State estimation method, device and equipment for energy storage battery and medium

Abstract

The invention relates to the technical field of battery management, and discloses a state estimation method, device and equipment for an energy storage battery and a medium, and the method comprises the steps: carrying out the preliminary estimation of the state of the energy storage battery through an extended Kalman filter, and obtaining an initial state value, a terminal voltage error and a Kalman gain at a current moment; taking the terminal voltage error and the Kalman gain at the current moment as input, performing error optimization through a self-adaptive spiral flight sparrow search algorithm, and outputting an error compensation amount to compensate an initial state value; feeding back the compensated state value to an extended Kalman filter containing a recalibration and quit mechanism so as to estimate the state of the energy storage battery at the next moment, repeatedly executing an error optimization step and a state feedback step based on an estimation result until a preset convergence condition is reached, and outputting a state estimation result of the energy storage battery; through an algorithm cooperation and dynamic adjustment mechanism, the precision and robustness of energy storage battery state estimation are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a state estimation method, device, equipment and medium for an energy storage battery. Background Art

[0002] Currently, energy storage systems, with their millisecond-level bidirectional power regulation capabilities and flexible operation characteristics, have become an important technical means to enhance the absorption level of distributed power sources, improve power quality, and support the power supply of important loads during distribution network fault recovery. The mainstream energy storage method of current energy storage systems is battery energy storage, so battery state estimation (state of charge SOC, state of health SOH) is crucial for capturing the dynamic changes of battery energy storage systems in actual operation.

[0003] In existing technologies, a data-driven approach is usually used to estimate the state of energy storage batteries. However, this approach requires a large amount of high-quality data. In actual applications, the data may be incomplete or noisy, which in turn affects the reliability and accuracy of the estimation results.

[0004] It can be seen that how to improve the reliability and accuracy of energy storage battery state estimation has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a state estimation method, device, equipment and medium for an energy storage battery, which solves the problem of how to improve the reliability and accuracy of energy storage battery state estimation.

[0006] To solve the above technical problems, the present invention provides a first aspect of a state estimation method for an energy storage battery, comprising: Obtain identification parameters of the energy storage battery, and preliminarily estimate the state of the energy storage battery through an extended Kalman filter based on the identification parameters to obtain an initial state value, a terminal voltage error, and a Kalman gain at the current moment; Taking the terminal voltage error and Kalman gain at the current moment as input, performing error optimization using a preset optimization algorithm, outputting an error compensation amount to compensate the initial state value, and obtaining a compensated estimated state value; the preset optimization algorithm is an adaptive spiral flying sparrow search algorithm; The compensated estimated state value is fed back to the improved extended Kalman filter to estimate the state of the energy storage battery at the next moment, and the error optimization step and the state feedback step are repeatedly performed based on the estimation result until a preset convergence condition is reached, and the state estimation result of the energy storage battery is output; the improved extended Kalman filter is configured as an extended Kalman filter including a recalibration and exit mechanism.

[0007] As one of the preferred solutions, the state of the energy storage battery is preliminarily estimated by using an extended Kalman filter based on the identification parameters to obtain the initial state value, terminal voltage error and Kalman gain at the current moment, including: Taking the health state and the state of charge of the energy storage battery as state variables, and constructing a state equation and an observation equation based on the state variables and the identification parameters; Predicting the state of the energy storage battery by using the state equation and the observation equation to obtain a predicted state value and its corresponding predicted covariance matrix; The state of the energy storage battery at the current moment is updated based on the predicted state value and its corresponding predicted covariance matrix to obtain the initial state value, terminal voltage error and Kalman gain at the current moment.

[0008] As one of the preferred solutions, the method of taking the terminal voltage error and Kalman gain at the current moment as input, performing error optimization through a preset optimization algorithm, and outputting an error compensation amount includes: Constructing a state estimation model of the energy storage battery based on the number of charge and discharge cycles of the energy storage battery, and constructing an objective function with minimizing the mean square error of the state estimation model as an optimization goal; The terminal voltage error and Kalman gain at the current moment are used as inputs, the adaptive spiral flying sparrow search algorithm is used to perform error optimization on the initial state value based on the objective function, and an error compensation amount is output.

[0009] As one preferred solution, the adaptive spiral flying sparrow search algorithm is used to perform error optimization on the initial state value and output an error compensation amount, including: A tent chaos map is used to initialize the population, and the solution space corresponding to the error compensation amount is divided into multiple layers of concentric search rings according to a spiral topology structure, so that each ring area is allocated to each individual after being encoded with polar coordinates; each individual in the population represents a set of error compensation amounts; The objective function is used as a fitness function to quantify the fitness value of each individual after the position update to determine the discoverer and the follower; The position of the finder is updated by introducing the residual of the extended Kalman filter or the adaptive weight of the residual of the improved extended Kalman filter and the Levy flight mechanism to generate a new candidate solution in the sub-area where the finder is located, and the fitness of the new candidate solution is calculated to update the finder; The follower is updated by adopting a variable spiral search strategy that introduces a time variable and the number of charge and discharge cycles of the energy storage battery; Calculating the fitness values of the updated discoverer and the updated follower based on the fitness function, eliminating individuals whose fitness values are lower than a preset fitness threshold, reinitializing at the ring boundary, and retaining individuals whose fitness values are higher than the preset fitness threshold to generate a new population; The discovery update step and the follower update step are iteratively performed according to the new population until a preset number of iterations is reached, and the position of the individual with the highest fitness is selected as the error compensation output.

[0010] As one preferred solution, the adaptive weight is expressed by the following formula: Where w and w0 are adaptive weight and basic weight respectively; a is the adjustment coefficient; RMS r is the residual of the extended Kalman filter or the residual of the improved extended Kalman filter; M is the maximum number of iterations.

