Battery SOH calculation method, electronic device and energy storage device

Abstract

The present application provides a battery SOH calculation method, electronic device and energy storage device. The battery SOH calculation method includes: obtaining the battery parameters of the battery at the previous moment, the battery parameters including the ohmic internal resistance; obtaining the measured parameters of the battery at the current moment; based on the dual Kalman filter algorithm and the ohmic internal resistance calculation formula, determining the battery parameters at the current moment according to the measured parameters and the battery parameters at the previous moment; determining the battery SOH estimation value according to the ohmic internal resistance in the battery parameters at the current moment; determining the target SOH value of the battery at the current moment according to the SOH estimation value. The battery SOH calculation method provided in the present application can improve the accuracy of calculating the battery SOH.

Description

Technical Field

[0001] The present application relates to the technical field of battery state estimation, and in particular to a battery SOH calculation method, electronic equipment and energy storage equipment. Background Art

[0002] As the battery is used, its life will gradually decrease, thus affecting its performance. The estimation of battery life in related technologies is based on the cumulative number of cycles or cumulative charge and discharge of the battery. However, due to the different working environment temperature, charge and discharge rates and other complex working conditions of the battery, the above methods cannot estimate the battery's state of health (SOH) well, and inaccurate SOH will bring certain risks to the use of the battery. Summary of the invention

[0003] In view of this, the present application provides a battery SOH calculation method, an electronic device and an energy storage device to improve the accuracy of battery SOH estimation.

[0004] The present application provides a first aspect of a battery SOH calculation method, including: obtaining battery parameters of the battery at the previous moment, the battery parameters including ohmic internal resistance; obtaining measurement parameters of the battery at the current moment; based on a dual Kalman filter algorithm and an ohmic internal resistance calculation formula, determining the battery parameters at the current moment according to the measurement parameters and the battery parameters at the previous moment; determining the battery's SOH estimation value according to the ohmic internal resistance in the battery parameters at the current moment; and determining the battery's target SOH value at the current moment according to the SOH estimation value.

[0005] In one embodiment, the battery parameters include a first battery parameter and a second battery parameter, the first battery parameter includes a polarization voltage and a battery SOC, the second battery parameter includes an ohmic internal resistance, a polarization resistance and a polarization capacitance, the measurement parameters include a battery current and a battery voltage of the battery, and based on a dual Kalman filter algorithm and an ohmic internal resistance calculation formula, the battery parameters at a current moment are determined according to the measurement parameters and the battery parameters at a previous moment, including: establishing a first state equation according to the ohmic internal resistance calculation formula and the first battery parameter; establishing a second state equation according to the second battery parameter; based on the Kalman filter algorithm, determining the first battery parameter at a current moment and the terminal voltage of the battery according to the measurement parameters at a current moment, the first state equation and the first battery parameter at a previous moment; based on the Kalman filter algorithm, determining the second battery parameter at a current moment according to the measurement parameters at a current moment, the second state equation, the second battery parameter at a previous moment, the terminal voltage and the first battery parameter at a current moment.

[0006] In one embodiment, based on the Kalman filter algorithm, the first battery parameter at the current moment and the terminal voltage of the battery are determined according to the measurement parameters at the current moment, the first state equation and the first battery parameter at the previous moment, including: obtaining the first covariance matrix at the previous moment; calculating the first a priori battery parameter at the current moment and the first a priori covariance at the current moment according to the first covariance matrix at the previous moment and the first battery parameter at the previous moment, the first a priori battery parameter at the current moment including the SOC a priori state value at the current moment; determining the battery open circuit voltage at the current moment according to the SOC a priori state value at the current moment; determining the first observation matrix at the current moment according to the battery open circuit voltage at the current moment and the SOC a priori state value at the current moment; based on the Kalman filter algorithm, determining the terminal voltage at the current moment, the first battery parameter and the first covariance matrix according to the first observation matrix, the first a priori covariance, the battery open circuit voltage and the first a priori battery parameter, the ohmic internal resistance at the previous moment and the measurement parameters at the current moment.

[0007] In one embodiment, based on the Kalman filtering algorithm, the second battery parameters at the current moment are determined according to the measurement parameters at the current moment, the second state equation, the second battery parameters at the previous moment, the terminal voltage and the first battery parameters at the current moment, including: obtaining the terminal voltage at the current moment, the second covariance matrix at the previous moment and the second observation matrix at the current moment; based on the Kalman filtering algorithm, the second battery parameters and the second covariance matrix at the current moment are determined according to the terminal voltage at the current moment, the second covariance matrix at the previous moment and the second observation matrix at the current moment.

[0008] In one embodiment, before obtaining the battery SOC at the current moment, the second covariance matrix at the previous moment and the second observation matrix at the current moment in the updated first battery parameter, the method also includes: obtaining the first derivative, first parameter, second parameter, third parameter and fourth parameter of the first battery parameter at the previous moment with respect to the second battery parameter at the current moment; determining the second derivative of the first battery parameter at the current moment with respect to the second battery parameter at the current moment based on the first derivative, first parameter and second parameter; determining the second observation matrix at the current moment based on the third parameter, fourth parameter and second derivative; and updating the first derivative based on the second observation matrix and the second derivative at the current moment.

[0009] In one embodiment, determining the estimated SOH value of the battery according to the ohmic internal resistance in the battery parameters at the current moment includes: performing a table lookup operation to determine the estimated SOH value according to the ohmic internal resistance at the current moment.

