SOC (state of charge) and SOH (state of health) joint estimation method and system for all battery cells of energy storage system

Abstract

The invention discloses an SOC and SOH joint estimation method and system for all battery cells of an energy storage system, and the method comprises the steps: measuring the terminal voltage of each battery, and taking the battery cell with the maximum terminal voltage and the battery cell with the minimum terminal voltage as characteristic battery cells; establishing a circuit model of the characteristic battery cell; based on the circuit model of the characteristic battery cell, carrying out joint estimation on SOC and SOH of the characteristic battery cell; based on the circuit model of each battery cell, carrying out fusion estimation on the SOC and SOH of each battery cell; and correcting the SOC estimated value and the SOH estimated value of each battery cell obtained by fusion estimation according to the SOC estimated value and the SOH estimated value of the characteristic battery cell obtained by joint estimation, and taking the SOC estimated value and the SOH estimated value of each battery cell obtained by fusion estimation as the SOC and SOH joint estimation result of all battery cells of the energy storage system. According to the method, the SOC and the SOH of each battery cell are accurately evaluated and serve as key indexes for realizing an active equalization technology.

Description

Technical Field

[0001] The present invention belongs to the technical field of large-scale energy storage battery state estimation. Specifically, it relates to a method and system for jointly estimating the SOC and SOH of all battery cells in an energy storage system. Background Art

[0002] With the vigorous development of renewable energy, energy storage systems are playing an increasingly important role in balancing power supply and demand, improving power grid reliability, and optimizing energy utilization. In an energy storage system, the state assessment of batteries, especially the accurate estimation of the state of charge (SOC) and state of health (SOH), is the key to ensuring the efficient and stable operation of the system. However, there are still some deficiencies in existing SOC and SOH estimation methods.

[0003] In the prior art, most SOC and SOH estimation methods mainly calculate for characteristic single cells, only focusing on the state of a single battery while ignoring the overall characteristics of the system. The limitation of this method is that the performance states of single cells may vary significantly due to manufacturing differences, usage environments, and working conditions, resulting in a large deviation between their actual performance and the estimated values. Especially in large-scale energy storage systems, due to the parallel and series characteristics of battery packs, the state change of a single battery will directly affect the performance of the entire system. Therefore, the method of separately estimating the SOC and SOH of characteristic single cells fails to capture the dynamic changes of the whole system and often leads to the inability to reflect the actual operating state of the energy storage system in real time and accurately.

[0004] With the development of new power systems, energy storage battery systems integrated by a large number of energy storage battery monomers with large energy storage capacity and power output capabilities are becoming more and more widely used. In large-scale energy storage battery systems with a capacity exceeding megawatt-hours and megawatt charge and discharge powers, the main problem to be solved is the joint estimation of the SOC and SOH of series-connected battery clusters. There are a large number of battery cells connected in series in large-scale energy storage battery systems. If the SOC and SOH of each battery cell are jointly estimated according to traditional methods, it will consume a large amount of hardware and computing resources. In order to effectively apply the active balancing technology, a method for jointly estimating the SOC and SOH of all battery cells in the entire energy storage system is particularly important. Summary of the Invention

[0005] To address the deficiencies in the existing technology, the present invention provides a method and system for jointly estimating the SOC and SOH of all the battery cells in an energy storage system, accurately evaluating the SOC and SOH of each battery cell, which are key indicators for implementing the active balancing technology; by comprehensively evaluating the states of all the battery cells, identifying the differences between individual battery cells, thereby optimizing the energy distribution of the battery pack, ensuring the balanced operation of each battery unit during charge and discharge, not only improving the overall utilization efficiency of the battery, but also extending the service life of the battery and reducing the safety hazards caused by individual imbalance.

[0006] The present invention adopts the following technical solutions.

[0007] The present invention proposes a method for jointly estimating the SOC and SOH of all the battery cells in an energy storage system, including: Measuring the terminal voltages of each battery, and taking the battery cell with the maximum terminal voltage and the battery cell with the minimum terminal voltage as characteristic battery cells; establishing a circuit model of the characteristic battery cells; Based on the circuit model of the characteristic battery cells, jointly estimating the SOC and SOH of the characteristic battery cells; Based on the circuit models of each battery cell, fusing and estimating the SOC and SOH of each battery cell; Using the SOC estimated value and SOH estimated value of the characteristic battery cells obtained from the joint estimation to correct the SOC estimated value and SOH estimated value of each battery cell obtained from the fusion estimation, as the joint estimation result of the SOC and SOH of all the battery cells in the energy storage system.

[0008] The circuit model of the characteristic battery cells is a second-order RC circuit model, and the second-order RC circuit model includes: the first RC link and the second RC link.

[0009] Constructing a hybrid Kalman filter using the adaptive extended Kalman filter and the adaptive unscented Kalman filter, and jointly estimating the SOC and SOH of the characteristic battery cells based on the circuit model of the characteristic battery cells, including: Based on the circuit model of the characteristic battery cells, using the adaptive extended Kalman filter to estimate the SOC of the characteristic battery cells, and the SOC estimated value of the characteristic battery cells at the estimation moment is the observed value of the measurement equation in the SOH estimation of the characteristic battery cells based on the adaptive unscented Kalman filter at the estimation moment; The posteriori estimated value of the capacity of the characteristic battery cells at the estimation moment obtained by estimating the SOH of the characteristic battery cells based on the adaptive unscented Kalman filter is the input value of the state space equation in the SOC estimation of the characteristic battery cells based on the adaptive extended Kalman filter at the estimation moment.

[0010] Based on the circuit model of the characteristic battery cell, the SOC of the characteristic battery cell is estimated by using adaptive extended Kalman filter, including: Based on the second-order RC circuit model of the characteristic battery cell, construct the state space equation and measurement equation for SOC estimation; After the initialization setting, improve the estimated value of the measurement noise covariance matrix and the estimated value of the noise covariance; Based on the improved estimated value of the noise covariance, conduct the prior estimation of the state vector and the error covariance; Based on the improved estimated value of the measurement noise covariance matrix, determine the Kalman gain matrix. Using the Kalman gain matrix, based on the state space equation and measurement equation for SOC estimation, conduct the posterior estimation of the state vector and the error covariance; From the state vector estimation at the estimation moment, obtain the SOC estimation result of the characteristic battery cell at the estimation moment.