[0011] As one preferred solution, the variable spiral search strategy is expressed by the following formula: Where x i (t), x worst (t) are the position and worst position of the i-th individual in the t-th iteration respectively; r is the dynamically adjusted spiral parameter; is the step size control parameter; Q and L are random factors; A is the random matrix; n is the total number of populations; r0 is the initial dynamic adjustment spiral parameter; T is time; T max is the maximum value of time; D cycle 、D cycle,max are the number of charge and discharge cycles of the energy storage battery and its maximum value.

[0012] As one preferred solution, feeding back the compensated estimated state value to an improved extended Kalman filter to estimate the state of the energy storage battery at the next moment includes: In response to the compensated estimated state value and triggering recalibration, using the compensated estimated state as an updated initial state value so that the improved extended Kalman filter quantizes a target covariance matrix corresponding to the updated initial state value through the extended Kalman filter; The target covariance matrix is compared with the preset matrix increment. When the target covariance matrix meets the preset matrix increment, the exit mechanism is triggered, and the noise covariance is reset to the initial value, waiting for the next iteration; when the target covariance matrix does not meet the preset matrix increment, the state of the energy storage battery at the next moment is estimated based on the updated initial state value and its corresponding target covariance matrix.

[0013] A second aspect of the present invention provides a state estimation device for an energy storage battery, comprising: An estimation module is used to obtain identification parameters of the energy storage battery and preliminarily estimate the state of the energy storage battery through an extended Kalman filter based on the identification parameters to obtain an initial state value, a terminal voltage error, and a Kalman gain at the current moment; a compensation module, configured to take the terminal voltage error and Kalman gain at the current moment as input, perform error optimization using a preset optimization algorithm, and output an error compensation amount to compensate the initial state value to obtain a compensated estimated state value; the preset optimization algorithm being an adaptive spiral flight sparrow search algorithm; An output module is configured to feed back the compensated estimated state value to an improved extended Kalman filter to estimate the state of the energy storage battery at the next moment, and repeatedly execute the error optimization step and the state feedback step based on the estimation result until a preset convergence condition is reached, and output the state estimation result of the energy storage battery; the improved extended Kalman filter is configured as an extended Kalman filter including a recalibration and exit mechanism.

[0014] A third aspect of the present invention provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the state estimation method for an energy storage battery as described above is implemented.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the state estimation method for the energy storage battery as described above is implemented.

[0016] Compared with the prior art, the embodiments of the present invention have the following advantages: (1) The extended Kalman filter is used to make a preliminary estimate of the state of charge and health of the energy storage battery, which can effectively deal with nonlinear system noise. The estimation error is optimized and compensated by the adaptive spiral flying sparrow search algorithm, further reducing the terminal voltage error and the deviation of the Kalman gain, and significantly improving the state estimation accuracy. (2) A recalibration mechanism is introduced into the extended Kalman filter, which can dynamically adjust the covariance matrix to adapt to external disturbances such as battery aging and temperature changes. When the predicted state variance increases significantly, the update is suspended and the previous state estimate is retained to prevent the estimate from diverging due to abnormal data. (3) The adaptive spiral flying sparrow search algorithm can dynamically adjust the error compensation amount through chaotic mapping, adaptive weighting and spiral search strategy to adapt to the state changes of the battery under different charge and discharge rates, temperatures and environments; the compensated state value is fed back to the improved extended Kalman filter, forming a closed-loop optimization process of "estimation-compensation-re-estimation", ensuring the real-time and accuracy of state estimation; (4) By combining the improved extended Kalman filter and the adaptive spiral flying sparrow search algorithm and introducing a recalibration and exit mechanism, high-precision estimation and dynamic optimization of the energy storage battery state are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flow chart of a method for estimating the state of an energy storage battery provided by an embodiment of the present invention; Figure 2 This is a structural diagram of a state estimation device for an energy storage battery provided by an embodiment of the present invention; Figure 3 This is a structural diagram of an electronic device provided by one embodiment of the present invention; Reference numerals: Among them, 10, estimation module; 20, compensation module; 30, output module; 5000, electronic device; 5001, processor; 5002, bus; 5003, memory; 5004, transceiver. DETAILED DESCRIPTION

[0019] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings and embodiments. Obviously, the embodiments described are only some embodiments of the present invention, rather than all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] In the description of this application, the terms "first," "second," "third," etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first," "second," "third," etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0021] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are for illustrative purposes only, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0022] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meanings as those commonly understood by those skilled in the art. The terms used in this specification are only for describing specific embodiments and are not intended to limit the present invention. Those skilled in the art will understand the specific meanings of the above terms in this application according to specific circumstances.

[0023] In one embodiment, if Figure 1 As shown, the first aspect of the present invention provides a state estimation method for an energy storage battery, comprising: S1. Obtain identification parameters of the energy storage battery, and preliminarily estimate the state of the energy storage battery through an extended Kalman filter based on the identification parameters to obtain the initial state value, terminal voltage error and Kalman gain at the current moment; wherein the identification parameters include the capacity, internal resistance, voltage, current, etc. of the energy storage battery.

[0024] In one embodiment, the preliminarily estimating the state of the energy storage battery through an extended Kalman filter based on the identification parameters to obtain the initial state value, terminal voltage error and Kalman gain at the current moment includes: Taking the health state and the state of charge of the energy storage battery as state variables, and constructing a state equation and an observation equation based on the state variables and the identification parameters; Predicting the state of the energy storage battery by using the state equation and the observation equation to obtain a predicted state value and its corresponding predicted covariance matrix; The state of the energy storage battery at the current moment is updated based on the predicted state value and its corresponding predicted covariance matrix to obtain the initial state value, terminal voltage error and Kalman gain at the current moment.