[0010] In one embodiment, determining the target SOH value of the battery at the current moment according to the SOH estimation value includes: obtaining the SOH display value of the battery at the previous moment; when the SOH estimation value is less than or equal to the target SOH value at the previous moment, determining the target SOH value at the current moment according to the SOH estimation value and the target SOH value at the previous moment.

[0011] In one embodiment, the method further includes: when the estimated SOH value is greater than the target SOH value at the previous moment, returning to the step of acquiring the battery parameters of the battery at the previous moment.

[0012] A second aspect of the present application provides an electronic device, including a processor and a memory for storing a program, wherein the program includes instructions, which, when executed by the processor, cause the processor to execute: obtaining battery parameters of the battery at the previous moment, the battery parameters including ohmic internal resistance; obtaining measured parameters of the battery at the current moment; determining the battery parameters at the current moment according to the measured parameters and the battery parameters at the previous moment based on a Kalman filter algorithm and an ohmic internal resistance calculation formula; determining an estimated SOH value of the battery according to the ohmic internal resistance in the battery parameters at the current moment; and determining a target SOH value of the battery at the current moment according to the estimated SOH value.

[0013] The third aspect of the present application provides an energy storage device, which includes a battery and a controller, and the controller is used to: obtain battery parameters of the battery at the previous moment, the battery parameters including ohmic internal resistance; obtain measurement parameters of the battery at the current moment; based on a Kalman filter algorithm and an ohmic internal resistance calculation formula, determine the battery parameters at the current moment according to the measurement parameters and the battery parameters at the previous moment; determine the SOH estimation value of the battery according to the ohmic internal resistance in the battery parameters at the current moment; determine the target SOH value of the battery at the current moment according to the SOH estimation value.

[0014] The battery SOH calculation method provided in the present application constructs a corresponding state equation based on the ohmic internal resistance calculation formula and battery characteristics, and predicts the ohmic internal resistance at the current moment based on the measurement parameters, the battery parameters at the previous moment, and the state equation based on the Kalman filter algorithm. Compared with the calculation of the ohmic internal resistance according to preset parameters in the related art, the method provided in the present application uses the Kalman filter algorithm to realize the iterative cycle calculation of the battery parameters, which can improve the accuracy of the battery parameters, that is, improve the accuracy of the calculated ohmic internal resistance. In this way, when determining the battery's SOH estimation value based on the ohmic internal resistance at the current moment, and determining the target SOH value at the current moment based on the SOH estimation value, the accuracy of the target SOH value can also be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present application and should not be regarded as limiting the scope of protection of the present application. In each of the drawings, similar components are numbered similarly.

[0016] Figure 1 This is a structural block diagram of an energy storage device involved in an embodiment of the present application.

[0017] Figure 2 A flowchart of a battery SOH calculation method provided in an embodiment of the present application.

[0018] Figure 3 A schematic diagram of a third-order RC equivalent circuit model involved in an embodiment of the present application.

[0019] Figure 4 Schematic diagram of the flow of sub-steps of step S203 in an embodiment of the present application.

[0020] Figure 5 Schematic diagram of the flow of sub-steps of step S403 in one embodiment of the present application.

[0021] Figure 6 Schematic diagram of the flow of sub-steps of step S404 in one embodiment of the present application.

[0022] Figure 7 This is a schematic diagram of some steps before executing step S701 in an embodiment of the present application.

[0023] Figure 8 A structural block diagram of an electronic device provided in one embodiment of the present application.

[0024] Fig. 9 A structural block diagram of a computer storage medium provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0026] It is understandable that the connection relationship described in this application refers to direct or indirect connection. For example, the connection between A and B can be either a direct connection between A and B or an indirect connection between A and B through one or more other electrical components. For example, A and C can be directly connected, and C and B can be directly connected, so that A and B are connected through C. It is also understandable that the "A connects B" described in this application can be a direct connection between A and B, or an indirect connection between A and B through one or more other electrical components.

[0027] In the description of this application, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is only a way to describe the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0028] In the description of this application, words such as "first" and "second" are only used to distinguish different objects, and do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit differences. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0030] Some embodiments will be described below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0031] As the battery is used, its life will gradually decrease, thus affecting its performance. The estimation of battery life in related technologies is based on the cumulative number of cycles or cumulative charge and discharge of the battery. However, due to the different working environment temperature, charge and discharge rates and other complex working conditions of the battery, the above methods cannot estimate the battery's state of health (SOH) well, and inaccurate SOH will bring certain risks to the use of the battery.

[0032] In view of this, the present application provides a battery SOH calculation method, an electronic device and an energy storage device to improve the accuracy of battery SOH estimation.

[0033] See also Figure 1 , Figure 1 The structure block diagram of the energy storage device to which the battery SOH calculation method provided in the present application is applied is shown. The energy storage device 10 includes a battery 11 and a controller 12. It is understandable that the battery SOH calculation method provided in the present application can be executed by the controller 12.

[0034] Please continue reading Figure 2 , Figure 2 A flow chart of a battery SOH calculation method provided in an embodiment of the present application. The method comprises the following steps:

[0035] Step S201: Obtain the battery parameters of the battery at the last moment, where the battery parameters include ohmic internal resistance.

[0036] Among them, the battery parameters may include polarization voltage, battery remaining capacity (State Of Charge, SOC), ohmic internal resistance, polarization resistance and polarization capacitance.

[0037] In the embodiment of the present application, the polarization voltages include U1, U2 and U3 as described above, the polarization resistors include R1, R2 and R3 as described above, and the polarization capacitors include C1, C2 and C3 as described above.