[0011] The state space equation and measurement equation for SOC estimation are as follows:

[0012] In the formula, is the state vector at the estimation moment, is the system input at the estimation moment, is the process noise of the state space equation, is the measured value of the terminal voltage of the battery cell at the estimation moment, is the measurement noise of the measurement equation, is the state matrix at the estimation moment, is the input matrix at the estimation moment, is the observation matrix at the estimation moment, is the direct transfer matrix at the estimation moment; Among them, using the value of the characteristic battery cell at the estimation moment , the voltage of the first RC link, the voltage of the second RC link, construct the state vector at the estimation moment, satisfying , with the current of the characteristic battery cell at the is the system input, satisfying .

[0013] Each matrix in the state space equation and the measurement equation satisfies the following relationship:

[0014]

[0015]

[0016]

[0017] In the formula, is the equation iteration time step, = , and are the resistance and capacitance of the first RC link respectively, = , and are the resistance and capacitance of the second RC link respectively, is the estimated value of the capacity of the characteristic battery cell at the estimation moment, is the charge and discharge efficiency of the characteristic battery cell, is the open circuit voltage of the characteristic battery cell at the estimation moment, is the internal resistance of the characteristic battery cell, and are the polarization internal resistances of the characteristic battery cell.

[0018] Improve the estimated value of the measurement noise covariance matrix and the estimated value of the noise covariance to satisfy the following relationship:

[0019] In the formula, is the forgetting factor, and its value range is , , are all intermediate parameters constructed at the estimation moment, is the estimated value of the measurement noise covariance matrix at the estimation moment, is the estimated value of the noise covariance at the estimation moment, is the Kalman gain matrix at the estimation moment, is the estimated value of the state vector at the estimation moment.

[0020] The SOH estimation of the characteristic battery cell based on the adaptive unscented Kalman filter includes: Based on the ampere-hour integration algorithm, using the estimation result of the characteristic battery cell at the estimation moment, establish the state space equation and measurement equation for SOH estimation; After the initialization settings, conduct a prior estimation of the capacity of the characteristic battery cell, output the estimation error covariance, and the error covariance between the prior estimation and the output estimation of the capacity of the characteristic battery cell. Among them, the estimation moment and the estimation moment of the estimated value of the difference between the estimation results is used as the output estimation; Determine the Kalman gain matrix using the output estimation error covariance and the error covariance between the prior estimation and the output estimation of the capacity of the characteristic battery cell. Based on the state space equation and measurement equation for SOH estimation, conduct a posteriori estimation of the capacity of the characteristic battery cell and a posteriori covariance estimation using the Kalman gain matrix; After obtaining the a posteriori estimation value of the capacity of the characteristic battery cell at the estimation moment, calculate the SOH estimation value from the following relational expression:

[0021] In the formula, is the estimation result of the characteristic battery cell at the estimation moment, is the a posteriori estimation value of the capacity of the characteristic battery cell at the estimation moment, and

[0022] The state space equation and measurement equation for SOH estimation are as follows:

[0023] In the formula, is the capacity of the characteristic battery cell at the estimation moment, is the system noise, is the difference between the estimation results at the estimation moment and the estimation moment, is the charge-discharge efficiency of the characteristic battery cell, is the current of the characteristic battery cell at the is the equation iteration time step, is the measurement noise.

[0024] Based on the circuit models of each battery cell, the SOC and SOH of each battery cell are estimated through fusion, including: Based on the circuit model of the battery cell, the internal resistance of each battery cell is identified; Using the internal resistance of each battery cell obtained through identification, the SOC of each battery cell calculated by the method combining ampere-hour integration and open-circuit voltage is corrected; Using the SOC difference of each battery cell, based on the open-circuit voltage characteristics of the battery cell, the SOH of each battery cell is estimated; Using the capacity estimation value determined based on the SOH estimation value of each battery cell, the SOC estimation value of each battery cell is obtained by the ampere-hour integration method.

[0025] The calculation formula for the estimated value of the SOH of each battery cell is as follows:

[0026]

[0027] In the formula, is the capacity estimation value of the th battery cell at the estimation time, is the cumulative charge of the th battery cell at the estimation time, is the SOC difference of the th battery cell at the estimation time, is the estimated value of the SOH of the th battery cell at the estimation time, is the rated capacity of the battery cell;

[0028]

[0029] In the formula, is the SOC of the th battery cell calculated by the ampere-hour integration method at the estimation time, using the internal resistance of the th battery cell obtained through identification to calculate the open-circuit voltage value of the th battery cell, and obtaining the SOC value corresponding to the open-circuit voltage through the open-circuit voltage look-up table method .

[0030] The SOC estimation value of each battery cell satisfies the following relational expression:​​​​​

[0031] In the formula, is the estimated SOC value of the th battery cell at the estimation moment, is the charge-discharge efficiency of the battery cell, is the current of all battery cells at the estimation moment, is the estimated capacity value of the th battery cell at the estimation moment, is the calculation iteration time step.

[0032] Taking the estimated SOC value of the characteristic battery cell obtained by joint estimation as a constraint, the estimated SOC values of each battery cell obtained by fusion estimation are corrected by the following relational expression:

[0033] In the formula, is the correction value of the estimated SOC value of the th battery cell at the estimation moment, is the estimated SOC value of the characteristic battery cell corresponding to the maximum terminal voltage at the estimation moment, is the estimated SOC value of the characteristic battery cell corresponding to the minimum terminal voltage at the estimation moment, is the estimated SOC value of the th battery cell at the estimation moment.

[0034] Taking the estimated SOH value of the characteristic battery cell obtained by joint estimation as a constraint, the estimated SOH values of each battery cell obtained by fusion estimation are corrected by the following relational expression: When :

[0035] When :

[0036] In the formula, is the correction value of the estimated SOH value of the th battery cell at the estimation moment, is the minimum value of the estimated SOH values of each battery cell at the estimation moment, is the maximum value of the estimated SOH values of each battery cell at the estimation moment, is the The estimated SOH value of the nth battery cell at the estimation moment, is the estimated SOH value of the characteristic battery cell corresponding to the maximum terminal voltage at the estimation moment, and is the estimated SOH value of the characteristic battery cell corresponding to the minimum terminal voltage at the estimation moment. This invention also proposes a joint SOC and SOH estimation system for all battery cells of an energy storage system, including: A joint SOC and SOH estimation module for characteristic battery cells, which is used to measure the terminal voltage of each battery, and use the battery cell with the maximum terminal voltage and the battery cell with the minimum terminal voltage as characteristic battery cells; establish a circuit model of the characteristic battery cells; based on the circuit model of the characteristic battery cells, jointly estimate the SOC and SOH of the characteristic battery cells;

[0037] A fusion estimation module for the SOC and SOH of each battery cell, which is used to fuse and estimate the SOC and SOH of each battery cell based on the circuit model of each battery cell; A joint SOC and SOH estimation module for all battery cells, which is used to correct the estimated SOC value and SOH value of each battery cell obtained by fusion estimation with the estimated SOC value and SOH value of the characteristic battery cells obtained by joint estimation, as the joint SOC and SOH estimation result of all battery cells of the energy storage system. This invention is also a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0038] This invention is also a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method are realized.