[0025] Specifically, the present invention uses an extended Kalman filter to estimate the state of the energy storage battery based on the acquired identification parameters: The present invention uses the state of health (SOH) and state of charge (SOC) of the energy storage battery as state variables, sets the initial state estimate (based on the initial conditions or prior knowledge of the battery) and the initialization covariance matrix (to express the uncertainty of the initial state estimate), and sets the process noise covariance matrix Q k , to express the uncertainty in the state transfer process, set the observation noise covariance matrix R k , to represent the uncertainty in the observation process. Then, based on the identification parameters and state variables, a state equation is constructed to describe the change of battery state variables, that is, the health state and state of charge over time. For SOH, its change is usually related to the number of charge and discharge cycles of the battery, temperature and other factors. The state equation can be constructed through empirical formulas or data-driven methods. For SOC, its change is usually related to the charge and discharge current of the battery. The state equation can be constructed based on the ampere-hour integration method or the equivalent circuit model. Then the state equation can be expressed as: Where x k is the actual state of the energy storage battery at time step k (i.e., SOH and SOC at the current moment); f is the state transfer function; u k is the input vector (such as the charge and discharge current, open circuit voltage, etc. in the identification parameters); w k is the process noise, which follows the covariance Q k The zero-mean multivariate Gaussian distribution of .

[0026] The observation equation is used to describe the relationship between the battery's state variables and measurable variables (such as terminal voltage, current, etc.). For SOC, its observation equation is constructed based on the relationship between the battery's open circuit voltage and SOC; for SOH, its observation equation involves the battery's internal resistance or other measurable parameters related to aging. The state equation can be expressed as: Where z k is the measurement value of the energy storage battery at time step k (i.e. the observation vector at the current moment); h(xk ) is the mapping function from the state vector to the observation vector; v k is the measurement noise that follows a zero-mean multivariate Gaussian distribution.

[0027] Then, based on the state equation and the state estimate at the current moment (and its corresponding covariance matrix), the state value is predicted. At the same time, based on the state equation and the covariance matrix of the process noise, the covariance matrix corresponding to the state estimate at the previous moment is predicted, which helps to evaluate the uncertainty of the predicted state. The prediction of the covariance matrix is performed by the following formula: Where, P k|k-1 is the prediction covariance matrix; F k is the state transfer matrix (linear approximation of f), which represents the partial derivative of the state transfer with respect to the state.

[0028] The Kalman gain is then calculated using the observations and the observation equation at the current moment, combined with the results of the prediction step: Where K k is the Kalman gain; H k is the observation matrix, which represents the partial derivative of the observation value with respect to the state.

[0029] The state estimate at the current moment is quantified using the Kalman gain and the observation value, which is expressed as follows: Where, To predict the state at time k based on the (k-1) time information, assuming the estimation error Following the variance The zero-mean multivariate Gaussian distribution of , the first step of each iteration is prediction, and the prediction state is recorded as .

[0030] And update the covariance matrix by the following formula: Where I is the identity matrix.

[0031] Finally, the updated state estimate As the initial state value output at the current moment, and in the update process, the terminal voltage error (i.e. the observation value and predicted value the difference between ).

[0032] The present invention uses both the health state and the state of charge as state variables, which can more comprehensively reflect the performance status of the battery. By constructing the state equation and the observation equation, the dynamic behavior of the battery can be more accurately described, thereby improving the accuracy of state estimation. The Kalman filter algorithm itself has the ability to suppress noise. Through the iterative update of the state equation and the observation equation, the estimation error can be gradually reduced. In addition, the introduction of the health state as a state variable can further consider the impact of battery aging on SOC estimation, thereby enhancing the robustness of the algorithm.

[0033] S2. Using the terminal voltage error and Kalman gain at the current moment as input, performing error optimization using a preset optimization algorithm, outputting an error compensation amount to compensate the initial state value, and obtaining a compensated estimated state value; the preset optimization algorithm is an adaptive spiral flying sparrow search algorithm (ASFSSA); In one embodiment, the method of taking the terminal voltage error and the Kalman gain at the current moment as input, performing error optimization through a preset optimization algorithm, and outputting an error compensation amount includes: Constructing a state estimation model of the energy storage battery based on the number of charge and discharge cycles of the energy storage battery, and constructing an objective function with minimizing the mean square error of the state estimation model as an optimization goal; The terminal voltage error and Kalman gain at the current moment are used as inputs, the adaptive spiral flying sparrow search algorithm is used to perform error optimization on the initial state value based on the objective function, and an error compensation amount is output.

[0034] Specifically, the present invention constructs a state estimation model for energy storage batteries based on the number of charge and discharge cycles of the energy storage batteries. 、 , which is expressed by the following formula: Where, is the maximum capacity of the energy storage battery; is the initial state of charge of the energy storage battery; I(t) is the current at time t; is the correction term for SOC estimation; is the state of charge dependent parameter; is the number of cycles of the energy storage battery; is the health status dependent parameter; is a parameter that can be determined by historical data.

[0035] The objective function is constructed with the minimum mean square error of the state estimation model as the optimization goal, which is expressed by the following formula: Where, 、 are the estimation errors of SOC and SOH, respectively, which are determined by their mean square errors.

[0036] The SOH of an energy storage battery usually shows a nonlinear relationship with its cycle number, which is mainly manifested in that as the cycle number increases, the battery's capacity decay and internal resistance increase will accelerate. Therefore, the present invention introduces the cycle number as a parameter to more accurately reflect the long-term usage status of the battery and capture the dynamic change trend of its health status.

[0037] The SOH estimation model usually includes parameters such as terminal voltage, current, and temperature, but these parameters mainly reflect the instantaneous state of the battery. The number of cycles is a cumulative indicator that can supplement the inadequacy of instantaneous parameters in describing long-term performance degradation, thereby improving the robustness and accuracy of the SOH estimation model.

