative algorithm is used to correct the calculation error of the numerical simulation in step S4, and the time difference between the current moment and the next moment is dynamically configured to reduce the error caused by the current approximation.

[0031] In step S6, time series data generated during the simulation process are collected, logical errors in the process are automatically analyzed according to preset rules, and structured simulation data is output for manual secondary analysis and verification.

[0032] In one embodiment, the step S2 is at least one of constant current charging, constant voltage charging, constant power charging, and constant resistance charging.

[0033] In one embodiment, in step S2, the real historical data is obtained from a specific SOC interval, including real data of constant current charging, constant voltage charging, constant power charging, and constant resistance charging. At the same time, the second-order equivalent circuit simulation model is used to simulate the data during this period to generate simulation data. If the simulation time is longer than the real data, an SOC compensation parameter is introduced in this interval to increase the offset of the SOC calculated in the simulation. The offset is adjusted iteratively until the deviation between the simulation result and the real data is within the allowable error range. Then, the next interval is entered for calculation and comparison, and finally, the calibration parameters unique to the battery are generated.

[0034] In one embodiment, the calibration parameter calculation formula in step S2 is cal=(1-curSoc)*10*timeStep*base*e timeOffset;cal represents the calibration parameter value, curSoc represents the starting soc of the interval, timeStep represents the time step of the calculation, base represents the base coefficient, and timeOffset represents the time deviation between the simulation data and the real data.

[0035] In one embodiment, in step S4, the simulation solver pre-programs processing methods for constant current charging, constant voltage charging, constant power charging, and constant resistance charging. Based on the constant current charging, constant voltage charging, constant power charging, and constant resistance charging defined in the test process document, it selects the corresponding method to execute the simulation and performs numerical simulation based on the physical model.

[0036] In one embodiment, the physical model includes calculating the battery terminal voltage, specifically using the following formula:

[0037] Among them, V T This indicates the battery terminal voltage, V. OC R represents the open-circuit voltage, R0 represents the battery internal resistance, I is the input current, and R P1 I is the polarization impedance 1, R P2 I is the polarization impedance 2, C P1 Polarization capacitor 1, C P2 Here, Δt1 is the step time t, which is the cumulative charging and discharging time of a single step; Δt2 is the step time t, which is the cumulative charging and discharging time of a single step.

[0038] In one embodiment, the physical model further includes calculating the change in SOC using the ampere-hour integration method, with the specific calculation formula being as follows: Where ΔSOC is the change in battery state of charge, I is the current, Δt is the duration of the current, and capacity is the rated capacity of the battery.

[0039] In one embodiment, the physical model further includes calculating the polarization voltage using a discretized form, specifically calculated using the following formula: Where prevUc is the polarization voltage at the current time point, Rp is the polarization impedance, and Cp is the polarization capacitance. Let be the polarization voltage at the next time point, and I be the current between the two time points. Here, expTerm is equivalent to the exponential function of the latter e, and is a variable without a specific definition.

[0040] Specifically, this application introduces a model calibration mechanism based on real historical data to dynamically adjust compensation parameters within different SOC ranges. This effectively corrects the systematic biases of traditional second-order equivalent circuit models in estimating SOC change rates and voltage dynamic response characteristics, significantly improving the temporal consistency and physical reliability of simulation results with actual battery behavior. By utilizing actual process step data to generate personalized calibration parameter sets for specific battery samples, the simulation model can accurately reflect performance differences between individual cells or different batches of batteries in terms of aging degree, internal resistance drift, and capacity decay. This achieves refined modeling and simulation for each battery, enhancing the individual adaptability and engineering practicality of process verification. Combining structured process document parsing, adaptive step-size iterative solution, and closed-loop simulation mechanisms, high-precision numerical simulation of complex charge-discharge steps such as constant current and constant voltage is achieved, ensuring the stability and convergence of the simulation process. Simultaneously, through built-in automatic logic analysis, potential errors such as abnormal step jumps, cutoff condition conflicts, and parameter out-of-bounds errors can be detected in real time during simulation, identifying process design defects in advance and avoiding equipment failures or safety risks in real testing. It not only significantly improves the realism, accuracy, and flexibility of battery process simulation, but also greatly shortens the process development cycle, reduces experimental costs, and improves R&D safety. It has broad application value and practical significance in scenarios such as power battery R&D, BMS algorithm verification, production line process pre-verification, and fast charging strategy optimization.