[0039]

[0040] The beneficial effects of this invention are at least as follows compared with the prior art. The method proposed by this invention can ensure accurate evaluation and balancing of the system under dynamic working conditions, improve the management level of the energy storage system, and enhance its safety and reliability. By comprehensively evaluating the SOC and SOH of the battery pack, this algorithm will achieve effective active balancing, optimize the charge and discharge strategy, and reduce the operation risk, thereby promoting the wider application of the energy storage system in the utilization of renewable energy and promoting the sustainable development of the power system.

[0041] BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 Figure 2 is a flowchart of a joint SOC and SOH estimation method for all battery cells of an energy storage system proposed by this invention; Figure 3 is a second-order RC circuit model of the characteristic battery cell in an embodiment of this invention; Figure 3It is a schematic diagram of the combined estimation of SOC and SOH of the characteristic battery cells in the embodiments of the present invention; Figure 4 It is a simple circuit model of each battery cell in the embodiments of the present invention; Figure 5 It is a schematic diagram of the SOC estimation of each battery cell in the embodiments of the present invention; Figure 6 It is a schematic diagram of the SOH estimation of each battery cell in the embodiments of the present invention. Detailed implementation manners

[0042] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0043] Facing the engineering application problem of the combined estimation of SOC and SOH of all battery cells in a large-scale energy storage battery system, the present invention proposes a method for jointly estimating the SOC and SOH of all battery cells in an energy storage system that can comprehensively estimate accuracy and save computing power resources, as Figure 1 shown, including: Step 1, measure the terminal voltage of each battery cell, and use the battery cell with the maximum terminal voltage and the battery cell with the minimum terminal voltage as the characteristic battery cells; establish a circuit model of the characteristic battery cells.

[0044] The present invention first selects and constructs a high-precision circuit model of the characteristic battery cells, so as to apply a high-precision algorithm to realize the combined estimation of SOC and SOH of the characteristic battery cells.

[0045] Specifically, Step 1 includes: Step 1.1, measure the terminal voltage of each battery cell, and use the single cell with the maximum terminal voltage and the single cell with the minimum terminal voltage as the characteristic battery cells; In the embodiment, selecting the single cells (battery cells) with the maximum and minimum terminal voltages in the energy storage system as the characteristic battery cells is a non-restrictive and better choice. Those skilled in the art can select single cells with different parameters such as capacity and internal resistance as the characteristic battery cells.

[0046] Step 1.2, establish a second-order RC circuit model of the characteristic battery cells; Figure 2 It is the second-order RC circuit model of the characteristic battery cells in the embodiments of the present invention; the high-precision circuit model of the characteristic battery cells adopted in the present invention is a second-order RC circuit model that is convenient for online identification and solution of model parameters; among them, is the open-circuit voltage of the characteristic battery cell circuit model, is the equivalent internal resistance of the characteristic battery cell circuit model, corresponding to the ohmic polarization of the battery. The two RC links correspond to the hysteresis effect of the electrochemical polarization. The parameters of the first RC link are and , and the parameters of the second RC link are and , is the terminal voltage of the battery cell, is the current of the battery cell. The charging direction is positive, and the discharging direction is negative; Step 1.3: Based on the second-order RC circuit model of the characteristic battery cell, determine the relationship between the terminal voltage and current of the battery cell; According to Kirchhoff's voltage law, the mathematical relationship expression of the terminal voltage and current in the complex frequency domain is as follows:

[0047] In the formula, is the terminal voltage of the battery cell in the complex frequency domain, is the open-circuit voltage of the battery cell in the complex frequency domain, is the current of the battery cell in the complex frequency domain, is the equivalent internal resistance of the battery cell, and are the resistance and capacitance of the first RC link respectively, and are the resistance and capacitance of the second RC link respectively, is the operator.

[0048] The high-precision second-order RC circuit model of the battery includes important parameters such as the open-circuit voltage, ohmic internal resistance, and second-order RC of the battery. By conducting detailed electrochemical characteristic tests and exponential polynomial data fitting on the characteristic battery cell, a dynamic model that conforms to the actual working conditions is established. This model can reflect the changes in the battery state in real time and provide accurate data support for the subsequent joint estimation of SOC and SOH.

[0049] Step 2: Based on the circuit model of the characteristic battery cell, jointly estimate the SOC and SOH of the characteristic battery cell; Due to the coupling effect between SOC and SOH, estimating SOC or SOH alone cannot guarantee obtaining the accurate and true state value of the battery. Therefore, the present invention realizes the joint estimation of SOC and SOH of the characteristic battery cell by constructing a hybrid Kalman filter, including: At the current estimation moment, using the SOH estimation result of the characteristic battery cell at the previous estimation moment, the SOC of the characteristic battery cell is estimated by using the Adaptive Extended Kalman Filter (AEKF). Using the SOC estimation result of the characteristic battery cell, the SOH of the characteristic battery cell is estimated by using the Adaptive Unscented Kalman Filter (AUKF). Through multiple iterations, the mutual feedback correction of the SOC and SOH estimation results of the characteristic battery cell is realized to obtain the estimation results of the SOC and SOH of the characteristic battery cell.