[0038] Accordingly, in other embodiments, when other equivalent circuit models are used, the polarization voltage, polarization resistance, and polarization capacitance may also include other parameters.

[0039] Step S202: Obtain the measurement parameters of the battery at the current moment.

[0040] In the embodiment of the present application, the measurement parameters include battery current and battery voltage.

[0041] Step S203: Based on the dual Kalman filter algorithm and the ohmic internal resistance calculation formula, the battery parameters at the current moment are determined according to the measured parameters and the battery parameters at the previous moment.

[0042] In some embodiments, the ohmic internal resistance calculation formula may be a formula obtained based on a battery equivalent circuit model. Figure 3 The third-order RC equivalent circuit model shown obtains the following ohmic internal resistance calculation formula.

[0043]

[0044] Among them, G represents the terminal voltage of the battery, OCV represents the open circuit voltage of the battery, I represents the current of the battery, R0 represents the ohmic internal resistance of the battery, R1 represents the ohmic polarization internal resistance, R2 represents the electrochemical polarization internal resistance, R3 represents the concentration polarization internal resistance, C1 represents the ohmic polarization capacitance, C2 represents the electrochemical polarization capacitance, C3 represents the concentration polarization capacitance, U1, U2 and U3 represent the first polarization voltage, the second polarization voltage and the third polarization voltage of the third-order equivalent circuit model respectively.

[0045] It is understandable that in other embodiments, the calculation formula of the ohmic internal resistance can also be obtained based on other equivalent circuit models, such as a second-order RC equivalent circuit model, etc., and the present application is not limited to this.

[0046] In step S203, a corresponding state equation can be constructed based on the ohmic internal resistance calculation formula and the battery characteristics, and based on the double Kalman filter algorithm, the battery parameters at the current moment can be predicted according to the measurement parameters, the battery parameters at the previous moment and the state equation, so that in each cycle, the battery parameters at the current moment are calculated iteratively to reduce the error in calculating the battery parameters at the current moment.

[0047] Step S204: determining the estimated SOH value of the battery according to the ohmic internal resistance in the battery parameters at the current moment.

[0048] In some embodiments, a table lookup operation may be performed to determine the SOH estimation value based on the ohmic internal resistance at the current moment.

[0049] Specifically, the battery can be tested to establish a lookup table of the ohmic internal resistance values ​​of the battery corresponding to different SOH values ​​and different SOCs at different temperatures, such as a three-dimensional map table, and the lookup table is stored in the storage space of the controller. In this way, after determining the ohmic internal resistance at the current moment in step S203, the controller can perform a table lookup operation based on the ohmic internal resistance to determine the estimated SOH value of the battery.

[0050] For example, a lookup table may be established for the ohmic internal resistance values ​​corresponding to 100%, 95%, 90% ... 70% SOH values ​​at -20°C, -15°C, -10°C, -5°C ... 55°C and 100%, 95%, 90% ... 0% SOC.

[0051] In some embodiments, in order to improve the accuracy of the SOH estimation value determined based on the ohmic internal resistance, after obtaining the ohmic internal resistance, when it is confirmed that the battery 11 maintains a stable state, the SOH estimation value of the battery is determined based on the ohmic internal resistance in the battery parameters at the current moment. For example, when the discharge current of the battery 11 within a preset time is greater than the first current threshold, and the fluctuation of the discharge current is less than the second current threshold, it can be confirmed that the battery 11 maintains a stable state. Among them, the preset time can be 5 seconds, the first current threshold can be 10 amperes, and the second current threshold can be 1 ampere. Accordingly, in some embodiments, after obtaining the ohmic internal resistance, when it is confirmed that the battery 11 is not in a stable state, it can return to step S201 to recalculate the SOH estimation value.

[0052] In other embodiments, the SOH estimation value can also be calculated based on the ohmic internal resistance, and the present application does not limit the specific calculation formula. It can be understood that after confirming the ohmic resistance in step S203, the battery parameters at the current moment are updated to the battery parameters at the previous moment, so that the cyclic operation of the battery SOH calculation method can be realized.

[0053] Step S205: Determine the target SOH value of the battery at the current moment according to the estimated SOH value.

[0054] In step S205 , the target SOH value at the current moment is calculated according to the estimated SOH value, so as to improve the accuracy of the target SOH value.

[0055] In summary, the battery SOH calculation method provided by the present application constructs a corresponding state equation based on the ohmic internal resistance calculation formula and battery characteristics, and predicts the ohmic internal resistance at the current moment based on the measurement parameters, the battery parameters at the previous moment, and the state equation based on the Kalman filter algorithm. Compared with the calculation of the ohmic internal resistance according to preset parameters in the related art, the method provided by the present application uses the Kalman filter algorithm to realize the iterative cycle calculation of the battery parameters, which can improve the accuracy of the battery parameters, that is, improve the accuracy of the calculated ohmic internal resistance. In this way, when determining the estimated SOH value of the battery according to the ohmic internal resistance at the current moment, and determining the target SOH value at the current moment according to the SOH estimated value, the accuracy of the target SOH value can also be improved.

[0056] See also Figure 4 In some embodiments, the battery parameters include a first battery parameter and a second battery parameter, the first battery parameter includes polarization voltage and battery SOC, the second battery parameter includes ohmic internal resistance, polarization resistance and polarization capacitance, and the measurement parameters include battery current and battery voltage of the battery. Step S203 includes the following sub-steps:

[0057] Step 401: Establish a first state equation according to an ohmic internal resistance calculation formula and a first battery parameter.