[0038] The calculated terminal voltage error and Kalman gain are used as inputs and passed to the adaptive spiral flying sparrow search algorithm. The objective function is used as the fitness function to calculate the fitness function value according to the input content, and the position and speed of the sparrow are updated iteratively. The sparrow that minimizes the fitness function value is found and used as the error compensation to compensate the initial state value obtained based on the extended Kalman filter to obtain the compensated estimated state value. The process is shown in the following formula: SOC opt = SOC EKF +ΔSOC SOH opt = SOH EKF +ΔSOH Where, SOC opt 、SOH opt Estimated state value for compensation; SOC EKF 、SOH EKF is the initial state value; ΔSOC and ΔSOH are the error compensation amounts.

[0039] The present invention can more accurately reflect the actual state of the battery by constructing a state estimation model based on the number of charge and discharge cycles and optimizing it based on the objective function of minimizing the mean square error; the application of the adaptive spiral flying sparrow search algorithm makes it possible to more effectively search for the optimal solution in the solution space, further reducing the error of the state estimation; after introducing the adaptive spiral flying sparrow search algorithm optimization, the number of cycles is used as an additional input dimension to provide the model with richer feature information, thereby improving the model's ability to capture the nonlinear change law of SOH; and the state estimation model is constructed based on the number of charge and discharge cycles, taking into account the aging process of the battery, so that the model can adapt to the changes in battery state under different number of cycles.

[0040] In one embodiment, the step of using the adaptive spiral flight sparrow search algorithm to perform error optimization on the initial state value and outputting an error compensation amount includes: A tent chaos map is used to initialize the population, and the solution space corresponding to the error compensation amount is divided into multiple layers of concentric search rings according to a spiral topology structure, so that each ring area is allocated to each individual after being encoded with polar coordinates; each individual in the population represents a set of error compensation amounts; The objective function is used as a fitness function to quantify the fitness value of each individual after the position update to determine the discoverer and the follower; The position of the finder is updated by introducing the residual of the extended Kalman filter or the adaptive weight of the residual of the improved extended Kalman filter and the Levy flight mechanism to generate a new candidate solution in the sub-area where the finder is located, and the fitness of the new candidate solution is calculated to update the finder; The follower is updated by adopting a variable spiral search strategy that introduces a time variable and the number of charge and discharge cycles of the energy storage battery; Calculating the fitness values of the updated discoverer and the updated follower based on the fitness function, eliminating individuals whose fitness values are lower than a preset fitness threshold, reinitializing at the ring boundary, and retaining individuals whose fitness values are higher than the preset fitness threshold to generate a new population; The discovery update step and the follower update step are iteratively performed according to the new population until a preset number of iterations is reached, and the position of the individual with the highest fitness is selected as the error compensation output.

[0041] Specifically, in order to improve the randomness of population initialization, the present invention uses tent chaos mapping to initialize the population, making the individual distribution more uniform and enhancing the population diversity: randomly generating an initial value, and iteratively generating a chaotic sequence through the tent chaos mapping formula, mapping the chaotic sequence to the search space of the error compensation amount to obtain the initial population; wherein, The tent chaos mapping formula is as follows: Where b i Chaotic sequence for the tent chaos map; is the parameter vector of the jth individual, including the state of charge error compensation and the health state error compensation; L and U are the upper and lower bounds of the model parameters; The jth value in the chaotic sequence generated by the tent chaos map is used to randomly initialize the parameter vector within the upper and lower bounds of the model parameters. This map ensures a uniform distribution of individuals in the solution space and improves the quality of population initialization.

[0042] Set algorithm parameters, such as population size, discoverer ratio, initial dynamic adjustment spiral parameters, maximum number of iterations, etc., and then use the objective function as the fitness function to quantify the fitness value of each individual; among them, the smaller the fitness value, the better the error compensation corresponding to the individual, and divide the roles of sparrow individuals in the population according to the quantification results and the set algorithm parameters: based on the discoverer ratio, select several individuals with better fitness values as discoverers, based on the vigilance ratio, randomly select several individuals as vigilants, and the remaining individuals as followers.

[0043] The finder position is updated by introducing the adaptive weight of the residual of the extended Kalman filter or the residual of the improved extended Kalman filter and the Levy flight mechanism; wherein the adaptive weight is expressed by the following formula: Where w and w0 are adaptive weight and basic weight respectively; a is the adjustment coefficient; RMS r is the residual of the extended Kalman filter or the residual of the improved extended Kalman filter; M is the maximum number of iterations; W is the size of the sliding window.

[0044] Then the discoverer position is updated by introducing adaptive weights: Where x i (t) is the position of the i-th individual in the t-th iteration.

[0045] It should be noted that the adaptive weights use the extended Kalman filter residual only for the first update of the discoverer's position; all subsequent updates use the improved extended Kalman filter residual. Dynamically adjusting the search step size for individual positions using the Kalman filter's current residual (i.e., the root mean square of the deviation between the observed and predicted values in the state estimate within the window) can expand the search range to accelerate convergence when the residual is large, and refine the search to improve accuracy when the residual is small.

[0046] The Levy flight mechanism formula is as follows: Where, is the step size control parameter, and the adaptive weight w is used to dynamically adjust the search step size and direction; x p is the current global optimal individual position; Levy(λ) is a random path following the Levy distribution. Through the Levy flight mechanism, individuals can jump in the solution space with a combination of long and short random steps, enhancing their ability to escape local optima.

[0047] After updating the position of the discoverer based on the adaptive weight and Levy flight mechanism, a new candidate solution can be generated in the sub-area where the discoverer is located. The fitness of the new candidate solution is calculated according to the fitness function and compared with the fitness value of the original discoverer. The individual with a better fitness value is selected as the updated discoverer.