[0050] Specifically, step 2 includes: Step 2.1, based on the circuit model of the characteristic battery cell, the SOC of the characteristic battery cell is estimated by using AEKF, specifically as follows: Step 2.1.1, based on the second-order RC circuit model of the characteristic battery cell, the state space equation and measurement equation for SOC estimation are constructed as follows:

[0051] In the formula, is the state vector at the estimation moment, is the system input at the estimation moment, is the process noise of the state space equation, is the measured value of the terminal voltage of the battery cell at the estimation moment, is the measurement noise of the measurement equation, is the state matrix at the estimation moment, is the input matrix at the estimation moment, is the observation matrix at the estimation moment, is the direct transfer matrix at the estimation moment; Among them, using the value of the characteristic battery cell at the estimation moment , the voltage of the first RC link, the voltage of the second RC link to construct the state vector at the estimation moment, satisfying Using the current of the characteristic battery cell at the ; Then, each matrix in the state space equation and the measurement equation satisfies the following relational expressions:

[0052]

[0053]

[0054]

[0055] In the formula, is the time step of equation iteration, = , and are the resistance and capacitance of the first RC link respectively, = , and are the resistance and capacitance of the second RC link respectively, is the estimated value of the capacity of the characteristic battery cell at the estimation moment, is the charge-discharge efficiency of the characteristic battery cell, is the open-circuit voltage of the characteristic battery cell at the estimation moment, is the internal resistance of the characteristic battery cell, and are the polarization internal resistances of the characteristic battery cell; Among them, the open-circuit voltage of the characteristic battery cell at the estimation moment is obtained by fitting the open-circuit voltage test data as follows:

[0056] In the formula, is the fitting parameter of SOC, is the exponential upper limit of the relational expression fitting, is the th power of the SOC at the estimation moment, is the fitting parameter of current, , , are the first set of combined fitting parameters of SOC, , , are the second set of combined fitting parameters of SOC; Step 2.1.2, perform initialization settings according to the following relational expressions:

[0057] In the formula, is the initial value of the state vector estimate, is the state vector at = 0 initial value at time, is the mathematical expectation operation, is the initial value of the error covariance matrix estimate, represents matrix transpose, is the initial value of the noise covariance matrix, is the positive definite initialization matrix, is the rated capacity of the characteristic battery cell.

[0058] Step 2.1.3, to avoid and losing positive definiteness in the calculation, after the initialization setting, the estimated value of the measurement noise covariance matrix and the estimated value of the noise covariance are improved to satisfy the following relationship:

[0059] where, is the forgetting factor, and the value range is .

[0060] Step 2.1.4, based on the estimated value of the improved noise covariance, perform prior estimation of the state vector and error covariance, including: The time update of the state estimation satisfies the following relationship:

[0061] where, is the prior estimated value of the state vector at the th estimation time, is the estimated value of the state vector at the th estimation time; The time update of the error covariance satisfies the following relationship:

[0062] where, is the prior estimated value of the error covariance at the th estimation time, is the estimated value of the noise covariance at the th estimation time.

[0063] Step 2.1.5, determine the Kalman gain matrix based on the estimated value of the improved measurement noise covariance matrix, and use the Kalman gain matrix to perform posterior estimation of the state vector and error covariance based on the state space equation and the measurement equation, including: The Kalman gain matrix satisfies the following relationship:

[0064] In the formula, is the Kalman gain matrix at the estimation moment, is the estimated value of the measurement noise covariance matrix at the estimation moment; The measurement update of the state estimation satisfies the following relationship:

[0065] In the formula, is the estimation result of the characteristic battery cell at the estimation moment, is the estimation result, is the voltage estimation result of the first RC link, is the voltage estimation result of the second RC link; The measurement update of the error covariance satisfies the following relationship:

[0066] In the formula, is the identity matrix; Step 2.1.6, obtain the estimation result of the characteristic battery cell at the estimation moment from the state vector estimation at the estimation moment, and advance from the estimation moment to the estimation moment.

[0067] Step 2.2, based on the SOC estimation result of the characteristic battery cell, use AUKF to estimate the SOH of the characteristic battery cell, specifically as follows: Step 2.2.1, based on the ampere-hour integration algorithm, use the estimation result of the characteristic battery cell at the estimation moment to establish the state space equation and measurement equation for SOH estimation as follows:

[0068] In the formula, is the capacity of the characteristic battery cell at the estimation moment,​​​ is the system noise, is the difference between the estimation time and the estimation result at the estimation time, is the charge and discharge efficiency of the characteristic battery cell, is the current of the characteristic battery cell at the estimation time, is the equation iteration time step, is the measurement noise; Step 2.2.2, perform initialization settings according to the following relational expression:

[0069] In the formula, is the estimated value of the capacity of the characteristic battery cell at is the capacity of the characteristic battery cell at is the initial value of the error covariance estimate, is the initial value of the system noise covariance matrix estimate, is the initial value of the detection noise covariance matrix estimate, is the noise covariance matching window length.

[0070] Step 2.2.3, after the initialization settings, perform a prior estimate of the capacity of the characteristic battery cell and the error covariance between the prior estimate and the output estimate of the capacity of the characteristic battery cell. Among them, the difference between the estimation time and the estimation result at the estimation time is used as the output estimate; as follows: 1), Select sigma points for the estimated value of the capacity of the characteristic battery cell, as follows:

[0071] In the formula, is the th sigma point of the estimated value of the capacity of the characteristic battery cell at the estimation time, is the posterior estimate value of the capacity of the characteristic battery cell at the estimation time, is the unscented transformation value of the th sigma point of the capacity of the characteristic battery cell, is the posterior estimate value of the error covariance at the estimation time, is the dimension of the state vector of the characteristic battery cell, indicates taking the th Row is the adjustment coefficient between the estimated mean and the error covariance of the characteristic battery cell, .

[0072] Adopt the sigma-point symmetric sampling strategy, and the corresponding weighting coefficients are:

[0073] In the formula, is the weighting coefficient corresponding to the th sigma point; 2), Make a prior estimate of the capacity of the characteristic battery cell The prior estimate of the capacity of the characteristic battery cell satisfies the following relational expression:

[0074] In the formula, is the prior estimate of the capacity of the characteristic battery cell at the estimation time; The error covariance of the prior estimate of the capacity satisfies the following relational expression:

[0075] In the formula, is the error covariance of the prior estimate of the capacity of the characteristic battery cell at the estimation time, is the estimated value of the system noise covariance matrix at the estimation time; 3), Select sigma points again for the estimated value of the capacity of the characteristic battery cell, as follows:

[0076] The weighting coefficients corresponding to each sigma point remain unchanged.

[0077] 4), Obtain the output estimate and the output covariance estimate as follows:

[0078]

[0079] In the formula, is the output estimate at the estimation time, which is the estimated value of the difference between the estimation time and the estimation time of the estimation result, is the th sigma point of the output estimate at the estimation time, is the error covariance of the output estimate, is the estimated value of the noise covariance matrix detected at the estimation moment; Calculate the prior estimate of the capacity of the characteristic battery cell and the error covariance of the output estimate are as follows:

[0080] In the formula, is the error covariance between the prior estimate of the capacity of the characteristic battery cell and the output estimate; Step 2.2.4: Determine the Kalman gain matrix using the error covariance of the output estimate and the error covariance between the prior estimate of the capacity of the characteristic battery cell and the output estimate. Based on the Kalman gain matrix, perform the posterior estimate of the capacity of the characteristic battery cell and the posterior covariance estimate based on the state space equation and the measurement equation for SOH estimation as follows: Based on the Kalman filter principle, the posterior estimate of the capacity of the characteristic battery cell and the posterior covariance estimate are:

[0081] In the formula, is the error covariance of the posterior estimate of the capacity of the characteristic battery cell at the estimation moment, is the prior estimate value of the state vector at the estimation moment; The adaptive covariance matching is:

[0082] Step 2.2.5: After obtaining the posterior estimate value of the capacity of the characteristic battery cell at the estimation moment, calculate the SOH estimate value from the following relationship:

[0083] In the formula, is the rated capacity of the characteristic battery cell; And advance from the estimation moment to the estimation moment.