[0058] In the embodiment of the present application, the ohmic internal resistance calculation formula is:

[0059]

[0060] Please refer again Figure 3 According to the principle of circuit theory, the following formula can be obtained:

[0061] , after discretization, we can get ;

[0062] , after discretization, we can get ;

[0063] , after discretization, we can get ;

[0064] again ， is the battery capacity.

[0065] In this way, the above formula is sorted out and combined with the state equation in the Kalman filter algorithm , the following first state equation can be established with SOC and polarization voltage as state variables:

[0066]

[0067] in,

[0068] , , ,

[0069] U=E, and E is the identity matrix,

[0070] and , , and They are respectively the first polarization voltage during the last calculation, the second polarization voltage during the last calculation, the third polarization voltage during the last calculation, and the charge state value during the last calculation.

[0071] Step 402: Establish a second state equation according to the second battery parameter.

[0072] Understandably, according to the electrochemical characteristics of the battery, the battery parameters are stable within a certain period of time. In this way, combined with the state equation in the Kalman filter algorithm , the following second state equation can be established with ohmic internal resistance, polarization resistance and polarization capacitance as state variables

[0073]

[0074] in,

[0075] A is a 7×7 standard matrix, B=0, and the state variable .

[0076] Step 403: Based on the Kalman filter algorithm, determine the first battery parameter at the current moment and the terminal voltage of the battery according to the measurement parameter at the current moment, the first state equation and the first battery parameter at the previous moment.

[0077] Step 404: Based on the Kalman filter algorithm, determine the second battery parameter at the current moment according to the measurement parameter at the current moment, the second state equation, the second battery parameter at the previous moment, the terminal voltage and the first battery parameter at the current moment.

[0078] Thus, in step S403, the first battery parameter and the terminal voltage of the battery at the current moment are determined based on the Kalman filter algorithm and the first state equation, and in step S404, the second battery parameter at the current moment is determined based on the Kalman filter algorithm according to the first battery parameter at the current moment, the second battery parameter at the previous moment and the second state equation, thereby implementing the double Kalman filter algorithm through steps S403 and S404. Moreover, since the second battery parameter includes the ohmic internal resistance, the value of the ohmic internal resistance obtained by the double Kalman filter algorithm is more accurate, thereby improving the accuracy of the subsequent calculation of the target SOH value of the battery based on the ohmic internal resistance.

[0079] See also Figure 5 In some embodiments, step S403 includes the following sub-steps:

[0080] Step S501: Obtain the first covariance matrix of the previous moment.

[0081] The previous moment is the previous moment adjacent to the current moment. It is understandable that the time between the previous moment and the current moment can be set according to the cycle of executing the battery SOH calculation method, and this application does not limit the time between the previous moment and the current moment.

[0082] It is understandable that when the controller 12 completes power-on or receives the corresponding instruction, it can obtain various initial values ​​at the initial moment before executing the battery SOH calculation method, such as the initial value of the battery parameter or the initial value of the probability parameter based on the Kalman filter algorithm, the initial value of the noise parameter, etc., as the various parameter values ​​at the previous moment when the battery SOH calculation method is started. After the controller 12 calculates the various battery parameters at the current moment according to the various initial values ​​at the initial moment, the battery parameters, probability parameters and noise parameters at the current moment can be updated to the battery parameters, probability parameters and noise parameters at the previous moment, so as to execute the battery SOG calculation method at the next moment. That is to say, in the SOH calculation method provided by the present application, under normal circumstances, the controller 12 calculates the battery parameters at the current moment based on the offline parameters (i.e., the various initial values ​​at the initial moment) at the beginning of the execution of the battery SOH calculation method, and the controller 12 calculates the battery parameters at the current moment based on the online parameters (i.e., the various parameter values ​​determined at the previous moment). In this way, the battery parameters at the current moment can be determined according to the real-time operation status of the battery 11, thereby improving the accuracy of the determined battery parameters, thereby improving the accuracy of the target SOH value obtained based on the battery parameters.

[0083] Step S502: Calculate the first a priori battery parameter at the current moment and the first a priori covariance at the current moment according to the first covariance matrix at the previous moment and the first battery parameter at the previous moment, wherein the first a priori battery parameter at the current moment includes the SOC a priori state value at the current moment.

[0084] In some embodiments, the first a priori battery parameter and the first a priori covariance at the current moment may be determined based on the following a priori state value estimation equation and a priori covariance equation in the Kalman filter algorithm.

[0085] A priori state value estimation equation:

[0086] A priori covariance equation:

[0087] in, represents the first a priori battery parameter at the current moment, Indicates the first battery parameter at the previous moment, represents the first prior covariance at the current moment, represents the first covariance matrix of the previous moment, express A The transposed matrix of represents the process excitation noise covariance.

[0088] When the controller 12 just starts to execute the battery SOH calculation method, the first battery parameter initial value and the first covariance matrix initial value can be obtained as the first battery parameter and the first covariance matrix of the previous moment, respectively.

[0089] In some embodiments, the first battery parameter initial value and the initial value of the first covariance matrix Can be

[0090]

[0091] In some embodiments, the process excitation noise covariance Q may be

[0092]

[0093] It can be understood that the process excitation noise covariance Q can be a preset process excitation noise covariance, or can be a process excitation noise covariance updated in real time based on a Kalman filter algorithm, and the present application does not limit this.

[0094] Step S503: determining the battery open circuit voltage at the current moment according to the SOC priori state value at the current moment.

[0095] In some embodiments, the controller 12 may perform a table lookup operation based on the SOC-OCV lookup table to determine the battery open circuit voltage at the current moment according to the SOC priori state value at the current moment.