[0041] It should be noted that while the basic second-order equivalent circuit model is simple in structure and computationally efficient, its simulation accuracy is limited, and it does not consider actual influencing factors such as battery aging and batch differences. To improve the model's ability to fit real battery behavior, this application introduces a calibration mechanism based on real historical data for each batch or individual battery cell. A personalized calibration parameter set is generated using data from a small number of typical processes, thereby significantly improving simulation accuracy.

[0042] Specifically, we first collect real historical test data for several basic charging steps (such as constant current charging, constant voltage charging, constant power charging, and constant resistance charging). We then divide the SOC (State of Charge) range into fixed intervals (e.g., each interval is defined as ΔSOC = 0.1), and compare the time response differences between the simulation results of the basic model and the real data within each interval. ΔSOC represents the change in battery state of charge, with a value between 0 and 1.

[0043] Taking constant current charging as an example: Real data is collected showing the battery charging from SOC = 0.1 to SOC = 0.9 with a current of 1C (i.e., 1 times the rated capacity current). Simultaneously, a second-order equivalent circuit model is used to simulate this process. The entire charging process is divided into multiple SOC intervals (e.g., [0.1–0.2], [0.2–0.3], etc.), and the time difference required to reach the same SOC increment is compared between the real data and the simulated data within each interval.

[0044] If the simulation takes longer, it indicates that the predicted SOC rise rate in that interval is too slow. In this case, an SOC compensation parameter is generated. During the simulation calculation in that interval, the value of the compensation parameter is iteratively adjusted to continuously reduce the time deviation between the simulation and the actual data until the error is within an acceptable range. After completing the calibration for the current interval, the calculation and comparison continue in the next interval, ultimately generating a set of calibration parameters specific to that battery sample. This mechanism effectively compensates for model deviations caused by factors such as battery aging and changes in internal resistance, achieving refined modeling for each battery.

[0045] In practical applications, users write software to design and test process flows using external technology and export them as JSON-formatted process files as system input. The system utilizes the open-source JSON parsing library FastJson to map the structured information in the process files to an internal object model, constructing a process object containing the main process information and multiple process steps. The process executor is responsible for scheduling the execution logic of each process step, including:

[0046] Triggering conditions for each step (such as time, voltage, SOC triggering); condition changes and jump relationships during execution; dynamic assignment of variables; cutoff condition judgment (such as reaching the target voltage, current decay to the threshold, etc.); exception handling and process interruption mechanism.

[0047] Battery model parameters (such as OCV-SOC curves, internal resistance, and RC branch parameters) and initial battery conditions (such as initial SOC and temperature) are used as inputs to provide the battery's static characteristics and initial conditions for simulation calculations. Simultaneously, real historical test data is imported for model calibration, further improving simulation accuracy. The simulation solver executes simulation calculations by calling the corresponding processing method based on the received step type information.

[0048] For constant current steps, the core is to calculate the battery terminal voltage based on a given current value; for constant voltage steps, the core is to deduce the required current value from the target voltage. For constant current steps, the battery state after Δt time can be directly calculated based on the initial SOC using the ampere-hour integration method. However, for steps with changing current, since the change in SOC depends on the current, the current value at the next moment cannot be directly predicted. Therefore, an approximation is used: the current at the next moment is considered equal to the current at the current moment, and ΔSOC and the subsequent open-circuit voltage Voc are calculated accordingly.

[0049] The core computational logic for simulation solutions differs for different types of work steps:

[0050] Constant current charging step: Given a current value, the core is to calculate the corresponding terminal voltage based on the battery model;

[0051] Constant voltage charging step: Given a target voltage, the core is to reverse-engineer the current value required to achieve that voltage.

[0052] For processes where the current remains constant, such as constant current, the change in SOC after a time step Δt can be directly calculated based on the current SOC using the ampere-hour integration method. This allows for updating the battery state and calculating the subsequent open-circuit voltage Voc.

[0053] For steps involving current changes (such as constant voltage charging, constant power charging, and constant resistance charging), since the current at the next moment is unknown, ΔSOC cannot be directly calculated. Therefore, an approximation strategy is adopted: the current at the next moment is approximated as the current at the current moment, and the SOC change and the corresponding Voc value are estimated in this way, which serve as the initial input for iterative solution.