[0084] When the present invention uses hybrid Kalman filtering for the joint estimation of the SOC and SOH of the characteristic battery cell, the SOC estimate value of the characteristic battery cell at the estimation moment obtained based on AEKF is the observed value of the measurement equation input in the SOH estimation of the characteristic battery cell based on AUKF; The estimation moment obtained from the SOH estimation of the characteristic battery cell based on AUKF The posteriori estimate of the characteristic cell capacity at the estimation moment , which is the input of the SOC estimation of the characteristic cell based on the AEKF algorithm at the input value of the state space equation at the estimation moment.

[0085] As Figure 3 shown, the AEKF algorithm and the AUKF algorithm interact fully in the joint estimation process of the characteristic cell, SOC, and SOH. By using the AEKF to estimate SOC and jointly using the AUKF to estimate SOH, the estimation accuracy is significantly improved. In the battery system, the estimation of SOC and SOH has highly non-linear characteristics. After their interaction, the AUKF algorithm can provide a more accurate non-linear state prediction for the AEKF algorithm, helping the AEKF algorithm better handle the linearization error. Conversely, the AEKF algorithm can assist the AUKF algorithm in optimizing the measurement update step, thus significantly improving the overall accuracy of the SOC and SOH estimations. Moreover, the algorithm robustness is enhanced. When the system is affected by external interference and the estimation of one algorithm deviates, the other algorithm can correct the deviation through information transmission in the interaction process. For example, if the AUKF algorithm has a large fluctuation in the SOH estimation due to strong noise interference at a certain moment, the AEKF algorithm, based on its relatively stable linear estimation characteristics, can transmit relatively reliable SOC-related information to the AUKF algorithm to assist it in recalibrating the SOH estimation. Also, the dynamic response ability is improved. The interaction between the two algorithms can accelerate the tracking of these dynamic changes. During the operation of the battery, when the SOC changes rapidly, the AEKF algorithm can quickly capture this change trend and transmit the information to the AUKF algorithm. Based on this, the AUKF algorithm can adjust the SOH estimation faster. In addition, potential information associations can be mined. SOC and SOH are not completely independent variables, and there is an inherent physical connection between them. The two algorithms can comprehensively analyze the battery measurement data (such as voltage, current, etc.) from different perspectives.

[0086] In summary, the present invention constructs a high-precision battery second-order RC circuit model by selecting characteristic cells, and uses a hybrid Kalman filtering algorithm combining AEKF and AUKF to perform high-precision joint estimation of SOC and SOH for the selected characteristic cells. The selection of the characteristic cells is based on their representativeness and reliability, ensuring that the constructed circuit model can accurately reflect the electrochemical characteristics and dynamic behavior of the battery, and providing a basis for the state estimation of other cells in the entire energy storage system.

[0087] Step 3: Based on the circuit models of each cell, perform fusion estimation of the SOC and SOH of each cell.

[0088] Specifically, Step 3 includes: Step 3.1. Based on the circuit model of the battery cell, estimate the SOC of each battery cell by combining ampere-hour integration and open-circuit voltage, including: Step 3.1.1. Based on the circuit model of the battery cell, online identify the internal resistance of each battery cell. The present invention uses Figure 4 the shown simple circuit model to carry out the estimation of the SOC and SOH of all battery cells, so as to reduce the algorithm complexity and the consumption of the hardware computing power of the battery management system in the scenario facing engineering applications.

[0089] Based on the simple circuit model of the battery cell, the internal resistance of each battery cell is identified in real time, satisfying the following relationship:

[0090] In the formula, is the internal resistance of the th battery cell, is the change in the terminal voltage of the th battery cell, is the change in the current of the energy storage cluster.

[0091] For all battery cells, a simple battery circuit model is constructed, and the internal resistance measurement of all battery cells is realized through online identification technology. The online identification process uses real-time data acquisition and analysis to ensure that the internal resistance changes of the battery cells in different charge and discharge states can be accurately captured, providing an important basis for the evaluation of SOC and SOH, and ensuring the real-time performance and effectiveness of the method.

[0092] Step 3.1.2. Use the identified internal resistance of each battery cell to correct the SOC of each battery cell calculated by the method of combining ampere-hour integration and open-circuit voltage. Specifically, use the method of combining ampere-hour integration and open-circuit voltage to estimate the SOC of each battery cell, as Figure 5 shown, including: 1). Obtain the initial value of the SOC of all battery cells by using the open-circuit voltage look-up table method .

[0093] 2). Calculate the SOC value of each battery cell by using the ampere-hour integration method, satisfying the following relationship:

[0094] In the formula, is the SOC of the th battery cell at the estimation moment, is the charge and discharge efficiency of the battery cell, is the current of all battery cells at the estimation moment, is the th at the The capacity of each battery cell is used to calculate the iterative time step.

[0095] 3), Using the internal resistance of the th battery cell identified in real time , calculate the open circuit voltage value of the th battery cell, and obtain the SOC value corresponding to the open circuit voltage through the open circuit voltage look-up table method , and then correct the SOC of each battery cell calculated by the ampere-hour integration method to satisfy the following relationship:

[0096]

[0097] In the formula, is the SOC difference of the th battery cell at the estimation moment, is the estimated value of the SOC of the th battery cell after correction at the estimation moment, is the correction coefficient, and its value range is .

[0098] Figure 6 Step 3.2, Estimate the SOH of each battery cell based on the open circuit voltage characteristics of the battery cell; The SOH estimation principle of each battery cell adopted in the present invention is as shown. The charge and discharge amounts of each battery cell are accumulated. The SOC difference of the th battery cell corresponds to the open circuit voltage difference and the accumulated charge . The calculation formula for the estimated value of the SOH of each battery cell is as follows:

[0099]

[0100] In the formula, is the estimated value of the capacity of the th battery cell at the estimation moment, is the accumulated charge of the th battery cell at the estimation moment, is the estimated value of the SOH of the th battery cell at the estimation moment, is the rated capacity of the battery cell.