[0096] Step S504: Determine the first observation matrix at the current moment according to the battery open circuit voltage at the current moment and the SOC priori state value at the current moment.

[0097] In some embodiments, the first observation matrix is ​​the derivative of the battery open circuit voltage to the SOC priori state value at the current moment. For example, the first observation matrix

[0098]

[0099] Wherein, der is the derivative of the battery open circuit voltage OCV to the SOC prior state value at the current moment. In some embodiments, der is expressed as an array der

[20] obtained in a discrete manner, der[n]=(OCV1–OCV2) / 5%, where OCV1 is the OCV voltage at 5*n%SOC, OCV2 is the OCV voltage at 5*(n-1)%SOC, and the value range of n is 1-20. The OCV voltage is calculated with every 5% as a point, and OCV is the battery voltage after 5% discharge from full charge and standing for one hour, der

[20] =(100%OCV-95%OCV) / 5%; der

[19] =(95%OCV-90%OCV) / 5%, and so on, der[1]=(5%OCV-0%OCV) / 5%.

[0100] Step S505: Based on the Kalman filter algorithm, determine the terminal voltage, the first battery parameter and the first covariance matrix at the current moment according to the first observation matrix, the first prior covariance, the battery open circuit voltage and the first prior battery parameter, the ohmic internal resistance at the previous moment and the measurement parameters at the current moment.

[0101] Specifically, in some embodiments, step S505 includes the following sub-steps:

[0102] Step S601: Calculate the first Kalman gain matrix at the current moment according to the first observation matrix and the first priori covariance at the current moment.

[0103] In some embodiments, the first Kalman gain matrix at the current moment may be determined based on a Kalman filter gain calculation equation in a Kalman filter algorithm.

[0104] The first Kalman gain matrix is:

[0105] Wherein, K represents the first Kalman filter gain; represents the transposed matrix of the first observation matrix H1, R represents the observation noise variance, and R = 1. It can be understood that in other embodiments, the observation noise variance R can be a preset observation noise variance, or an observation noise variance updated in real time based on the Kalman filter algorithm, and the present application does not limit this.

[0106] Step S602: Determine the terminal voltage at the current moment according to the battery open circuit voltage at the current moment and the first a priori battery parameter, the ohmic internal resistance at the previous moment and the battery current at the current moment.

[0107] In this embodiment, the terminal voltage at the current moment is: ;in, .

[0108] Step S603: Determine the first battery a posteriori parameters and the first a posteriori covariance at the current moment according to the first a priori battery parameters, the first Kalman gain matrix, the battery voltage and the terminal voltage at the current moment.

[0109] In some embodiments, the first a posteriori battery parameter and the first a posteriori covariance at the current moment may be determined based on the following a posteriori state value estimation equation and a posteriori covariance equation in the Kalman filter algorithm.

[0110] A priori state value estimation equation:

[0111] A priori covariance equation:

[0112] in, represents the first battery posterior parameter at the current moment; Indicates battery voltage; represents the first posterior covariance at the current moment.

[0113] Step S604: updating the first battery posterior parameters and the first posterior covariance at the current moment to the first battery parameters and the first covariance matrix at the current moment respectively.

[0114] In this way, by executing the above steps provided in this embodiment, the terminal voltage at the current moment and the first battery parameter at the current moment can be determined in step S403.

[0115] See also Figure 6 In some embodiments, step S404 includes the following sub-steps:

[0116] Step S701: Obtain the terminal voltage at the current moment, the second covariance matrix at the previous moment, and the second observation matrix at the current moment.

[0117] In the embodiment of the present application, the second observation matrix is ​​the derivative of the battery voltage with respect to the second a priori battery parameter. The specific calculation process will be described in detail later.

[0118] Step S702: determining the second battery parameters and the second covariance matrix at the current moment according to the measurement parameters, the terminal voltage at the current moment, the second covariance matrix at the previous moment, and the second observation matrix at the current moment.

[0119] In some embodiments, step S702 includes the following sub-steps:

[0120] Step S801: determining the second a priori battery parameters and the second a priori covariance at the current moment according to the second battery parameters and the second covariance matrix at the previous moment.

[0121] It can be understood that since A in the second state equation is a 7×7 standard matrix and B=0, the second a priori battery parameter can be obtained: , the second prior covariance .in, Indicates the second battery parameter at the last moment, represents the second covariance matrix of the previous moment, and Q represents the process excitation noise covariance.

[0122] In some embodiments,

[0123]

[0124]

[0125] Step S802: Calculate the second Kalman gain matrix at the current moment according to the second prior covariance and the second observation matrix at the current moment.

[0126] In some embodiments, the second Kalman gain is: .in, is the transposed matrix of the second observation matrix H2, R=0.1.

[0127] Step S803: Determine the second a posteriori battery parameters and the second a posteriori covariance at the current moment according to the battery voltage, the terminal voltage at the current moment, the second a priori covariance at the current moment, the second Kalman gain matrix at the current moment and the second observation matrix at the current moment.

[0128] In some embodiments, the second a posteriori battery parameter is: ; Where V is the battery voltage and G is the terminal voltage at the current moment.

[0129] The second posterior covariance is: ; Where eye(7) is a 7×7 matrix.

[0130] Step S804: updating the second a posteriori battery parameters and the second a posteriori covariance at the current moment to the second battery parameters and the second covariance matrix at the current moment respectively.

[0131] In this way, by executing the above steps provided in this embodiment, the second battery parameter at the current moment can be determined in step S404.