[0048] Then, a variable spiral search strategy that introduces the time variable and the number of charge and discharge cycles of the energy storage battery is used to update the follower. The update of the follower position by the variable spiral search strategy is expressed as follows: Where x worst (t) is the worst position of the i-th individual in the t-th iteration; r is the dynamic adjustment spiral parameter; Q and L are random factors; A is a random matrix; n is the total number of populations; r0 is the initial dynamic adjustment spiral parameter; T is time; T max is the maximum value of time; D cycle 、D cycle,max The present invention adopts a variable spiral search strategy that introduces time variables and the number of charge and discharge cycles of the energy storage battery, and dynamically adjusts the spiral parameters to diversify the search path. This strategy results in a larger spiral search amplitude in the early stages of iteration and when the battery is new, which is beneficial for global search. In the later stages of iteration and when the battery is aged, the spiral search amplitude gradually decreases, which is beneficial for local search and convergence.

[0049] Then, the fitness values of the updated discoverer and the updated follower are calculated based on the fitness function. According to the calculation results, individuals with fitness values lower than the preset fitness threshold are eliminated, reinitialized at the ring boundary, and individuals with fitness values higher than the preset fitness threshold are retained to generate a new population; finally, based on the new population, the discovery update step and the follower update step are iteratively executed until the preset number of iterations is reached, and the position of the individual with the highest fitness is selected as the error compensation output.

[0050] The present invention uses a tent chaos map to initialize the population, which can increase the diversity and ergodicity of the population, helping to prevent the algorithm from falling into a local optimal solution, thereby improving optimization accuracy. The introduction of adaptive weights and the Levy flight mechanism enables the algorithm to dynamically adjust the search step size and direction during the search process, further increasing the possibility of finding the global optimal solution. The variable spiral search strategy, by introducing time variables and the number of charge and discharge cycles of the energy storage battery, makes the search process more flexible and efficient, accelerating the convergence of the algorithm. The introduction of a residual factor as an adaptive adjustment factor achieves a dynamic trade-off between estimation accuracy (error minimization) and estimation confidence (covariance trace minimization), avoiding overfitting or divergence problems caused by a single objective. The online closed-loop optimization of the noise covariance matrix is introduced to suppress the sharp fluctuations of the Kalman gain. This scheme comprehensively considers the number of charge and discharge cycles of the energy storage battery and the objective function, enabling the algorithm to adapt to changes in battery state under different operating conditions and enhancing the robustness of the algorithm. By optimizing the initial state value and outputting an error compensation amount, the accuracy of energy storage battery state estimation can be significantly improved, providing a more reliable basis for battery management.

[0051] In another embodiment, the symbiotic biogeography optimization-Lévy flight hybrid algorithm can also be used as a preset optimization algorithm to perform error optimization to obtain an error compensation amount, which specifically includes: Initializing the number of habitats, immigration and emigration rate calculation parameters and Levy flight parameters, randomly generating an initial population for each habitat using a set of error compensation amounts, and constructing a fitness function using the terminal voltage error and Kalman gain at the current moment to quantify the fitness value of each habitat; quantifying the in-migration rate and the out-migration rate based on the fitness values and the initialized in-migration rate calculation parameters, so as to perform in-migration and out-migration operations on each of the habitats; Generate a Lévy flight step length based on the initialized Lévy flight parameters to impose a Lévy flight disturbance on the emigrated habitat; Randomly select two habitats to exchange some features for mutually beneficial cooperation, calculate the fitness value of the new habitat, and retain the habitat with a high fitness value to generate a new population; The migration-in and migration-out operations and the Levy flight disturbance are repeatedly performed based on the new population until a preset number of iterations is reached, and the habitat with the highest fitness value is selected as the error compensation output.

[0052] The present invention adopts the symbiotic biogeography optimization-Lévy flight hybrid algorithm when performing error optimization, which combines the species migration mechanism of biogeography optimization and the long-distance jumping characteristics of Lévy flight to perform the optimization process, including: First, the number of habitats, immigration and emigration rate calculation parameters, such as the immigration and emigration rate baseline value and immigration and emigration rate adjustment factor, and Lévy flight parameters such as the Lévy flight step size coefficient and the Lévy flight index are initialized. A set of error compensation values is randomly generated in the solution space as the initial population of each habitat, so that each habitat represents a possible solution space region. Based on the terminal voltage error and Kalman gain at the current moment, a fitness function is constructed to quantify the fitness value of each habitat, that is, the quality of the error compensation value corresponding to each habitat. The fitness function can be expressed as follows: In the formula, Fitness i is the fitness value; X i is the i-th habitat; λ is the mean immigration rate.

[0053] Based on the calculated fitness value and the initialized in-migration and out-migration rates, parameters are calculated to quantify the in-migration and out-migration rates. For each habitat, the in-migration rate probability is used to migrate characteristics of other habitats (such as ΔSOC or ΔSOH), and for each habitat, the out-migration rate probability is used to migrate characteristics of other habitats. The in-migration operation moves some individuals from habitats with low fitness values to habitats with high fitness values, while the out-migration operation moves some individuals from habitats with high fitness values to habitats with low fitness values, thereby increasing population diversity.

[0054] The Lévy flight step length is generated according to the initialized Lévy flight parameters. The Lévy flight step length obeys the Lévy distribution, which can be generated by the Mantegna algorithm. A Lévy flight perturbation is applied to the outgoing habitat, that is, a random search is performed around it to explore new solution space areas.