[0101] ​​​​​Combined with the open-circuit voltage characteristics of the battery cells, an SOH evaluation model is established. By periodically measuring the open-circuit voltage of the battery, the relationship between it and the SOC is analyzed, and the estimated value of SOH is derived using this relationship. This method effectively identifies the battery aging process and performance degradation by dynamically monitoring the change in the open-circuit voltage of the battery cells, ensuring real-time evaluation of SOH.

[0102] Step 3.3, using the capacity estimated value determined based on the SOH estimated value of each battery cell, the SOC estimated value of each battery cell is obtained by the ampere-hour integration method, satisfying the following relationship:

[0103] In the formula, is the SOC estimated value of the th battery cell at the estimation moment, is the capacity estimated value of the th battery cell at the estimation moment; The method of combining ampere-hour integration and open-circuit voltage is used to realize the SOC estimation of all battery cells. Specifically, it includes integrating the current of the battery to calculate the accumulated charge, and combining the real-time measurement of the open-circuit voltage to construct an SOC estimation model. This model can adaptively adjust under different working conditions, improving the accuracy and stability of SOC estimation.

[0104] The fusion estimation of the SOC and SOH of each battery cell is realized through the capacity estimated value of each battery cell at different estimation moments.

[0105] Step 4, using the SOC estimated value and SOH estimated value of the characteristic battery cells obtained by the joint estimation to correct the SOC estimated value and SOH estimated value of each battery cell obtained by the fusion estimation.

[0106] The SOC and SOH of the characteristic battery cells are estimated by using a more accurate battery model, the combined algorithm of AEKF and AUKF, realizing high-precision estimation of the SOC and SOH of the characteristic battery cells; while the SOC and SOH of all battery cells are obtained by a simpler method, using a relatively simple battery model, combined with the ampere-hour integration method to realize the estimation of SOC and SOH, with lower accuracy; therefore, in Step 4, the high-precision estimation results of the SOC and SOH of the characteristic battery cells are used to correct the SOC and SOH of all battery cells, thereby reducing the overall calculation complexity while improving the estimation accuracy of all battery cells.

[0107] Constrained by the SOC estimated value of the characteristic battery cells obtained by the joint estimation, the SOC estimated value of each battery cell obtained by the fusion estimation is corrected according to the following relationship:

[0108] In the formula, is the correction value of the SOC estimated value of the th cell at the estimation moment, and is the SOC estimated value of the characteristic cell corresponding to the maximum terminal voltage at the estimation moment, and is the SOC estimated value of the characteristic cell corresponding to the minimum terminal voltage at the estimation moment, and is the SOC estimated value of the th cell at the estimation moment; With the SOH estimated value of the characteristic cell obtained by joint estimation as a constraint, the SOH estimated values of each cell obtained by fusion estimation are corrected by the following relational expression: When and at the time,

[0109] When at the time:

[0110] In the formula, is the correction value of the SOH estimated value of the th cell at the estimation moment, and is the minimum value of the SOH estimated values of each cell at the estimation moment, and is the maximum value of the SOH estimated values of each cell at the estimation moment, and is the SOH estimated value of the th cell at the estimation moment, and is the SOH estimated value of the characteristic cell corresponding to the maximum terminal voltage at the estimation moment, and is the SOH estimated value of the characteristic cell corresponding to the minimum terminal voltage at the estimation moment.

[0111] It can be seen from the above relational expression that the present invention corrects the SOC and SOH estimated values of all cells based on the high-precision SOC and SOH estimated values of the characteristic cells. By establishing a similarity relationship between the characteristic cells and other cells, the accurate state information of the characteristic cells is effectively propagated to the entire battery pack, ensuring more accurate state estimation of all cells in the group.

[0112] ​The present invention proposes a joint estimation algorithm for the SOC and SOH of characteristic battery cells that combines adaptive extended Kalman filtering and adaptive unscented Kalman filtering, which better adapts to the fast time-varying characteristics of the SOC of energy storage batteries and the non-linear characteristics of the SOH. The present invention also proposes a joint estimation algorithm for SOC and SOH that combines a high-precision battery model and a simple model, significantly reducing the computational complexity of the algorithm. An algorithm for calibrating the SOC and SOH estimation values of all battery cells with the high-precision SOC and SOH estimation values of characteristic battery cells is adopted to improve the estimation accuracy of the SOC and SOH of all battery cells. The method is applicable to various types of energy storage batteries, including lithium-ion batteries, sodium-ion batteries, lead-acid batteries, etc. By making corresponding model adjustments and parameter optimizations according to the characteristics of different battery types, the method can be widely applied to various energy storage systems. This method has low computational complexity and resource occupancy. Through efficient model simplification and algorithm optimization, the joint estimation of SOC and SOH can be completed with less computational resources in practical applications, ensuring the effectiveness and practicality of the method in large-scale energy storage systems.

[0113] The present invention also proposes a joint estimation system for the SOC and SOH of all battery cells in an energy storage system, including: A joint estimation module for the SOC and SOH of characteristic battery cells, which is used to measure the terminal voltage of each battery, and use the battery cell with the maximum terminal voltage and the battery cell with the minimum terminal voltage as characteristic battery cells; establish a circuit model of the characteristic battery cells; based on the circuit model of the characteristic battery cells, jointly estimate the SOC and SOH of the characteristic battery cells; A fusion estimation module for the SOC and SOH of each battery cell, which is used to fuse and estimate the SOC and SOH of each battery cell based on the circuit model of each battery cell; A joint estimation module for the SOC and SOH of all battery cells, which is used to correct the SOC estimation value and SOH estimation value of each battery cell obtained by fusion estimation with the SOC estimation value and SOH estimation value of the characteristic battery cells obtained by joint estimation, as the joint estimation result of the SOC and SOH of all battery cells in the energy storage system.

[0114] In the embodiment, the Long Short-Term Memory networks (LSTM) is adopted to implement the combined estimation of the SOC estimation value and SOH estimation value of the characteristic battery cells, and correct the SOC estimation value and SOH estimation value of each battery cell obtained by the fusion estimation. This process can be combined with an advanced monitoring and data analysis platform to utilize historical operation data, improve the estimation accuracy of the SOC and SOH of all batteries, and realize the intelligent monitoring and management of the entire energy storage battery system. In the monitoring and data analysis platform, a combined estimation algorithm for the SOC and SOH of the characteristic battery cells based on a high-precision model is deployed to make full use of the powerful hardware resources of the platform; the combined estimation algorithms for the SOC and SOH are redundantly deployed in the monitoring and data analysis platform and the energy storage battery management system as backups for each other to improve the operation reliability of the energy storage system; the combined estimation results of the SOC and SOH are combined with cloud computing and big data analysis technologies, and artificial intelligence algorithms are applied to realize the compensation and correction of the combined estimation results of the SOC and SOH of all single cells.