[0132] Furthermore, the controller 12 may record the first battery parameter and the second battery parameter at the current moment as the battery parameter at the current moment.

[0133] As mentioned above, the second observation matrix is ​​the derivative of the battery voltage with respect to the second a priori battery parameter. Introducing partial derivatives into this formula, we can get

[0134] , in .

[0135] After simplifying the calculation, we get

[0136] H2 =

[0137]

[0138] again , k1 is the Kalman gain after substituting the state equation constructed with X as the state vector into the Kalman filter formula, and der is the derivative of SOC with respect to OCV. In this way, we can get

[0139]

[0140] It can be seen from the above that the second observation matrix can be obtained by cyclically iteratively calculating the battery parameters at the previous moment and the battery parameters at the current moment.

[0141] So, see Figure 7 Before executing step S701, the method further includes:

[0142] Step S901: Obtain a first derivative E1 of a first battery parameter at a previous moment with respect to a second battery parameter at a current moment, a first parameter F, a second parameter J, a third parameter C, and a fourth parameter D.

[0143] in,

[0144] The first parameter F=

[0145] ;

[0146] The second parameter J= ;

[0147] The third parameter C= ;

[0148] The fourth parameter D= .

[0149] Step S902: determining a second derivative of the first battery parameter at the current moment with respect to the second battery parameter at the current moment according to the first derivative, the first parameter and the second parameter.

[0150] In some embodiments, the second derivative E2 may be calculated according to the following equation.

[0151] E2=F+J*E1.

[0152] Step S903: Determine the second observation matrix at the current moment according to the third parameter, the fourth parameter and the second derivative.

[0153] In some embodiments, the second observation matrix H2 may be calculated according to the following equation.

[0154] H2=C+D*E2.

[0155] Step S904: Update the first derivative according to the second observation matrix and the second derivative at the current moment.

[0156] In some embodiments, the first derivative may be updated according to the following equation.

[0157] E1=E2-K1*H2.

[0158] Among them, K1 is the Kalman gain after the state equation is constructed with X as the state vector and substituted into the Kalman filter formula.

[0159] In this way, when the first derivative is obtained to calculate the second observation matrix H2 at the next moment, the second observation matrix H2 can be calculated iteratively based on the historical battery parameters, thereby improving the accuracy of the second observation matrix H2 calculated at the current moment.

[0160] In this way, by executing the above steps S901 to S903, the second observation matrix H2 can be calculated based on the battery parameters at the previous moment and the battery parameters at the current moment.

[0161] In some embodiments, in step S205 , the estimated SOH value may be directly used as the target SOH value of the battery at the current moment.

[0162] In some other embodiments, step S205 includes the following sub-steps:

[0163] Step S101: Obtain the target SOH value of the battery at the last moment.

[0164] It is understandable that when the controller 12 just executes the battery SOH calculation method, the target SOH value at the last moment may be the target SOH value or the preset SOH value obtained by the battery in the last power-on cycle.

[0165] Step S102: when the estimated SOH value is less than or equal to the target SOH value at the previous moment, determining the target SOH value at the current moment according to the estimated SOH value and the target SOH value at the previous moment.

[0166] Understandably, as the usage time of the battery 11 increases, the SOH of the battery generally decreases. Therefore, when the estimated SOH value is less than or equal to the target SOH value at the previous moment, it indicates that the accuracy of the currently obtained estimated SOH value is high and can be used in subsequent calculations. In some embodiments, when the estimated SOH value is greater than the target SOH value at the previous moment, the process returns to step 201 to recalculate the estimated SOH value.

[0167] In some embodiments, determining the target SOH value at the current moment according to the estimated SOH value and the target SOH value at the previous moment may include:

[0168] The adjustment coefficient is obtained, and the target SOH value at the current moment is determined according to the adjustment coefficient, the estimated SOH value, and the target SOH value at the previous moment.

[0169] For example, the target SOH value can be calculated according to the following formula:

[0170] SOH tar =SOH est *b1+ SOH tar_1 *b2+c

[0171] Among them, SOH tar Indicates the target SOH value at the current moment; SOH est Indicates the estimated SOH value; SOH tar_1 Indicates the target SOH value at the previous moment, b1, b2 and c are adjustment coefficients, where the value range of b1 and b2 is [0,1], and c can be any value. For example, b1 and b2 are both 0.5, and c is 0.

[0172] In this way, in step S102, the target SOH value at the current moment is calculated by combining the estimated SOH value and the target SOH value at the previous moment, which can prevent sudden changes in SOH and cause large errors.

[0173] In some embodiments, when the estimated SOH value is less than or equal to the target SOH value at the last moment, the battery SOH calculation method further includes: updating the target SOH value at the current moment to the target SOH value at the last moment. In this way, the battery SOH calculation method can be cyclically operated.

[0174] See also Figure 8 The present application provides an electronic device 20, including a processor 21, and a memory 22 storing a program. The program includes instructions, and when the instructions are executed by the processor 21, the processor 21 executes: obtaining battery parameters of the battery at the last moment, the battery parameters including ohmic internal resistance;

[0175] Get the measurement parameters of the battery at the current moment;

[0176] Based on the Kalman filter algorithm and the ohmic internal resistance calculation formula, the battery parameters at the current moment are determined according to the measured parameters and the battery parameters at the previous moment;

[0177] The SOH estimation value of the battery is determined according to the ohmic internal resistance in the battery parameters at the current moment; and the target SOH value of the battery at the current moment is determined according to the SOH estimation value.

