Battery SOC-SOH joint evaluation method and device

Abstract

The invention discloses a battery SOC-SOH joint evaluation method and device, and the method comprises the steps: screening standing working condition points of an energy storage battery SOC in a non-plateau period, building a dynamic SOC-OCV function and an SOH experience attenuation model, taking the minimum SOC jump of a calibration point as a target optimization parameter, updating the model according to an optimization result, calculating the subsequent SOC and SOH, monitoring the SOC calibration value jump of a subsequent standing point, and carrying out the SOC-SOH joint evaluation. And when the threshold value is exceeded, the dynamic OCV estimation is restarted. The device comprises a working condition point screening module, a model construction and parameter optimization module, an SOC and SOH calculation module, and a monitoring and restarting module. By dynamically updating model parameters and a jump monitoring mechanism, the problem of insufficient evaluation precision under complex working conditions is solved, the method is suitable for scenes such as new energy automobiles and energy storage systems, and the accuracy and adaptability of battery state evaluation are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery performance analysis, and particularly relates to a battery SOC-SOH combined evaluation method and device. Background Art

[0002] Against the backdrop of the accelerated global clean energy transition, battery systems, as core energy storage units, have deeply penetrated into fields such as new energy vehicles, smart grids, and distributed energy. However, under long-term cycling conditions, batteries are susceptible to irreversible aging effects such as capacity decay and internal resistance degradation due to the nonlinear and time-varying characteristics of their electrochemical properties. This degradation behavior not only significantly restricts their service life but also poses safety hazards such as thermal runaway under multi-stress coupling conditions. Accurate monitoring and assessment of battery operating status, especially the collaborative identification of state of charge (SOC) and state of health (SOH), has become a key link in improving the reliability and safety protection level of energy storage systems throughout their life cycle.

[0003] Battery SOC (State of Charge) refers to the ratio of a battery's current remaining charge to its maximum available capacity, reflecting its state of charge at a given moment. Battery SOH (State of Health) measures the degree of degradation of a battery's current performance compared to its initial performance, typically expressed as a percentage. This indicator is used to assess whether the battery meets operational requirements or requires maintenance or replacement. An accurate SOC-SOH assessment system should be able to monitor battery operating status in real time, predict future performance changes, and support maintenance and replacement decisions.

[0004] Currently, battery SOC-SOH assessment methods primarily include laboratory testing, physical models, and data-driven statistical models. Laboratory testing offers high accuracy but is costly and difficult to apply to real-time monitoring in actual projects. Physical models are based on the electrochemical characteristics of batteries, but their parameters are complex and difficult to accurately obtain. Data-driven statistical models utilize historical data for real-time monitoring, but they rely heavily on large datasets and struggle to handle small datasets or new battery types. Therefore, a highly accurate and versatile combined battery SOC-SOH assessment method suitable for complex operating conditions is urgently needed. Summary of the Invention

[0005] To this end, the present invention provides a battery SOC-SOH joint evaluation method and device to solve the problems of existing battery SOC-SOH evaluation methods such as complex aging paths under complex working conditions, insufficient applicability and accuracy, and limitations in handling new and aged batteries.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a battery SOC-SOH combined evaluation method, comprising the following steps:

[0007] S1. Based on the actual operating conditions of the energy storage battery, determine and select the static operating point of the energy storage battery SOC in the non-plateau period;

[0008] S2. Construct a function of the dynamic SOC-OCV curve and an empirical SOH attenuation model, and construct an optimization equation based on the goal of minimizing the SOC jump at the calibration point to perform parameter optimization.

[0009] S3. Update the function of the dynamic SOC-OCV curve and the parameters of the SOH empirical decay model according to the parameter optimization result, and calculate the subsequent SOC and SOH of the energy storage battery;

[0010] S4. Monitor the jump of the SOC calibration value at the subsequent static point. If the jump exceeds the preset jump value threshold for a set number of consecutive times, repeat steps S1, S2, and S3 to restart the dynamic OCV estimation.

[0011] As a preferred solution for the battery SOC-SOH joint evaluation method, the method of judging and screening the SOC static operating point of the energy storage battery in the non-plateau period based on the actual operating conditions of the energy storage battery specifically includes:

[0012] Collect the battery current and voltage curves over time, and select the operating conditions where the cluster current is less than the static current threshold σ and the duration is greater than 15 minutes, and the cell voltage change rate before the static end time is less than the threshold ζ. σ is set according to the battery capacity, and ζ is set according to the battery capacity and voltage collection accuracy.

[0013] Based on the cell voltage corresponding to the selected static operating point and combined with the battery's initial SOC-OCV curve, the battery's state of charge (SOC) at the time of rest is estimated. Based on the condition that the battery SOC is greater than a threshold value α or less than a threshold value β, a standby SOC operating point in the non-plateau period is selected; wherein α and β are set according to the inflection points at both ends of the battery SOC-OCV curve.

[0014] As a preferred solution for the battery SOC-SOH joint evaluation method, the function of constructing a dynamic SOC-OCV curve and an SOH empirical decay model is constructed, and an optimization equation based on the goal of minimizing the SOC jump at the calibration point is constructed to perform parameter optimization, specifically including:

[0015] Establish the function expression of dynamic SOC-OCV curve:

[0016] OCV=f(SOC;Φ)

[0017] Where Φ is the parameter set to be estimated for the dynamic SOC-OCV curve;

[0018] Select the SOH empirical decay model, the expression is:

[0019]

[0020] Among them, SOH k represents the SOH of the cell at the kth static point, g represents the empirical decay model of SOH, Ω is the parameter set to be estimated for the empirical decay model, Q0 is the cumulative charge of the cell from the initial commissioning to the start of the calculation, X k Calculating SOH for the empirical decay model k The required operating data, C0 is the nominal capacity of the battery cell, C k is the remaining available capacity of the battery cell at the kth static point;

[0021] Establish an SOC evaluation model and use the ampere-hour integration method to calculate the battery SOC. The calculation formula is:

[0022]