[0055] Two habitats are randomly selected and some of their features (such as certain dimensions of error compensation) are exchanged for mutually beneficial cooperation. Through mutually beneficial cooperation, new solutions can be generated and the fitness of the population may be improved. The fitness value of the newly generated habitat is calculated and compared with the fitness value of the original habitat. The habitat with a high fitness value is retained to generate a new population.

[0056] Finally, based on the new population, the migration operations and Levy flight disturbances are repeated until the preset number of iterations is reached. In each iteration, the population is updated according to the new habitat and fitness value, and the error compensation corresponding to the habitat with the highest fitness value is finally selected as the final output. This error compensation can be used to correct the initial state value of the energy storage battery state estimation model and improve the accuracy of state estimation.

[0057] The present invention balances exploration and exploitation by simulating the immigration and emigration behavior of species in an ecosystem; introduces Lévy flights in local search to enhance the ability to escape local optimality; improves the quality of solutions through mutually beneficial cooperation between species; and significantly improves the efficiency of solving complex nonlinear optimization problems through species migration, Lévy flight perturbations, and symbiotic cooperation mechanisms; the solution can also adapt to different types of energy storage batteries and different working conditions, and has strong versatility and practicality.

[0058] S3. Feedback the compensated estimated state value to the improved extended Kalman filter to estimate the state of the energy storage battery at the next moment, and repeatedly perform the error optimization step and the state feedback step based on the estimation result until a preset convergence condition is reached, and output the state estimation result of the energy storage battery; the improved extended Kalman filter is configured as an extended Kalman filter including a recalibration and exit mechanism.

[0059] In one embodiment, feeding back the compensated estimated state value to an improved extended Kalman filter to estimate the state of the energy storage battery at the next moment includes: In response to the compensated estimated state value and triggering recalibration, using the compensated estimated state as an updated initial state value so that the improved extended Kalman filter quantizes a target covariance matrix corresponding to the updated initial state value through the extended Kalman filter; The target covariance matrix is compared with the preset matrix increment. When the target covariance matrix meets the preset matrix increment, the exit mechanism is triggered, and the noise covariance is reset to the initial value, waiting for the next iteration; when the target covariance matrix does not meet the preset matrix increment, the state of the energy storage battery at the next moment is estimated based on the updated initial state value and its corresponding target covariance matrix.

[0060] Specifically, the "recalibration" step in the improved extended Kalman filter of the present invention is to re-estimate the actual effect of the Kalman gain after the state is updated, allowing the state estimator to exit the update when the update is useless. That is, after compensating the initial state value, the compensated estimated state value can trigger recalibration. At this time, the compensated estimated state is used as the updated initial state value, so that the improved extended Kalman filter can quantify the target covariance matrix corresponding to the updated initial state value through the covariance matrix calculation method of the traditional extended Kalman filter.

[0061] The present invention embeds the ASFSSA algorithm in the main loop of the designed improved extended Kalman filter to achieve online real-time optimization of noise parameters. Assume that the process noise covariance matrix Q of the improved extended Kalman filter is k , the observation noise covariance matrix R kare all diagonal matrices, then the optimization variables are: Where q1, q2, q3, and r are the parameters to be optimized.

[0062] Combining the stability of prediction error and Kalman gain: Where, is the estimation error of the noise covariance moment; b is the balance coefficient, which is taken as 0.1 in the present invention; N is the total time step; Prediction Error is the prediction error term; Gain Stability is the gain stability term; Trace is the Frobenius norm of the Kalman gain.

[0063] Then the update of the process noise covariance matrix and the observation noise covariance matrix in each iteration can be expressed as follows: Where, are the updated process noise covariance matrix and the updated observation noise covariance matrix respectively; Through ASFSSA's swarm intelligent search, we can avoid the local optimum of traditional gradient methods. Every E steps, we call ASFSSA optimization: Where, 、 are the process noise covariance matrix and observation noise covariance matrix after ASFSSA optimization respectively; Finally, the updated covariance matrix is used as the target covariance matrix in each iteration to estimate the state of the energy storage battery at the next moment.

[0064] Then the obtained target covariance matrix is compared with the preset matrix increment (the preset matrix increment can be set according to the actual needs of the system and the performance requirements of the filter, and is used to determine whether the target covariance matrix meets the requirements). When the target covariance matrix meets the preset matrix increment, it means that the current state estimation is accurate enough, triggering the exit mechanism, resetting the noise covariance to the initial value, and performing the next iteration, that is, restarting the prediction-update step of the initial state value based on the initialized noise covariance; when the target covariance matrix does not meet the preset matrix increment, it means that the current state estimation still needs to be improved, and then the state of the energy storage battery at the next moment is estimated based on the updated initial state value and its corresponding target covariance matrix.

[0065] The basic idea of the improved extended Kalman filter employed in this invention is not to minimize the updated state covariance matrix to the smallest possible value, but rather to re-estimate the system after the state update to more accurately estimate the actual effect of the Kalman gain. In fact, in the new framework, if the system is strongly nonlinear, the trace of the covariance matrix after recalibration may increase, and the predicted state may have a large variance. This phenomenon indicates that the "recalibration" has failed to improve the estimate and should be withdrawn. To address this situation, the improved extended Kalman filter also adds an "exit" step to the framework, allowing the state estimator to exit the update when the update is no longer useful.

[0066] By compensating the estimated state value and triggering recalibration, the present invention can more accurately reflect the actual state of the energy storage battery, reduce the estimation deviation caused by the initial state error, and use the compensated estimated state as the updated initial state value, which helps to improve the convergence and stability of the extended Kalman filter during the iteration process and improve the accuracy of state estimation. When the target covariance matrix meets the preset matrix increment, the exit mechanism is triggered and the noise covariance is reset to the initial value, which can avoid the accumulation of errors in the noise covariance during the iteration process and maintain the stability of the filter performance. The scheme can dynamically adjust the filter parameters and estimation process according to the actual state of the energy storage battery and environmental changes, thereby improving the adaptability and robustness of the filter.