[0115] The present invention adopts an artificial intelligence algorithm based on LSTM to realize the compensation and correction of the combined estimation results of the SOC and SOH of all battery cells by the platform, and the steps are as follows: (1) Obtain the algorithm data set In the construction of the hybrid filtering algorithm, the parameters related to the SOC and SOH in each algorithm period constitute the algorithm input data set.

[0116]

[0117] In the formula, is the input data set at the estimation moment, … are the terminal voltages of battery cells 1, …, ; … are the currents of battery cells 1, …, ; … are the state-of-charge estimation values of battery cells 1, …, ; … are the capacity estimation values of battery cells 1, …, ; is the estimation moment, is the total number of battery cells.

[0118] Calculate the difference between the estimated value and the reference value in each estimation period, and construct the algorithm output data set.

[0119]

[0120] In the formula, is the The output data set at the estimated time, … For the SOC estimation values of battery cells 1, …, the difference between the estimated value and the reference value, … For the SOC estimation values of battery cells 1, …, the difference between the estimated value and the reference value of SOH.

[0121] (2) Training the LTSM neural network Input the algorithm input data set and the algorithm output data set into the LTSM neural network model for training until the model output error is less than the preset value.

[0122] (3) Algorithm compensation Deploy the LTSM neural network to compensate and correct the SOC and SOH estimation values output by the hybrid filtering algorithm.

[0123]

[0124] In the formula, , are respectively the prior estimate and the posterior estimate of the SOC of the th battery cell at the estimated time, , are respectively the prior estimate and the posterior estimate of the SOH of the th battery cell at the estimated time.

[0125] As described above, it is only part of the implementation principle of the present invention and does not impose any formal restrictions on the present invention. The parallel prediction methods using other total current prediction methods and total voltage prediction methods all fall within the scope of protection of the technical solution of the present invention.

[0126] The present disclosure may be a system, method, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions thereon for causing a processor to implement various aspects of the present disclosure.

[0127] A computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example—but not limited to—an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not construed as being a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0128] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0129] Computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for jointly estimating the SOC and SOH of all cells in an energy storage system, characterized in that: include: The terminal voltage of each battery is measured, and the cell with the largest terminal voltage and the cell with the smallest terminal voltage are taken as characteristic cells; Establish a circuit model of characteristic cells; Based on the circuit model of the characteristic cell, the SOC and SOH of the characteristic cell are jointly estimated; Based on the circuit model of each battery cell, the SOC and SOH of each battery cell are estimated in a fusion manner; The SOC estimated value and SOH estimated value of the characteristic battery cell obtained by the joint estimation are used to correct the SOC estimated value and SOH estimated value of each battery cell obtained by the fusion estimation, and the result is the joint estimation result of SOC and SOH of all the battery cells in the energy storage system.

2. The method for jointly estimating SOC and SOH of all cells of an energy storage system according to claim 1, characterized in that: The circuit model of the characteristic cell is a second-order RC circuit model, which includes: The first RC link and the second RC link.

3. The method for jointly estimating SOC and SOH of all cells of an energy storage system according to claim 2, characterized in that: The hybrid Kalman filter is constructed by using the adaptive extended Kalman filter and the adaptive unscented Kalman filter. Based on the circuit model of the characteristic cell, the SOC and SOH of the characteristic cell are jointly estimated, including: Based on the circuit model of the characteristic cell, the adaptive extended Kalman filter is used to estimate the SOC of the characteristic cell. The estimated SOC value of the characteristic cell at the estimation time is the first input in the SOH estimation of the characteristic cell based on the adaptive unscented Kalman filter. Estimating the observed values ​​of the measurement equation at the moment of estimation; The SOH of the characteristic cell is estimated based on the adaptive unscented Kalman filter. The a posteriori estimate of the characteristic cell capacity at the estimation time is the first input in the SOC estimation of the characteristic cell based on the adaptive extended Kalman filter. The input values ​​of the state-space equations at the estimation time.

4. The method for jointly estimating SOC and SOH of all cells of an energy storage system according to claim 3, characterized in that: Based on the circuit model of the characteristic cell, an adaptive extended Kalman filter is used to estimate the SOC of the characteristic cell, including: Based on the second-order RC circuit model of the characteristic battery cell, the state space equation and measurement equation for SOC estimation are constructed; After the initialization setting, the estimate of the measurement noise covariance matrix and the estimate of the noise covariance are improved; A priori estimation of the state vector and the error covariance is performed based on the estimated value of the improved noise covariance; Determine a Kalman gain matrix based on an estimated value of an improved measurement noise covariance matrix, and use the Kalman gain matrix to perform a posteriori estimation of a state vector and an error covariance based on a state space equation and a measurement equation for SOC estimation; From The state vector at the estimation time is obtained from the Estimation time characteristic battery Estimation results.

5. The method for jointly estimating SOC and SOH of all cells of an energy storage system according to claim 4, characterized in that: The state space equation and measurement equation for SOC estimation are as follows: In the formula, For the The state vector at the estimation time, For the System input for estimation time, is the process noise of the state-space equation, For the The terminal voltage measurement value of the battery cell at the estimation time, is the measurement noise of the measurement equation, For the The state matrix at the estimation time, For the The input matrix at the estimation moment, For the The observation matrix at the estimation time, For the Direct transfer matrix at the estimation moment; Among them, the characteristic battery is used in the Estimated time value , the voltage of the first RC link , the voltage of the second RC link Construction The state vector at the estimation time satisfies , with the Estimated current of characteristic cells at the time of evaluation As system input, satisfy .

6. The method for jointly estimating SOC and SOH of all cells of an energy storage system according to claim 5, characterized in that: The matrices in the state space equation and the measurement equation satisfy the following relationship: In the formula, is the equation iteration time step, = , and are the resistance and capacitance of the first RC link, = , and are the resistance and capacitance of the second RC link, For the Estimated capacity value of characteristic battery cell at the estimation time, is the charge and discharge efficiency of the characteristic battery cell, For the The open circuit voltage of the characteristic cell at the time of estimation, is the internal resistance of the characteristic cell, and is the polarization internal resistance of the characteristic battery cell.