[0023] Among them, SOC k-1 and SOC k are the SOC corresponding to the k-1th and kth static points, t k-1 and t k are the moments corresponding to the k-1th and kth static points, I t is the cluster current at time t, C k-1 is the remaining available capacity of the battery corresponding to the k-1th static point, and C k-1 =C0·SOH k-1 ;

[0024] Get the SOC correction value at the rest point based on the cell rest voltage and SOC-OCV curve:

[0025] SOC k′ =f -1 (OCV k )

[0026] Among them, OCV k is the single cell voltage at the kth static point;

[0027] Construct the objective function of dynamic SOC-OCV and SOC-SOH joint estimation to minimize the SOC change of the battery before and after calibration:

[0028]

[0029] Where SOC0 and Q0 are the cell SOC and cumulative charge at the start of the calculation, respectively. SOC0, Q0, the parameter set Φ to be estimated for the dynamic SOC-OCV curve, and the parameter set Ω to be estimated for the empirical decay model are the variables to be optimized.

[0030] Based on the selected static operating points and optimization equations, the sequential least squares quadratic programming method is used to optimize the parameters.

[0031] As a preferred solution of the battery SOC-SOH joint evaluation method, the function of updating the dynamic SOC-OCV curve and the parameters of the SOH empirical decay model according to the parameter optimization results to calculate the subsequent SOC and SOH of the energy storage battery specifically includes:

[0032] According to the parameters obtained by the sequential least squares quadratic programming method, the SOH of the battery at any subsequent time t is evaluated:

[0033]

[0034] Among them, X t It is the temperature and voltage operating data;

[0035] According to the parameters obtained by the sequential least squares quadratic programming method, the SOC of the battery at any subsequent time t is evaluated:

[0036]

[0037] Among them, SOC i-1 is the SOC at the subsequent i-1th static point, C t is the available capacity of the battery cell at time t, and C t =C0·SOH t .

[0038] As the preferred solution for the battery SOC-SOH joint evaluation method, the SOH empirical decay model uses C t =(1-B(Q t ) n )×C0, where B and n are parameters to be optimized, and Q t is the current charge and discharge capacity; Q t Together with the initial cumulative charge capacity Q0, it constitutes the total charge and discharge capacity, which is used to calculate the degree of battery capacity attenuation.

[0039] As a preferred solution of the battery SOC-SOH joint evaluation method, the monitoring of the SOC calibration value jump at the subsequent static point, if the jump exceeds the preset jump value threshold for a set number of consecutive times, repeat steps S1, S2, and S3 to restart the dynamic OCV estimation, specifically including:

[0040] The dynamic SOC-OCV curve parameter set Φ obtained by parameter optimization estimates the SOC correction value at the subsequent static point: SOC i′ =f -1 (OCV i ), where OCV iis the cell voltage at the subsequent i-th static point, and f is the dynamic SOC-OCV curve;

[0041] Calculate the jump amount of the SOC correction value at the subsequent static point: ΔSOC i =|SOC i -SOC i′ |, if ΔSOC i If the preset jump value threshold Y is exceeded, steps S1, S2, and S3 are repeated to restart the dynamic OCV estimation; the preset jump value threshold Y is set according to the battery model and operating conditions.

[0042] The present invention also provides a battery SOC-SOH joint evaluation device, comprising:

[0043] The operating point screening module is used to judge and screen the static operating point of the energy storage battery SOC in the non-plateau period based on the actual operating conditions of the energy storage battery;

[0044] The model building and parameter optimization module is used to construct the function of the dynamic SOC-OCV curve and the SOH empirical decay model, and to build an optimization equation based on the goal of minimizing the SOC jump at the calibration point for parameter optimization;

[0045] An SOC and SOH calculation module, configured to update the function of the dynamic SOC-OCV curve and the parameters of the SOH empirical decay model according to the parameter optimization result, and calculate the subsequent SOC and SOH of the energy storage battery;

[0046] The monitoring and restart module is used to monitor the jump of the SOC calibration value at the subsequent static point. If the jump exceeds the preset jump value threshold for a set number of consecutive times, the dynamic OCV estimation is restarted.

[0047] As a preferred solution for the battery SOC-SOH joint evaluation device, the operating point screening module includes:

[0048] The data acquisition and preliminary screening unit is used to collect the battery current and voltage change curves over time, and screen the operating conditions where the cluster current is less than the static current threshold σ and the duration is greater than 15 minutes, and the cell voltage change rate before the static end time is less than the threshold ζ. σ is set according to the battery capacity, and ζ is set according to the battery capacity and voltage acquisition accuracy.

[0049] The non-plateau screening unit is used to estimate the battery's state of charge (SOC) at the time of rest based on the cell voltage corresponding to the screened resting operating point, combined with the battery's initial SOC-OCV curve, and screen the SOC for standby use at the non-plateau operating point based on the condition that the battery SOC is greater than a threshold α or less than a threshold β; wherein α and β are set according to the inflection points at both ends of the battery SOC-OCV curve.

[0050] As a preferred solution for the battery SOC-SOH joint evaluation device, the model building and parameter optimization module includes:

[0051] Dynamic SOC-OCV function establishment unit, used to establish the function expression of the dynamic SOC-OCV curve:

[0052] OCV=f(SOC;Φ)

[0053] Where Φ is the parameter set to be estimated for the dynamic SOC-OCV curve;

[0054] The SOH experience decay model establishment unit is used to select the SOH experience decay model. The expression is:

[0055]

[0056] Among them, SOH k represents the SOH of the cell at the kth static point, g represents the empirical decay model of SOH, Ω is the parameter set to be estimated for the empirical decay model, Q0 is the cumulative charge of the cell from the initial commissioning to the start of the calculation, X k Calculating SOH for the empirical decay model k The required operating data, C0 is the nominal capacity of the battery cell, C k is the remaining available capacity of the battery cell at the kth static point;

[0057] The SOC evaluation model establishment unit is used to establish the SOC evaluation model and calculate the battery SOC using the ampere-hour integration method. The calculation formula is:

[0058]

[0059] Among them, SOC k-1 and SOC k are the SOC corresponding to the k-1th and kth static points, t k-1 and t k are the moments corresponding to the k-1th and kth static points, I t is the cluster current at time t, C k-1 is the remaining available capacity of the battery corresponding to the k-1th static point, and C k-1 =C0·SOH k-1 ;

[0060] The SOC correction value acquisition unit is used to obtain the SOC correction value at the static point based on the cell static voltage and the SOC-OCV curve:

[0061] SOC k′ =f -1 (OCV k )

[0062] Among them, OCV k is the single cell voltage at the kth static point;

[0063] The objective function construction unit is used to construct the objective function of dynamic SOC-OCV and SOC-SOH joint estimation to minimize the SOC change of the battery before and after calibration:

[0064]

[0065] Where SOC0 and Q0 are the cell SOC and cumulative charge at the start of the calculation, respectively. SOC0, Q0, the parameter set Φ to be estimated for the dynamic SOC-OCV curve, and the parameter set Ω to be estimated for the empirical decay model are the variables to be optimized.

[0066] The parameter optimization unit is used to optimize the parameters based on the screened static operating points and optimization equations using the sequential least squares quadratic programming method.

[0067] As a preferred solution of the battery SOC-SOH joint evaluation device, the SOC and SOH calculation module includes:

[0068] The subsequent SOH evaluation unit is used to evaluate the SOH of the battery at any subsequent time t based on the parameters obtained by the sequential least squares quadratic programming method:

[0069]

[0070] Among them, X t It is the temperature and voltage operating data;

[0071] The subsequent SOC evaluation unit is used to evaluate the SOC of the battery at any subsequent time t based on the parameters obtained by the sequential least squares quadratic programming method:

[0072]

[0073] Among them, SOC i-1 is the SOC at the subsequent i-1th static point, C t is the available capacity of the battery cell at time t, and C t =C0·SOH t ;

[0074] The monitoring and restart module includes:

[0075] The SOC correction value estimation unit is used to estimate the SOC correction value at the subsequent static point based on the dynamic SOC-OCV curve parameter set Φ obtained by parameter optimization: SOC i′ =f -1 (OCV i ), where OCV iis the cell voltage at the subsequent i-th static point, and f is the dynamic SOC-OCV curve;

[0076] Jump amount calculation and judgment unit, used to calculate the jump amount of the SOC correction value at the subsequent static point: ΔSOC i =|SOC i -SOC i′ |, if ΔSOC i If the preset jump value threshold Y is exceeded, the dynamic OCV estimation is restarted; the preset jump value threshold Y is set according to the battery model and operating conditions.

[0077] The present invention has the following advantages:

[0078] First, by screening non-plateau static operating points and dynamically updating the SOC-OCV curve, the aging pattern of the battery in actual operation can be accurately reflected, solving the problem that traditional methods are difficult to capture the aging path under complex working conditions.

[0079] Second, it does not need to rely on a large amount of historical data and is suitable for online evaluation of new and aging batteries. It combines the ampere-hour integration method with the parameter optimization mechanism to achieve real-time SOC-SOH joint calculation under dynamic working conditions.

[0080] Third, by optimizing the parameters of the empirical decay model and optimization equation, we can adapt to the aging characteristics of different types of batteries throughout their life cycle, and solve the difficulties in obtaining parameters of traditional physical models and the limitations of data-driven models that rely on large data sets.

[0081] Fourth, by monitoring the SOC jump value to trigger the model restart, the parameters are continuously updated to match the battery aging process, avoiding assessment deviations caused by capacity attenuation or sudden changes in operating conditions, and improving the reliability of the full life cycle assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0083] Figure 1 This is a flow chart of a battery SOC-SOH combined evaluation method provided in an embodiment of the present invention;

[0084] Figure 2 Schematic diagram of the battery SOC-SOH joint evaluation device architecture provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0085] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0086] Example 1

[0087] See also Figure 1 , Embodiment 1 of the present invention provides a battery SOC-SOH joint evaluation method, comprising the following steps:

[0088] S1. Based on the actual operating conditions of the energy storage battery, determine and select the static operating point of the energy storage battery SOC in the non-plateau period;

[0089] Specifically, when the battery is at rest, its voltage tends to be stable, making the relationship between OCV and SOC more accurate. The slope of the OCV-SOC curve during non-plateau periods (high or low SOC ranges) is steep, allowing small voltage changes to reflect significant SOC changes, facilitating accurate calibration. The screening criteria must be low current (close to rest) and stable voltage (low rate of change) to ensure data reliability.

[0090] S2. Construct a function of the dynamic SOC-OCV curve and an empirical SOH attenuation model, and construct an optimization equation based on the goal of minimizing the SOC jump at the calibration point to perform parameter optimization.

[0091] Specifically, battery aging will cause the OCV-SOC relationship to change. The dynamic SOC-OCV curve is described by the function OCV=f(SOC;Φ). The curve is dynamically updated by the parameter Φ to adapt to the aging process. The SOH empirical decay model is based on the cumulative charge and discharge capacity, temperature and other data, and uses empirical formulas to fit the capacity decay law, such as SOH k =C k / C0, where C k Calculated by the aging model g, it reflects the degradation of the battery's state of health with usage. SOC changes are calculated by integrating the current, but initial errors accumulate and require OCV correction. Aiming to minimize the SOC jump before and after calibration, the initial SOC (SOC0), accumulated charge (Q0), and model parameters (Φ, Ω) are optimized to align the model output with actual voltage data, improving assessment accuracy.

[0092] S3. Update the function of the dynamic SOC-OCV curve and the parameters of the SOH empirical decay model according to the parameter optimization result, and calculate the subsequent SOC and SOH of the energy storage battery;

[0093] Specifically, after the optimized parameters (such as Φ, Ω) are updated, the SOC-OCV curve and SOH model can more accurately reflect the current battery status. Subsequent SOC calculation combines the ampere-hour integral and the real-time SOH (SOH t ), where the available capacity C t =C0·SOH t , dynamically adjusted as the battery ages to ensure the accuracy of the integral benchmark.