[0178] On the other hand, please refer again to Figure 1 The embodiment of the present application further provides an energy storage device 10, including: a battery 11 and a controller 12. The controller 12 is used to execute the battery SOH calculation method provided in the embodiment of the present application.

[0179] In the embodiment of the present application, a more detailed functional description of each of the above modules can be referred to the corresponding content of the aforementioned part, which will not be repeated here.

[0180] See also Fig. 9 The embodiment of the present application further provides a computer storage medium 30, which stores a computer program 31. When the computer program 31 is executed by a processor, the processor executes the above-mentioned battery SOH calculation method.

[0181] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer storage medium or transmitted through the computer storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that contains one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0182] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes. In the absence of conflict, the technical features in this embodiment and the implementation scheme can be combined arbitrarily.

[0183] The present application is not limited to the above specific implementations. A person skilled in the art can easily understand that there are many alternatives to the test fixture of the present application without departing from the principle and scope of the present application. The scope of protection of the present application shall be subject to the content of the claims.

Claims

1. A battery SOH calculation method, characterized in that: include: Obtaining battery parameters of the battery at the last moment, the battery parameters including a first battery parameter and a second battery parameter, the first battery parameter including a polarization voltage and a battery SOC, and the second battery parameter including an ohmic internal resistance, a polarization resistance, and a polarization capacitance; Acquire measurement parameters of the battery at a current moment, wherein the measurement parameters include a battery current and a battery voltage of the battery; Based on the dual Kalman filter algorithm and the ohmic internal resistance calculation formula, the battery parameters at the current moment are determined according to the measured parameters and the battery parameters at the previous moment, including: Establishing a first state equation according to the ohmic internal resistance calculation formula and the first battery parameter; Establishing a second state equation according to the second battery parameter; Based on the Kalman filter algorithm, determining the first battery parameter at the current moment and the terminal voltage of the battery according to the measurement parameter at the current moment, the first state equation and the first battery parameter at the previous moment, including: Get the first covariance matrix of the previous moment; Calculating a first a priori battery parameter at a current moment and a first a priori covariance at a current moment according to the first covariance matrix at a previous moment and the first battery parameter at a previous moment, wherein the first a priori battery parameter at a current moment includes a SOC a priori state value at a current moment; Determine the battery open circuit voltage at the current moment according to the SOC priori state value at the current moment; Determine a first observation matrix at a current moment according to the battery open circuit voltage at a current moment and the SOC priori state value at a current moment; Based on the Kalman filter algorithm, determine the terminal voltage at the current moment, the first battery parameter and the first covariance matrix according to the first observation matrix, the first a priori covariance, the battery open circuit voltage and the first a priori battery parameter, the ohmic internal resistance at the previous moment and the measured parameter at the current moment; Based on the Kalman filter algorithm, determining the second battery parameter at the current moment according to the measurement parameter at the current moment, the second state equation, the second battery parameter at the previous moment, the terminal voltage and the first battery parameter at the current moment, including: Obtaining a first derivative of the first battery parameter at a previous moment with respect to the second battery parameter at a current moment, a first parameter, a second parameter, a third parameter, and a fourth parameter; Determine a second derivative of the first battery parameter at a current moment with respect to the second battery parameter at a current moment according to the first derivative, the first parameter, and the second parameter; Determine a second observation matrix at a current moment according to the third parameter, the fourth parameter and the second derivative; updating the first derivative according to the second observation matrix and the second derivative at the current moment; Obtaining the terminal voltage at the current moment, the second covariance matrix at the previous moment, and the second observation matrix at the current moment; Based on the Kalman filter algorithm, determining the second battery parameter and the second covariance matrix at the current moment according to the terminal voltage at the current moment, the second covariance matrix at the previous moment, and the second observation matrix at the current moment; Determine an estimated SOH value of the battery according to the ohmic internal resistance in the battery parameters at the current moment; Determining a target SOH value of the battery at a current moment according to the estimated SOH value, wherein determining the target SOH value of the battery at a current moment according to the estimated SOH value includes: Obtaining the target SOH value of the battery at the last moment; When the estimated SOH value is less than or equal to the target SOH value at the previous moment, the target SOH value at the current moment is determined according to the estimated SOH value and the target SOH value at the previous moment.

2. The method according to claim 1, characterized in that: The determining the estimated SOH value of the battery according to the ohmic internal resistance in the battery parameters at the current moment includes: A table lookup operation is performed to determine the SOH estimation value according to the ohmic internal resistance at a current moment.

3. The method according to claim 1, characterized in that The method further comprises: When the estimated SOH value is greater than the target SOH value at the last moment, the process returns to the step of obtaining the battery parameters of the battery at the last moment.