[0067] After estimating the state of the energy storage battery at the next moment, the error optimization step and the state feedback step are repeatedly executed based on the estimation result until the preset convergence condition is reached, that is, when the preset number of iterations is reached, the final state estimation value is output as the state estimation result of the energy storage battery. The state estimation value of the next moment obtained in each iteration process can also be output as the state estimation result of the energy storage battery to analyze the state of the energy storage battery.

[0068] In an embodiment of the present application, based on the problem of how to improve the reliability and accuracy of energy storage battery state estimation, a state estimation method for an energy storage battery is designed. The method obtains identification parameters of the energy storage battery and preliminarily estimates the state of the energy storage battery through an extended Kalman filter based on the identification parameters to obtain an initial state value, terminal voltage error, and Kalman gain at the current moment. The terminal voltage error and Kalman gain at the current moment are used as inputs to perform error optimization through a preset optimization algorithm, and an error compensation amount is output to compensate the initial state value to obtain a compensated estimated state value. The preset optimization algorithm is an adaptive spiral flying sparrow search algorithm. The compensated estimated state value is fed back to an improved extended Kalman filter to estimate the state of the energy storage battery at the next moment. The error optimization step and the state feedback step are repeatedly performed based on the estimation result until a preset convergence condition is reached, and the state estimation result of the energy storage battery is output. The improved extended Kalman filter is configured as a technical solution of an extended Kalman filter including a recalibration and exit mechanism. Through algorithm collaboration and dynamic adjustment mechanism, the accuracy and robustness of energy storage battery state estimation are significantly improved.

[0069] It should be noted that although the steps in the above flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0070] In another embodiment, Figure 2 As shown, the second aspect of the present invention provides a state estimation device for an energy storage battery, comprising: An estimation module 10 is configured to obtain identification parameters of the energy storage battery and preliminarily estimate the state of the energy storage battery through an extended Kalman filter based on the identification parameters to obtain an initial state value, a terminal voltage error, and a Kalman gain at the current moment; a compensation module 20 configured to take the current terminal voltage error and the Kalman gain as inputs, perform error optimization using a preset optimization algorithm, and output an error compensation amount to compensate the initial state value to obtain a compensated estimated state value; the preset optimization algorithm being an adaptive spiral flight sparrow search algorithm; The output module 30 is used to feed back the compensated estimated state value to the improved extended Kalman filter to estimate the state of the energy storage battery at the next moment, and repeatedly execute the error optimization step and the state feedback step based on the estimation result until a preset convergence condition is reached, and output the state estimation result of the energy storage battery; the improved extended Kalman filter is configured as an extended Kalman filter including a recalibration and exit mechanism.

[0071] It should be noted that the various modules in the aforementioned state estimation device for an energy storage battery can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the aforementioned modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so that the processor can call and execute the operations corresponding to each of the aforementioned modules. For the specific definition of a state estimation device for an energy storage battery, please refer to the definition of a state estimation method for an energy storage battery above. Both have the same functions and effects and will not be repeated here.

[0072] A third aspect of the present invention provides an electronic device, comprising: processor, memory, and bus; The bus is used to connect the processor and the memory; The memory is used to store operation instructions; The processor is used to call the operation instruction, and the executable instruction enables the processor to perform operations corresponding to the state estimation method for energy storage batteries as shown in the first aspect of the present application.

[0073] In an alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The electronic device 5000 shown includes: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, for example, via a bus 5002. Optionally, the electronic device 5000 may further include a transceiver 5004. It should be noted that in actual applications, the number of transceivers 5004 is not limited to one, and the structure of the electronic device 5000 does not constitute a limitation on the embodiments of the present application.

[0074] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0075] The bus 5002 may include a path for transmitting information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0076] The memory 5003 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, a CD-ROM or other optical disk storage, an optical disc storage (including a compact disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0077] The memory 5003 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the above method embodiments.

[0078] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0079] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for estimating the state of an energy storage battery as shown in the first aspect of the present application is implemented.

[0080] Another embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiments.

[0081] In addition, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0082] In summary, the present invention relates to the field of battery management technology, and discloses a state estimation method, device, equipment and medium for energy storage batteries, which preliminarily estimates the state of the energy storage battery through an extended Kalman filter to obtain the initial state value, terminal voltage error and Kalman gain at the current moment; takes the terminal voltage error and Kalman gain at the current moment as input, performs error optimization through an adaptive spiral flying sparrow search algorithm, outputs an error compensation amount to compensate for the initial state value, feeds the compensated state value back to an extended Kalman filter including a recalibration and exit mechanism to estimate the state of the energy storage battery at the next moment, and repeatedly executes the error optimization step and the state feedback step based on the estimation result until a preset convergence condition is reached, and outputs the state estimation result of the energy storage battery; through algorithm collaboration and dynamic adjustment mechanism, the accuracy and robustness of energy storage battery state estimation are significantly improved.

[0083] Each embodiment in this specification is described in a progressive manner, and the parts that are directly the same or similar in each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0084] The above-described embodiments merely represent several preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the scope of protection of the claims.

Claims

1. A state estimation method for an energy storage battery, characterized in that: include: Obtain identification parameters of the energy storage battery, and preliminarily estimate the state of the energy storage battery through an extended Kalman filter based on the identification parameters to obtain an initial state value, a terminal voltage error, and a Kalman gain at the current moment; Taking the terminal voltage error and Kalman gain at the current moment as input, performing error optimization using a preset optimization algorithm, outputting an error compensation amount to compensate the initial state value, and obtaining a compensated estimated state value; the preset optimization algorithm is an adaptive spiral flying sparrow search algorithm; Feeding back the compensated estimated state value to the improved extended Kalman filter to estimate the state of the energy storage battery at the next moment, and repeating the error optimization step and the state feedback step based on the estimation result until a preset convergence condition is reached, and outputting the state estimation result of the energy storage battery; The improved extended Kalman filter is configured as an extended Kalman filter including a recalibration and exit mechanism.