[0094] S4. Monitor the jump of the SOC calibration value at the subsequent static point. If the jump exceeds the preset jump value threshold for a set number of consecutive times, repeat steps S1, S2, and S3 to restart the dynamic OCV estimation;

[0095] Specifically, when battery aging worsens or operating conditions change suddenly, the SOC-OCV curve will drift significantly, causing the calibration value jump (ΔSOC) to increase. By monitoring whether ΔSOC exceeds the preset jump value threshold Y, the model is determined to be invalid. If it exceeds the limit multiple times in a row, the process is restarted to achieve dynamic model updates and maintain assessment accuracy.

[0096] In a possible embodiment, in step S1, judging and screening the non-plateau static operating point of the SOC of the energy storage battery based on the actual operating condition of the energy storage battery specifically includes:

[0097] S11. Collect the battery current and voltage change curves over time, and select the operating conditions where the cluster current is less than the static current threshold σ and the duration is greater than 15 minutes, and the cell voltage change rate before the static end time is less than the threshold ζ. σ is set according to the battery capacity, and ζ is set according to the battery capacity and voltage collection accuracy.

[0098] S12. Estimate the state of charge (SOC) of the battery at the time of rest based on the cell voltage corresponding to the selected resting operating point and the initial SOC-OCV curve of the battery. Select a standby SOC operating point in a non-plateau period based on the condition that the battery SOC is greater than a threshold value α or less than a threshold value β. α and β are set according to the inflection points at both ends of the battery SOC-OCV curve.

[0099] Specifically, the current threshold σ ensures that the battery is in an approximately open circuit state (no significant charge or discharge) for more than 15 minutes so that the polarization effect is fully attenuated and the voltage tends to stabilize. The voltage change rate threshold ζ (such as 2mV / 5min) is used to determine whether the static state is sufficient to avoid errors introduced by voltage data that has not reached equilibrium. The parameters are adjusted according to the battery characteristics (capacity) and acquisition accuracy to ensure the effectiveness of the screening. The voltage changes slowly with the SOC during the SOC plateau period (such as the 30%-70% range) and is difficult to calibrate accurately. Non-plateau operating points are screened by α (such as 95%) and β (such as 10%), and the high slope characteristics at both ends of the curve are used to make small changes in voltage correspond to significant SOC changes, thereby improving calibration accuracy.

[0100] In this embodiment, in step S2, the function of constructing the dynamic SOC-OCV curve and the SOH empirical decay model are constructed, and an optimization equation based on minimizing the SOC jump at the calibration point is constructed to perform parameter optimization, specifically including:

[0101] S21. Establish the function expression of the dynamic SOC-OCV curve:

[0102] OCV=f(SOC;Φ)

[0103] Where Φ is the parameter set to be estimated for the dynamic SOC-OCV curve; the function f is typically a polynomial or lookup table model, and the parameters Φ (e.g., polynomial coefficients) are fitted using historical data and iteratively updated as the battery ages, ensuring that the mapping between OCV and SOC matches the current state in real time.

[0104] S22. Select the SOH empirical decay model, the expression is:

[0105]

[0106] Among them, SOH k represents the SOH of the cell at the kth static point, g represents the empirical decay model of SOH, Ω is the parameter set to be estimated for the empirical decay model, Q0 is the cumulative charge of the cell from the initial commissioning to the start of the calculation, X k Calculating SOH for the empirical decay model k The required operating data, C0 is the nominal capacity of the battery cell, C k is the remaining available capacity of the cell at the kth rest point; the empirical model g (such as exponential decay, power function) is based on the cumulative charge capacity Q0 and the operating data X k (temperature, voltage, etc.), quantify the degree of capacity attenuation. Parameters such as the attenuation coefficient are determined by optimization, so that the SOH predicted by the model is k It is consistent with the actual capacity test value.

[0107] S23. Establish an SOC evaluation model and use the ampere-hour integration method to calculate the battery SOC. The calculation formula is:

[0108]

[0109] Among them, SOC k-1 and SOC k are the SOC corresponding to the k-1th and kth static points, t k-1 and t k are the moments corresponding to the k-1th and kth static points, I t is the cluster current at time t, C k-1 is the remaining available capacity of the battery corresponding to the k-1th static point, and C k-1 =C0·SOH k-1 The ampere-hour integral calculates the change in charge by accumulating current, but it depends on the accurate available capacity C. k-1 Combined with SOH k-1 Dynamic Adjustment C k-1 , which solves the error accumulation problem caused by the traditional integration method not considering capacity attenuation.

[0110] S24. Obtain the SOC correction value at the static point based on the cell static voltage and the SOC-OCV curve:

[0111] SOC k′ =f -1 (OCV k )

[0112] Among them, OCV k is the cell voltage at the kth static point; the inverse function f of the dynamic SOC-OCV curve is used -1 , the measured static voltage OCV k Converted to the corrected SOC value SOC k′ , correct the cumulative error of ampere-hour integration, and form a dual verification mechanism of integral calculation and voltage correction.

[0113] S25. Construct the objective function for the joint estimation of dynamic SOC-OCV and SOC-SOH to minimize the SOC change of the battery before and after calibration:

[0114]

[0115] Where SOC0 and Q0 are the battery cell SOC and cumulative charge at the start of the calculation, respectively. SOC0, Q0, the parameter set Φ to be estimated for the dynamic SOC-OCV curve, and the parameter set Ω to be estimated for the empirical decay model are the variables to be optimized. The objective function is the SOC calculated value (SOC k ) and correction value (SOC k′) is minimized, and by optimizing the initial parameters (SOC0, Q0) and model parameters (Φ, Ω), the model output is optimally fitted with the actual data, thereby improving the consistency of the joint evaluation.

[0116] S26. Based on the selected static operating points and optimization equations, a sequential least squares quadratic programming method is used to optimize the parameters. This method iteratively solves local quadratic programming subproblems, gradually approaching the optimal parameter solution. It effectively handles constrained nonlinear optimization problems and ensures that model parameters quickly converge to the optimal solution under dynamic conditions.