[0054] Taking the constant voltage charging step as an example (the constant power charging and constant resistance charging steps are similar in principle, while the constant current step does not require this process), the specific iterative process is as follows:

[0055] Given a target constant voltage value V0, an initial approximate current I1 is set. Based on the current battery state, the state variables such as SOC and Voc at the next time step are predicted, and the corresponding terminal voltage V1 is calculated. V1 is compared with the target constant voltage value V0. If the deviation is large, the approximate current I1 is adjusted, and the state and voltage at the next time step are recalculated. This iterative process is repeated until |V1-V0| is less than the preset tolerance, at which point the voltage is considered to have converged, and the simulation for that time step is complete.

[0056] Specifically, for the current variation step, taking the constant voltage charging step as an example (the constant power charging and constant resistance charging steps are similar), and constant current charging does not require this operation; based on the current battery state and the initial approximate current I, predict the battery state at the next moment and calculate the corresponding terminal voltage V1; calculate the deviation ε = V1 - V0 between V1 and the target constant voltage value V0; if |ε| exceeds the preset convergence threshold, adjust the approximate current value according to the error direction: if ε>0, it means the voltage is too high, so decrease the current; if ε<0, it means the voltage is too low, so increase the current; update the current value I = I1 + ΔI, and recalculate the voltage at the next moment; after each adjustment, determine whether the error direction has reversed: if the direction has reversed, it means the correction step size ΔI is too large, so let ΔI = ΔI / 2; if the direction has not changed but the error is still large, increase the step size, let ΔI = ΔI + ΔI / 10; repeat the above process until |V1 - V0| is less than the allowable error, then the voltage is considered to have converged, and the simulation calculation for this time step is completed. This adaptive iterative mechanism effectively solves the problem that the current cannot be explicitly solved in constant voltage steps, improves simulation accuracy and numerical stability, and ensures that the real response behavior of the battery can still be accurately simulated under dynamic conditions.

[0057] Specifically, the preset rules in step S6 are user-defined and refer to the process operation results that meet the user's expectations.

[0058] For example, a certain testing process includes the following three steps:

[0059] Charge at a constant current of 1A until the voltage reaches 4V or the charging time reaches 1 hour.

[0060] Charge at a constant voltage of 4V until the current decays to 1A;

[0061] Discharge at a constant current of 1A until the voltage drops to 3V or the discharge time reaches 1 hour.

[0062] For this process, users can customize a set of verification rules according to their expected goals, for example:

[0063] Rule 1: All steps must be triggered and terminated by physical conditions such as voltage and current, and must not be terminated due to time constraints (i.e., "timeout termination" is prohibited);

[0064] Rule 2: The cumulative charging capacity of the first two charging steps should be equal to the discharge capacity of the third step to ensure energy closed loop.

[0065] These rules do not have a fixed template; instead, they are flexibly defined by the user based on the process design intent—that is, "set the corresponding judgment criteria according to what behavior the process is expected to achieve." After the simulation runs, the system automatically compares the output timing data with these preset rules to determine whether the simulation results meet expectations.

[0066] Because battery testing processes typically involve multiple complex steps and jump logic, hidden errors such as logical conflicts, improper cutoff condition settings, and capacity mismatches are easily encountered during manual programming. These errors are difficult to detect through static program checks in traditional development processes and often require actual testing to be exposed, resulting in long debugging cycles and high costs.

[0067] The simulation and rule verification mechanism of this method can identify process design defects in advance without the participation of physical batteries, effectively verify the correctness and feasibility of process logic, and significantly improve the reliability and efficiency of process development.

[0068] Based on the second-order equivalent circuit model, this application introduces a series of interval SOC calibration parameters to dynamically compensate for model deviations according to SOC intervals, thereby improving the accuracy of state estimation. At the same time, an adaptive step-size iterative algorithm is added to dynamically adjust the time step during the simulation process and correct the calculation error caused by the current approximation, which effectively improves the accuracy of voltage response and charging process simulation and significantly optimizes the overall simulation accuracy.

[0069] The system directly interfaces with external process design software. After completing the test process design, users can import the process file (such as in JSON format) into the simulation system and run it immediately. Based on the calibrated battery model and physical simulation engine, the system generates complete virtual test data, covering detailed operational information for each step, including key parameters such as current, voltage, capacity, time, and temperature.

[0070] The simulation results can be used to: quickly identify process logic errors (such as conflicting jump conditions or unreasonable cutoff conditions); estimate the time and energy consumption required for the entire testing process; assist in process parameter debugging and process optimization; and support physical pre-verification of new processes, reducing trial-and-error costs. Through a closed-loop process of "programming—simulation—verification," the development efficiency and reliability of battery testing processes are significantly improved, providing strong support for engineering applications.