2. A state estimation method for an energy storage battery according to claim 1, characterized in that: The method of preliminarily estimating the state of the energy storage battery through an extended Kalman filter based on the identification parameters to obtain the initial state value, terminal voltage error and Kalman gain at the current moment includes: Taking the health state and the state of charge of the energy storage battery as state variables, and constructing a state equation and an observation equation based on the state variables and the identification parameters; Predicting the state of the energy storage battery by using the state equation and the observation equation to obtain a predicted state value and its corresponding predicted covariance matrix; The state of the energy storage battery at the current moment is updated based on the predicted state value and its corresponding predicted covariance matrix to obtain the initial state value, terminal voltage error and Kalman gain at the current moment.

3. The state estimation method for an energy storage battery according to claim 1, characterized in that: The method uses the terminal voltage error and the Kalman gain at the current moment as inputs to perform error optimization through a preset optimization algorithm and output an error compensation amount, including: Constructing a state estimation model of the energy storage battery based on the number of charge and discharge cycles of the energy storage battery, and constructing an objective function with minimizing the mean square error of the state estimation model as an optimization goal; The terminal voltage error and Kalman gain at the current moment are used as inputs, the adaptive spiral flying sparrow search algorithm is used to perform error optimization on the initial state value based on the objective function, and an error compensation amount is output.

4. A state estimation method for an energy storage battery according to claim 3, characterized in that: The step of using the adaptive spiral flying sparrow search algorithm to perform error optimization on the initial state value and outputting an error compensation amount includes: A tent chaos map is used to initialize the population, and the solution space corresponding to the error compensation amount is divided into multiple layers of concentric search rings according to a spiral topology structure, so that each ring area is allocated to each individual after being encoded with polar coordinates; each individual in the population represents a set of error compensation amounts; The objective function is used as a fitness function to quantify the fitness value of each individual after the position update to determine the discoverer and the follower; The position of the finder is updated by introducing the residual of the extended Kalman filter or the adaptive weight of the residual of the improved extended Kalman filter and the Levy flight mechanism to generate a new candidate solution in the sub-area where the finder is located, and the fitness of the new candidate solution is calculated to update the finder; The follower is updated by adopting a variable spiral search strategy that introduces a time variable and the number of charge and discharge cycles of the energy storage battery; Calculating the fitness values of the updated discoverer and the updated follower based on the fitness function, eliminating individuals whose fitness values are lower than a preset fitness threshold, reinitializing at the ring boundary, and retaining individuals whose fitness values are higher than the preset fitness threshold to generate a new population; The discovery update step and the follower update step are iteratively performed according to the new population until a preset number of iterations is reached, and the position of the individual with the highest fitness is selected as the error compensation output.

5. A state estimation method for an energy storage battery according to claim 4, characterized in that: The adaptive weight is expressed by the following formula: Where w and w0 are adaptive weight and basic weight respectively; a is the adjustment coefficient; RMS r is the residual of the extended Kalman filter or the residual of the improved extended Kalman filter; M is the maximum number of iterations.

6. A state estimation method for an energy storage battery according to claim 5, characterized in that: The variable spiral search strategy is expressed by the following formula: Where x i (t), x worst (t) are the position and worst position of the i-th individual in the t-th iteration respectively; r is the dynamically adjusted spiral parameter; is the step size control parameter; Q and L are random factors; A is the random matrix; n is the total number of populations; r0 is the initial dynamic adjustment spiral parameter; T is time; T max is the maximum value of time; D cycle 、D cycle,max are the number of charge and discharge cycles of the energy storage battery and its maximum value.

7. A state estimation method for an energy storage battery according to claim 1, characterized in that: Feeding back the compensated estimated state value to the improved extended Kalman filter to estimate the state of the energy storage battery at the next moment includes: In response to the compensated estimated state value and triggering recalibration, using the compensated estimated state as an updated initial state value so that the improved extended Kalman filter quantizes a target covariance matrix corresponding to the updated initial state value through the extended Kalman filter; The target covariance matrix is compared with the preset matrix increment. When the target covariance matrix meets the preset matrix increment, the exit mechanism is triggered, and the noise covariance is reset to the initial value, waiting for the next iteration; when the target covariance matrix does not meet the preset matrix increment, the state of the energy storage battery at the next moment is estimated based on the updated initial state value and its corresponding target covariance matrix.

8. A state estimation device for an energy storage battery, characterized in that: include: An estimation module is used to obtain identification parameters of the energy storage battery and preliminarily estimate the state of the energy storage battery through an extended Kalman filter based on the identification parameters to obtain an initial state value, a terminal voltage error, and a Kalman gain at the current moment; a compensation module, configured to take the terminal voltage error and Kalman gain at the current moment as input, perform error optimization using a preset optimization algorithm, and output an error compensation amount to compensate the initial state value to obtain a compensated estimated state value; the preset optimization algorithm being an adaptive spiral flight sparrow search algorithm; an output module, configured to feed back the compensated estimated state value to an improved extended Kalman filter to estimate the state of the energy storage battery at the next moment, and repeatedly perform the error optimization step and the state feedback step based on the estimation result until a preset convergence condition is reached, and output the state estimation result of the energy storage battery; The improved extended Kalman filter is configured as an extended Kalman filter including a recalibration and exit mechanism.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for estimating the state of an energy storage battery according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the state estimation method for the energy storage battery according to any one of claims 1 to 7 is implemented.

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