[0117] In a possible embodiment, in step S3, updating the function of the dynamic SOC-OCV curve and the parameters of the SOH empirical decay model according to the parameter optimization result to calculate the subsequent SOC and SOH of the energy storage battery specifically includes:

[0118] S31. Evaluate the SOH of the battery at any subsequent time t based on the parameters obtained by the sequential least squares quadratic programming method:

[0119]

[0120] Among them, X t is the temperature and voltage operating data; the updated parameter Ω enables the experience decay model g to more accurately predict the current operating data X t (such as real-time temperature, voltage) under the capacity attenuation, so as to obtain the current SOH t , reflecting the real-time changes in battery health status

[0121] S32. Evaluate the SOC of the battery at any subsequent time t based on the parameters obtained by the sequential least squares quadratic programming method:

[0122]

[0123] Among them, SOC i-1 is the SOC at the subsequent i-1th static point, C t is the available capacity of the battery cell at time t, and C t =C0·SOH t Available capacity C t Follow SOH t Dynamic adjustment ensures that the baseline value of the ampere-hour integral is consistent with the current actual capacity of the battery, avoiding SOC calculation deviation caused by capacity decay and achieving high-precision SOC tracking throughout the entire life cycle.

[0124] In this embodiment, in step S4, the SOC calibration value jumps at the subsequent static point are monitored. If the jump exceeds the preset jump value threshold for a set number of consecutive times, steps S1, S2, and S3 are repeated to restart the dynamic OCV estimation, specifically including:

[0125] S41, the dynamic SOC-OCV curve parameter set Φ obtained by parameter optimization is used to estimate the SOC correction value at the subsequent static point: SOC i′ =f -1 (OCV i ), where OCV i is the cell voltage at the subsequent i-th static point, f is the dynamic SOC-OCV curve; the SOC-OCV curve updated by the latest parameter Φ is used to calculate the subsequent static voltage OCV i Perform back calculation to obtain the corrected SOC i′ , used to verify the accuracy of the model in real time.

[0126] S42, calculating the jump amount of the SOC correction value at the subsequent static point: ΔSOC i =|SOC i -SOC i ′|, if ΔSOC i If the preset jump value threshold Y is exceeded, steps S1, S2, and S3 are repeated to restart the dynamic OCV estimation; the preset jump value threshold Y is set according to the battery model and operating conditions. Jump value ΔSOC i Reflects the deviation between the model prediction value and the actual correction value. When the deviation exceeds the threshold Y and occurs continuously, it indicates that the battery aging or operating condition changes have exceeded the adaptation range of the current model. The process needs to be restarted and re-modeled to ensure the adaptability of the evaluation system.

[0127] In a possible embodiment, the SOH empirical decay model uses C t =(1-B(Q t ) n )×C0, where B and n are parameters to be optimized, and Q t is the current charge and discharge capacity; Q t Together with the initial cumulative charge capacity Q0, it constitutes the total charge and discharge capacity, which is used to calculate the battery capacity attenuation degree. This model is based on the power function attenuation law, Q t The sum of (current charge and discharge amount) and Q0 (initial cumulative amount) reflects the total number of battery cycles. The parameters B and n are determined by optimization to determine the decay rate and curve shape, which can better fit the capacity decay characteristics of most batteries, such as the power-law decay law of lithium-ion batteries.

[0128] In one possible embodiment, the steps of the sequential least squares quadratic programming method are as follows:

[0129] 1. Initialization: Set the initial guess solution x0, the convergence tolerance ∈, and the maximum number of iterations.

[0130] 2. Iteration loop:

[0131] For each step k:

[0132] Calculate gradients and constraints: Calculate the objective function f(x k ) and the Jacobian matrix of the constraints (equalities, inequalities).

[0133] Build a local quadratic model: Use the second-order approximation of the objective function at the current point (such as BFGS to update the Hessian matrix) and linearized constraints to construct a quadratic programming (QP) subproblem.

[0134] Solve the QP subproblem: Under the local model, find the steepest descent direction p that satisfies the linearization constraints k This step handles activity constraints via the active set strategy.

[0135] Line search determines the step size: along direction p k Perform a one-dimensional search and select a step size α so that the new point x k+1 =x k +αp k The objective function is reduced and the constraints are satisfied.

[0136] Update and check convergence: If the gradient norm, constraint violation, or step size change is less than ∈, or the maximum number of iterations is reached, terminate; otherwise, continue iterating.

[0137] 3. Output result: Return the optimal solution x * , objective function value and convergence status.

[0138] The application scenarios of the present invention are as follows:

[0139] Scenario 1: Electric Vehicle Battery Management System (BMS):

[0140] When driving, electric vehicles need to know the remaining battery capacity (SOC) in real time to display the range, and monitor the battery health state (SOH) to assess life degradation to avoid false range or safety hazards caused by battery aging.

[0141] The present invention screens the vehicle's static operating points (such as after being parked for a long time), uses the dynamic SOC-OCV curve and the SOH empirical model, and combines the current integration and voltage correction during driving to achieve high-precision SOC tracking. For example, when the vehicle is stationary after high-speed driving, the system automatically screens the operating points that meet the conditions, updates the SOC-OCV curve, and corrects the integration error caused by high-speed discharge. The battery capacity attenuation is evaluated based on the SOH model to provide a basis for battery balancing strategy and charging cut-off voltage setting. For example, when the SOH is lower than 80%, the system automatically adjusts the charging upper limit to 90% to slow down the aging rate. Compared with the traditional ampere-hour integration method, the present invention solves the SOC drift problem under complex operating conditions (sudden acceleration, climbing, etc.) of electric vehicles through dynamic parameter optimization and jump monitoring, and the error can be controlled within ±3%.

[0142] Scenario 2: Grid-level energy storage power station

[0143] Large-scale energy storage power stations need to manage thousands of batteries, accurately assess the SOC to schedule charging and discharging power, monitor the SOH to predict replacement cycles, and reduce operation and maintenance costs.