7. The method for jointly estimating SOC and SOH of all cells of an energy storage system according to claim 6, characterized in that: The measurement noise covariance matrix The estimated value and noise covariance of The estimated value of is improved to satisfy the following relationship: In the formula, is the forgetting factor, and its value range is , , All are Estimation of the intermediate parameters constructed at the moment, For the The estimated value of the measurement noise covariance matrix at the estimation time, For the The estimated value of the noise covariance at the estimation time, For the The Kalman gain matrix at the estimation time, For the The estimated value of the state vector at the estimation time.

8. The method for jointly estimating SOC and SOH of all cells of an energy storage system according to claim 3, characterized in that: The SOH estimation of characteristic cells is performed based on the adaptive unscented Kalman filter, including: Based on the ampere-hour integration algorithm, using the Estimation time characteristic battery Estimation results, establish state space equations and measurement equations for SOH estimation; After the initialization setting, the capacity of the characteristic cell is estimated a priori, the error covariance of the output estimation, and the error covariance of the capacity a priori estimation and the output estimation of the characteristic cell are estimated. Estimated time and Estimated time The estimated value of the difference between the estimated results is used as the output estimate; The Kalman gain matrix is ​​determined by using the error covariance of the output estimation and the prior estimation of the capacity of the characteristic cell and the error covariance of the output estimation. The Kalman gain matrix is ​​used to perform a posteriori estimation of the capacity of the characteristic cell and a posteriori estimation of the covariance based on the state space equation and measurement equation for SOH estimation. Get the After estimating the a posteriori estimate of the capacity of the characteristic cell at the time of estimation, the SOH estimate is calculated by the following relationship: In the formula, For the Estimation time characteristic battery Estimation results, For the The posterior estimate of the capacity of the characteristic cell at the estimation time, is the rated capacity of the characteristic battery cell.

9. The method for jointly estimating SOC and SOH of all cells of an energy storage system according to claim 8, characterized in that: The state space equation and measurement equation for SOH estimation are as follows: In the formula, For the Estimated capacity of characteristic cells at the time of evaluation, is the system noise, For the Estimated time and Estimated time The difference between the estimated results, For the Estimation time characteristic battery Estimation results, is the charge and discharge efficiency of the characteristic battery cell, For the Estimated current of characteristic cells at the time of evaluation, is the equation iteration time step, To measure noise.

10. The method for jointly estimating SOC and SOH of all cells of an energy storage system according to claim 1, characterized in that: Based on the circuit model of each battery cell, the SOC and SOH of each battery cell are estimated by fusion, including: Based on the circuit model of the battery cell, identify the internal resistance of each battery cell; The internal resistance of each battery cell obtained by identification is used to correct the SOC of each battery cell calculated by combining the ampere-hour integration and the open circuit voltage. The SOC difference of each battery cell is used to estimate the SOH of each battery cell based on the open circuit voltage characteristics of the battery cell. Using the estimated capacity value determined based on the estimated SOH value of each battery cell, the estimated SOC value of each battery cell is obtained by the ampere-hour integration method.

11. The method for jointly estimating SOC and SOH of all cells of an energy storage system according to claim 10, characterized in that: The calculation formula for the estimated value of each battery cell SOH is as follows: In the formula, For the Estimated time Estimated capacity of each battery cell, For the Estimated time The accumulated charge capacity of each battery cell, For the Estimated time The SOC difference of each battery cell is For the Estimated time The estimated SOH value of each battery cell, is the rated capacity of the battery cell; In the formula, is the first Estimated time The SOC of each battery cell is obtained by using the first Internal resistance of a cell , calculate the The open circuit voltage value of each battery cell is obtained, and the SOC value corresponding to the open circuit voltage is obtained by the open circuit voltage table lookup method .

12. The method for jointly estimating SOC and SOH of all cells in an energy storage system according to claim 11, characterized in that: The estimated SOC value of each battery cell satisfies the following relationship: In the formula, For the Estimated time The estimated SOC value of each battery cell, is the charge and discharge efficiency of the battery cell, For the Estimated current of all cells at the time, For the Estimated time Estimated capacity of each battery cell, is the time step for the computation iteration.

13. The method for jointly estimating SOC and SOH of all cells of an energy storage system according to claim 1, characterized in that: Taking the SOC estimate of the characteristic cell obtained by joint estimation as a constraint, the SOC estimate of each cell obtained by fusion estimation is corrected by the following relationship: In the formula, For the Estimated time Correction value of the estimated SOC value of each battery cell, For the The estimated SOC value of the characteristic cell corresponding to the maximum terminal voltage at the estimation time, For the The estimated SOC value of the characteristic cell corresponding to the minimum terminal voltage at the estimation time, For the Estimated time The estimated SOC value of each battery cell.

14. The method for jointly estimating SOC and SOH of all cells in an energy storage system according to claim 1, characterized in that: Taking the SOH estimate of the characteristic cell obtained by joint estimation as a constraint, the SOH estimate of each cell obtained by fusion estimation is corrected by the following relationship: when hour, when hour: In the formula, For the Estimated time Correction value of the estimated SOH value of each battery cell, For the The minimum value of the estimated SOH value of each battery cell at the estimation time, For the The maximum value of the estimated SOH value of each cell at the estimation time, For the Estimated time The estimated SOH value of each battery cell, For the The estimated SOH value of the characteristic cell corresponding to the maximum terminal voltage at the estimation time, For the The estimated SOH value of the characteristic cell corresponding to the minimum terminal voltage at the estimation moment.

15. A system for jointly estimating SOC and SOH of all cells in an energy storage system, characterized in that: include: The SOC and SOH joint estimation module of the characteristic battery cell is used to measure the terminal voltage of each battery, and the battery cell with the largest terminal voltage and the battery cell with the smallest terminal voltage are used as the characteristic battery cell; Establish a circuit model of the characteristic battery cell; based on the circuit model of the characteristic battery cell, jointly estimate the SOC and SOH of the characteristic battery cell; The SOC and SOH fusion estimation module of each battery cell is used to fusion estimate the SOC and SOH of each battery cell based on the circuit model of each battery cell; The SOC and SOH joint estimation module for all battery cells is used to correct the SOC estimation value and SOH estimation value of each battery cell obtained by fusion estimation based on the SOC estimation value and SOH estimation value of the characteristic battery cell obtained by joint estimation, as the SOC and SOH joint estimation result of all battery cells of the energy storage system.

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