4. An electronic device comprising a processor and a memory storing a program, characterized in that: The program includes instructions which, when executed by the processor, cause the processor to: Obtaining battery parameters of the battery at the last moment, the battery parameters including a first battery parameter and a second battery parameter, the first battery parameter including polarization voltage and battery SOC, the second battery parameter including ohmic internal resistance, polarization resistance and polarization capacitance; Acquire measurement parameters of the battery at a current moment, wherein the measurement parameters include a battery current and a battery voltage of the battery; Based on the dual Kalman filter algorithm and the ohmic internal resistance calculation formula, the battery parameters at the current moment are determined according to the measured parameters and the battery parameters at the previous moment, including: Establishing a first state equation according to the ohmic internal resistance calculation formula and the first battery parameter; Establishing a second state equation according to the second battery parameter; Based on the Kalman filter algorithm, determining the first battery parameter at the current moment and the terminal voltage of the battery according to the measurement parameter at the current moment, the first state equation and the first battery parameter at the previous moment, including: Get the first covariance matrix of the previous moment; Calculating a first a priori battery parameter at a current moment and a first a priori covariance at a current moment according to the first covariance matrix at a previous moment and the first battery parameter at a previous moment, wherein the first a priori battery parameter at a current moment includes a SOC a priori state value at a current moment; Determine the battery open circuit voltage at the current moment according to the SOC priori state value at the current moment; Determine a first observation matrix at a current moment according to the battery open circuit voltage at a current moment and the SOC priori state value at a current moment; Based on the Kalman filter algorithm, determine the terminal voltage at the current moment, the first battery parameter and the first covariance matrix according to the first observation matrix, the first a priori covariance, the battery open circuit voltage and the first a priori battery parameter, the ohmic internal resistance at the previous moment and the measured parameter at the current moment; Based on the Kalman filter algorithm, determining the second battery parameter at the current moment according to the measurement parameter at the current moment, the second state equation, the second battery parameter at the previous moment, the terminal voltage and the first battery parameter at the current moment, including: Obtaining a first derivative of the first battery parameter at a previous moment with respect to the second battery parameter at a current moment, a first parameter, a second parameter, a third parameter, and a fourth parameter; Determine a second derivative of the first battery parameter at a current moment with respect to the second battery parameter at a current moment according to the first derivative, the first parameter, and the second parameter; Determine a second observation matrix at a current moment according to the third parameter, the fourth parameter and the second derivative; updating the first derivative according to the second observation matrix and the second derivative at the current moment; Obtaining the terminal voltage at the current moment, the second covariance matrix at the previous moment, and the second observation matrix at the current moment; Based on the Kalman filter algorithm, determining the second battery parameter and the second covariance matrix at the current moment according to the terminal voltage at the current moment, the second covariance matrix at the previous moment, and the second observation matrix at the current moment; Determine an estimated SOH value of the battery according to the ohmic internal resistance in the battery parameters at the current moment; Determining a target SOH value of the battery at a current moment according to the estimated SOH value, wherein determining the target SOH value of the battery at a current moment according to the estimated SOH value includes: Obtaining the target SOH value of the battery at the last moment; When the estimated SOH value is less than or equal to the target SOH value at the previous moment, the target SOH value at the current moment is determined according to the estimated SOH value and the target SOH value at the previous moment.

5. An energy storage device, characterized in that: The energy storage device includes a battery and a controller, and the controller is used for: Obtaining battery parameters of the battery at the last moment, the battery parameters including a first battery parameter and a second battery parameter, the first battery parameter including a polarization voltage and a battery SOC, and the second battery parameter including an ohmic internal resistance, a polarization resistance, and a polarization capacitance; Obtaining measurement parameters of the battery at the current moment; Based on the dual Kalman filter algorithm and the ohmic internal resistance calculation formula, the battery parameters at the current moment are determined according to the measured parameters and the battery parameters at the previous moment, including: Establishing a first state equation according to the ohmic internal resistance calculation formula and the first battery parameter; Establishing a second state equation according to the second battery parameter; Based on the Kalman filter algorithm, determining the first battery parameter at the current moment and the terminal voltage of the battery according to the measurement parameter at the current moment, the first state equation and the first battery parameter at the previous moment, including: Get the first covariance matrix of the previous moment; Calculating a first a priori battery parameter at a current moment and a first a priori covariance at a current moment according to the first covariance matrix at a previous moment and the first battery parameter at a previous moment, wherein the first a priori battery parameter at a current moment includes a SOC a priori state value at a current moment; Determine the battery open circuit voltage at the current moment according to the SOC priori state value at the current moment; Determine a first observation matrix at a current moment according to the battery open circuit voltage at a current moment and the SOC priori state value at a current moment; Based on the Kalman filter algorithm, determine the terminal voltage at the current moment, the first battery parameter and the first covariance matrix according to the first observation matrix, the first a priori covariance, the battery open circuit voltage and the first a priori battery parameter, the ohmic internal resistance at the previous moment and the measured parameter at the current moment; Based on the Kalman filter algorithm, determining the second battery parameter at the current moment according to the measurement parameter at the current moment, the second state equation, the second battery parameter at the previous moment, the terminal voltage and the first battery parameter at the current moment, including: Obtaining a first derivative of the first battery parameter at a previous moment with respect to the second battery parameter at a current moment, a first parameter, a second parameter, a third parameter, and a fourth parameter; Determine a second derivative of the first battery parameter at a current moment with respect to the second battery parameter at a current moment according to the first derivative, the first parameter, and the second parameter; Determine a second observation matrix at a current moment according to the third parameter, the fourth parameter and the second derivative; updating the first derivative according to the second observation matrix and the second derivative at the current moment; Obtaining the terminal voltage at the current moment, the second covariance matrix at the previous moment, and the second observation matrix at the current moment; Based on the Kalman filter algorithm, determining the second battery parameter and the second covariance matrix at the current moment according to the terminal voltage at the current moment, the second covariance matrix at the previous moment, and the second observation matrix at the current moment; Determine an estimated SOH value of the battery according to the ohmic internal resistance in the battery parameters at the current moment; Determining a target SOH value of the battery at a current moment according to the estimated SOH value, wherein determining the target SOH value of the battery at a current moment according to the estimated SOH value includes: Obtaining the target SOH value of the battery at the last moment; When the estimated SOH value is less than or equal to the target SOH value at the previous moment, the target SOH value at the current moment is determined according to the estimated SOH value and the target SOH value at the previous moment.

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