[0144] The present invention is used to screen the non-plateau static points of the energy storage battery (such as the low load period of the power grid in the early morning every day), and the SOC-OCV curve and SOH model parameters are optimized by the sequential least squares quadratic programming method to achieve consistency evaluation of grouped batteries. For example, the SOH extreme value within the cluster is calculated based on the SOH evaluation result of the single cell, and the battery cell with low SOH is located and an alarm is given. The remaining number of battery cycles is predicted in combination with the SOH model to provide data support for the decommissioning strategy of the power station. For example, when the average SOH of the batteries in the entire station is lower than 85%, a batch replacement plan is initiated. The present invention can adapt to the long-term high-current charging and discharging conditions of the energy storage power station, and solves the problem of accuracy attenuation of the traditional physical model at high temperature and high rate by dynamically updating the model parameters.

[0145] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario and completed by multiple devices working together. In this distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the battery SOC-SOH joint assessment method.

[0146] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0147] Example 2

[0148] See also Figure 2 , Embodiment 2 of the present invention further provides a battery SOC-SOH joint evaluation device, comprising:

[0149] The operating point screening module 100 is used to determine and screen the stationary operating point of the energy storage battery SOC in the non-plateau period based on the actual operating conditions of the energy storage battery;

[0150] The model building and parameter optimization module 200 is used to build a function of the dynamic SOC-OCV curve and the SOH empirical decay model, and to build an optimization equation based on the goal of minimizing the SOC jump at the calibration point for parameter optimization;

[0151] The SOC and SOH calculation module 300 is used to update the function of the dynamic SOC-OCV curve and the parameters of the SOH empirical decay model according to the parameter optimization result, and calculate the subsequent SOC and SOH of the energy storage battery;

[0152] The monitoring and restart module 400 is used to monitor the jump of the SOC calibration value at the subsequent static point, and restart the dynamic OCV estimation if the jump exceeds the preset jump value threshold for a set number of consecutive times.

[0153] In this embodiment, the operating point screening module 100 includes:

[0154] The data collection and preliminary screening unit 101 is used to collect the battery current and voltage change curves over time, and screen the operating conditions where the cluster current is less than the static current threshold σ and the duration is greater than 15 minutes, and the cell voltage change rate before the static end time is less than the threshold ζ. Wherein, σ is set according to the battery capacity, and ζ is set according to the battery capacity and voltage collection accuracy.

[0155] The non-plateau screening unit 102 is used to estimate the battery's state of charge (SOC) at the time of rest based on the cell voltage corresponding to the screened resting operating point and in combination with the battery's initial SOC-OCV curve, and screen the SOC for standby at the non-plateau operating point based on the condition that the battery SOC is greater than a threshold α or less than a threshold β; wherein α and β are set according to the inflection points at both ends of the battery SOC-OCV curve.

[0156] In this embodiment, the model building and parameter optimization module 200 includes:

[0157] The dynamic SOC-OCV function establishing unit 201 is used to establish a function expression of the dynamic SOC-OCV curve:

[0158] OCV=f(SOC;Φ)

[0159] Where Φ is the parameter set to be estimated for the dynamic SOC-OCV curve;

[0160] The SOH experience decay model establishment unit 202 is used to select the SOH experience decay model, which is expressed as:

[0161]

[0162] Among them, SOH k represents the SOH of the cell at the kth static point, g represents the empirical decay model of SOH, Ω is the parameter set to be estimated for the empirical decay model, Q0 is the cumulative charge of the cell from the initial commissioning to the start of the calculation, X k Calculating SOH for the empirical decay model k The required operating data, C0 is the nominal capacity of the battery cell, C k is the remaining available capacity of the battery cell at the kth static point;

[0163] The SOC evaluation model establishment unit 203 is used to establish an SOC evaluation model and calculate the battery SOC using the ampere-hour integration method. The calculation formula is:

[0164]

[0165] Among them, SOC k-1 and SOC k are the SOC corresponding to the k-1th and kth static points, t k-1 and t k are the moments corresponding to the k-1th and kth static points, I t is the cluster current at time t, C k-1 is the remaining available capacity of the battery corresponding to the k-1th static point, and C k-1 =C0·SOH k-1 ;

[0166] The SOC correction value acquisition unit 204 is used to acquire the SOC correction value at the rest point based on the cell rest voltage and the SOC-OCV curve:

[0167] SOC k′ =f -1 (OCV k)

[0168] Among them, OCV k is the single cell voltage at the kth static point;

[0169] The objective function construction unit 205 is used to construct an objective function for the dynamic SOC-OCV and SOC-SOH joint estimation to minimize the SOC change of the battery before and after calibration:

[0170]

[0171] Where SOC0 and Q0 are the cell SOC and cumulative charge at the start of the calculation, respectively. SOC0, Q0, the parameter set Φ to be estimated for the dynamic SOC-OCV curve, and the parameter set Ω to be estimated for the empirical decay model are the variables to be optimized.

[0172] The parameter optimization unit 206 is used to optimize the parameters using a sequential least squares quadratic programming method based on the screened static operating points and the optimization equation.

[0173] In this embodiment, the SOC and SOH calculation module 300 includes:

[0174] The subsequent SOH evaluation unit 301 is used to evaluate the SOH of the battery at any subsequent time t according to the parameters obtained by the sequential least squares quadratic programming method:

[0175]

[0176] Among them, X t It is the temperature and voltage operating data;

[0177] The subsequent SOC evaluation unit 302 is used to evaluate the SOC of the battery at any subsequent time t according to the parameters obtained by the sequential least squares quadratic programming method:

[0178]

[0179] Among them, SOC i-1 is the SOC at the subsequent i-1th static point, C t is the available capacity of the battery cell at time t, and C t =C0·SOH t ;

[0180] The monitoring and restarting module 400 includes:

[0181] The SOC correction value estimation unit 401 is used to estimate the SOC correction value at the subsequent static point based on the dynamic SOC-OCV curve parameter set Φ obtained by parameter optimization: SOC i′ =f -1 (OCV i), where OCV i is the cell voltage at the subsequent i-th static point, and f is the dynamic SOC-OCV curve;

[0182] The jump amount calculation and judgment unit 402 is used to calculate the jump amount of the SOC correction value at the subsequent static point: ΔSOC i =|SOC i -SOC i′ |, if ΔSOC i If the preset jump value threshold Y is exceeded, the dynamic OCV estimation is restarted; the preset jump value threshold Y is set according to the battery model and operating conditions.