[0071] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0072] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0073] Therefore, the above description is only a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A battery process simulation and testing method, characterized in that, The method includes the following steps: S1, input the battery model parameters and battery initial state required for the second-order equivalent circuit and preprocess them to obtain the basic second-order equivalent circuit simulation model. The battery model parameters include the OCV-SOC curve function, internal resistance, and capacitance and resistance of the two RC branches. The battery initial state includes the initial SOC value and initial temperature. S2, acquire at least one real historical data of the process step, and use the second-order equivalent circuit simulation model to perform simulation to generate simulation data. Divide the interval into an interval every Δsoc, compare the time difference between the simulation data and the real historical data in each interval, and generate a calibration parameter set for the current battery sample by iteratively adjusting the compensation parameters of each SOC interval. S3, receive a test process file in JSON or other format written by external process writing software, parse the test process file and create a mapped object in the program, construct a process object model containing process main information and multiple process step sets, and schedule the execution conditions, jump relationships, cut-off conditions and variable assignments of each process step through the process executor. S4. The simulation solver selects the corresponding simulation strategy according to the step type defined in the test process file and performs numerical simulation based on the physical model. S5. An adaptive step-size iterative algorithm is used to correct the calculation error of the numerical simulation in step S4. The time difference between the current moment and the next moment is dynamically configured to reduce the error caused by the current approximation. S6 collects time series data generated during the simulation process, automatically analyzes logical errors in the process steps according to preset rules, and outputs structured simulation data for manual secondary analysis and verification.

2. The battery process simulation and testing method according to claim 1, characterized in that, In step S2, the process step is at least one of constant current charging, constant voltage charging, constant power charging, and constant resistance charging.

3. The battery process simulation and testing method according to claim 2, characterized in that, In step S2, the real historical data is obtained from a specific SOC range, including real data on constant current charging, constant voltage charging, constant power charging, and constant resistance charging. Simultaneously, the second-order equivalent circuit simulation model is used to simulate the data during this period to generate simulation data. If the simulation time is longer than the real data, an SOC compensation parameter is introduced in this range to increase the offset of the calculated SOC value in the simulation. This offset is adjusted iteratively until the deviation between the simulation result and the real data is within the allowable error range. Then, the next range is entered for calculation and comparison, and finally, the calibration parameters unique to the battery are generated.

4. The battery process simulation testing method according to claim 3, characterized in that, The formula for calculating the calibration parameters in step S2 is cal=(1-curSoc)*10*timeStep*base*e timeOffset ;cal represents the calibration parameter value, curSoc represents the starting soc of the interval, timeStep represents the time step of the calculation, base represents the base coefficient, and timeOffset represents the time deviation between the simulation data and the real data.

5. The battery process simulation testing method according to claim 4, characterized in that, In step S4, the simulation solver pre-programs processing methods for constant current charging, constant voltage charging, constant power charging, and constant resistance charging. Based on the constant current charging, constant voltage charging, constant power charging, and constant resistance charging defined in the test process document, it selects the corresponding method to execute the simulation and performs numerical simulation based on the physical model.

6. The battery process simulation testing method according to claim 4, characterized in that, The physical model includes calculating the battery terminal voltage, and the specific calculation formula is as follows: Among them, V T This indicates the battery terminal voltage, V. OC R represents the open-circuit voltage, R0 represents the battery internal resistance, I is the input current, and R P1 I is the polarization impedance 1, R P2 I is the polarization impedance 2, C P1 Polarization capacitor 1, C P2 Here, Δt1 is the step time t, which is the cumulative charging and discharging time of a single step; Δt2 is the step time t, which is the cumulative charging and discharging time of a single step.

7. The battery process simulation testing method according to claim 6, characterized in that, The physical model also includes calculating the SOC change using the ampere-hour integration method, with the specific calculation formula being as follows: in Δ SOC is the change in the state of charge of the battery, I is the current, Δt is the duration of the current, and capacity is the rated capacity of the battery.

8. The battery process simulation testing method according to claim 6, characterized in that, The physical model also includes calculating the polarization voltage using a discretized form, with the specific calculation formula as follows: Where prevUc is the polarization voltage at the current time point, Rp is the polarization impedance, and Cp is the polarization capacitance. Let I be the polarization voltage at the next time point, and let I be the current between the two time points.

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