[0183] It should be noted that the information interaction, execution process, etc. between the above-mentioned device modules are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and will not be repeated here.

[0184] Example 3

[0185] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code of a battery SOC-SOH joint evaluation method is stored. The program code includes instructions for executing the battery SOC-SOH joint evaluation method of embodiment 1 or any possible implementation thereof.

[0186] Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0187] Example 4

[0188] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0189] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the battery SOC-SOH joint evaluation method of Example 1 or any possible implementation thereof.

[0190] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in a memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0191] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0192] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computing system, and thus, they can be stored in a storage system and executed by the computing system. In some cases, the steps shown or described herein can be performed in a different order than that shown, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0193] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A battery SOC-SOH joint evaluation method, characterized in that: The following steps are involved: S1. Based on the actual operating conditions of the energy storage battery, determine and select the static operating point of the energy storage battery SOC in the non-plateau period; S2. Construct a function of the dynamic SOC-OCV curve and an empirical SOH attenuation model, and construct an optimization equation based on the goal of minimizing the SOC jump at the calibration point to perform parameter optimization. S3. Update the function of the dynamic SOC-OCV curve and the parameters of the SOH empirical decay model according to the parameter optimization result, and calculate the subsequent SOC and SOH of the energy storage battery; S4. Monitor the jump of the SOC calibration value at the subsequent static point. If the jump exceeds the preset jump value threshold for a set number of consecutive times, repeat steps S1, S2, and S3 to restart the dynamic OCV estimation.

2. The battery SOC-SOH joint evaluation method according to claim 1, characterized in that: The determining and screening of the non-plateau static operating point of the SOC of the energy storage battery based on the actual operating condition of the energy storage battery specifically includes: Collect the battery current and voltage curves over time, and select the operating conditions where the cluster current is less than the static current threshold σ and the duration is greater than 15 minutes, and the cell voltage change rate before the static end time is less than the threshold ζ. σ is set according to the battery capacity, and ζ is set according to the battery capacity and voltage collection accuracy. Based on the cell voltage corresponding to the selected static operating point and combined with the battery's initial SOC-OCV curve, the battery's state of charge (SOC) at the time of rest is estimated. Based on the condition that the battery SOC is greater than a threshold value α or less than a threshold value β, a standby SOC operating point in the non-plateau period is selected; wherein α and β are set according to the inflection points at both ends of the battery SOC-OCV curve.

3. The battery SOC-SOH joint evaluation method according to claim 1, characterized in that: The function of constructing the dynamic SOC-OCV curve and the SOH empirical decay model, and the optimization equation based on minimizing the SOC jump at the calibration point as the goal to perform parameter optimization, specifically include: Establish the function expression of dynamic SOC-OCV curve: OCV=f(SOC;Φ) Where Φ is the parameter set to be estimated for the dynamic SOC-OCV curve; Select the SOH empirical decay model, the expression is: Among them, SOH k represents the SOH of the cell at the kth static point, g represents the empirical decay model of SOH, Ω is the parameter set to be estimated for the empirical decay model, Q0 is the cumulative charge of the cell from the initial commissioning to the start of the calculation, X k Calculating SOH for the empirical decay model k The required operating data, C0 is the nominal capacity of the battery cell, C k is the remaining available capacity of the battery cell at the kth static point; Establish an SOC evaluation model and use the ampere-hour integration method to calculate the battery SOC. The calculation formula is: Among them, SOC k-1 and SOC k are the SOC corresponding to the k-1th and kth static points, t k-1 and t k are the moments corresponding to the k-1th and kth static points, I t is the cluster current at time t, C k-1 is the remaining available capacity of the battery corresponding to the k-1th static point, and C k-1 =C0·SOH k-1 ; Get the SOC correction value at the rest point based on the cell rest voltage and SOC-OCV curve: SOC k′ =f -1 (OCV k ) Among them, OCV k is the single cell voltage at the kth static point; Construct the objective function of dynamic SOC-OCV and SOC-SOH joint estimation to minimize the SOC change of the battery before and after calibration: Where SOC0 and Q0 are the cell SOC and cumulative charge at the start of the calculation, respectively. SOC0, Q0, the parameter set Φ to be estimated for the dynamic SOC-OCV curve, and the parameter set Ω to be estimated for the empirical decay model are the variables to be optimized. Based on the selected static operating points and optimization equations, the sequential least squares quadratic programming method is used to optimize the parameters.

4. The battery SOC-SOH joint evaluation method according to claim 3, characterized in that: The function of updating the dynamic SOC-OCV curve and the parameters of the SOH empirical decay model according to the parameter optimization result, and calculating the subsequent SOC and SOH of the energy storage battery, specifically includes: According to the parameters obtained by the sequential least squares quadratic programming method, the SOH of the battery at any subsequent time t is evaluated: Among them, X t It is the temperature and voltage operating data; According to the parameters obtained by the sequential least squares quadratic programming method, the SOC of the battery at any subsequent time t is evaluated: Among them, SOC i-1 is the SOC at the subsequent i-1th static point, C t is the available capacity of the battery cell at time t, and C t =C0·SOH t .

5. The battery SOC-SOH joint evaluation method according to claim 3, characterized in that: The SOH empirical decay model uses C t =(1-B(Q t ) n )×C0, where B and n are parameters to be optimized, and Q t is the current charge and discharge capacity; Q t Together with the initial cumulative charge capacity Q0, it constitutes the total charge and discharge capacity, which is used to calculate the degree of battery capacity attenuation.

6. The battery SOC-SOH joint evaluation method according to claim 1, characterized in that: The monitoring of the SOC calibration value jump at the subsequent static point, if the jump exceeds the preset jump value threshold for a set number of consecutive times, repeating steps S1, S2, and S3 to restart the dynamic OCV estimation, specifically includes: The dynamic SOC-OCV curve parameter set Φ obtained by parameter optimization estimates the SOC correction value at the subsequent static point: SOC i′ =f -1 (OCV i ), where OCV i is the cell voltage at the subsequent i-th static point, and f is the dynamic SOC-OCV curve; Calculate the jump amount of the SOC correction value at the subsequent static point: ΔSOC i =|SOC i -SOC i′ |, if ΔSOC i If the preset jump value threshold Y is exceeded, steps S1, S2, and S3 are repeated to restart the dynamic OCV estimation; the preset jump value threshold Y is set according to the battery model and operating conditions.

7. A battery SOC-SOH joint evaluation device, characterized in that: include: The operating point screening module is used to judge and screen the static operating point of the energy storage battery SOC in the non-plateau period based on the actual operating conditions of the energy storage battery; The model building and parameter optimization module is used to construct the function of the dynamic SOC-OCV curve and the SOH empirical decay model, and to build an optimization equation based on the goal of minimizing the SOC jump at the calibration point for parameter optimization; An SOC and SOH calculation module, configured to update the function of the dynamic SOC-OCV curve and the parameters of the SOH empirical decay model according to the parameter optimization result, and calculate the subsequent SOC and SOH of the energy storage battery; The monitoring and restart module is used to monitor the jump of the SOC calibration value at the subsequent static point. If the jump exceeds the preset jump value threshold for a set number of consecutive times, the dynamic OCV estimation is restarted.

8. The battery SOC-SOH joint evaluation device according to claim 7, characterized in that: The operating point screening module includes: The data acquisition and preliminary screening unit is used to collect the battery current and voltage change curves over time, and screen the operating conditions where the cluster current is less than the static current threshold σ and the duration is greater than 15 minutes, and the cell voltage change rate before the static end time is less than the threshold ζ. σ is set according to the battery capacity, and ζ is set according to the battery capacity and voltage acquisition accuracy. The non-plateau screening unit is used to estimate the battery's state of charge (SOC) at the time of rest based on the cell voltage corresponding to the screened resting operating point, combined with the battery's initial SOC-OCV curve, and screen the SOC for standby use at the non-plateau operating point based on the condition that the battery SOC is greater than a threshold α or less than a threshold β; wherein α and β are set according to the inflection points at both ends of the battery SOC-OCV curve.

9. The battery SOC-SOH joint evaluation device according to claim 8, characterized in that: The model building and parameter optimization module includes: Dynamic SOC-OCV function establishment unit, used to establish the function expression of the dynamic SOC-OCV curve: OCV=f(SOC;Φ) Where Φ is the parameter set to be estimated for the dynamic SOC-OCV curve; The SOH experience decay model establishment unit is used to select the SOH experience decay model. The expression is: Among them, SOH k represents the SOH of the cell at the kth static point, g represents the empirical decay model of SOH, Ω is the parameter set to be estimated for the empirical decay model, Q0 is the cumulative charge of the cell from the initial commissioning to the start of the calculation, X k Calculating SOH for the empirical decay model k The required operating data, C0 is the nominal capacity of the battery cell, C k is the remaining available capacity of the battery cell at the kth static point; The SOC evaluation model establishment unit is used to establish the SOC evaluation model and calculate the battery SOC using the ampere-hour integration method. The calculation formula is: Among them, SOC k-1 and SOC k are the SOC corresponding to the k-1th and kth static points, t k-1 and t k are the moments corresponding to the k-1th and kth static points, I t is the cluster current at time t, C k-1 is the remaining available capacity of the battery corresponding to the k-1th static point, and C k-1 =C0·SOH k-1 ; The SOC correction value acquisition unit is used to obtain the SOC correction value at the static point based on the cell static voltage and the SOC-OCV curve: SOC k′ =f -1 (OCV k ) Among them, OCV k is the single cell voltage at the kth static point; The objective function construction unit is used to construct the objective function of dynamic SOC-OCV and SOC-SOH joint estimation to minimize the SOC change of the battery before and after calibration: Where SOC0 and Q0 are the cell SOC and cumulative charge at the start of the calculation, respectively. SOC0, Q0, the parameter set Φ to be estimated for the dynamic SOC-OCV curve, and the parameter set Ω to be estimated for the empirical decay model are the variables to be optimized. The parameter optimization unit is used to optimize the parameters based on the screened static operating points and optimization equations using the sequential least squares quadratic programming method.

10. The battery SOC-SOH joint evaluation device according to claim 9, characterized in that: The SOC and SOH calculation module includes: The subsequent SOH evaluation unit is used to evaluate the SOH of the battery at any subsequent time t based on the parameters obtained by the sequential least squares quadratic programming method: Among them, X t It is the temperature and voltage operating data; The subsequent SOC evaluation unit is used to evaluate the SOC of the battery at any subsequent time t based on the parameters obtained by the sequential least squares quadratic programming method: Among them, SOC i-1 is the SOC at the subsequent i-1th static point, C t is the available capacity of the battery cell at time t, and C t =C0·SOH t ; The monitoring and restart module includes: The SOC correction value estimation unit is used to estimate the SOC correction value at the subsequent static point based on the dynamic SOC-OCV curve parameter set Φ obtained by parameter optimization: SOC i′ =f -1 (OCV i ), where OCV i is the cell voltage at the subsequent i-th static point, and f is the dynamic SOC-OCV curve; Jump amount calculation and judgment unit, used to calculate the jump amount of the SOC correction value at the subsequent static point: ΔSOC i =|SOC i -SOC i′ |, if ΔSOC i If the preset jump value threshold Y is exceeded, the dynamic OCV estimation is restarted; the preset jump value threshold Y is set according to the battery model and operating conditions.

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