SOC and SOH estimation method and system of power battery and medium

Abstract

The invention provides an SOC and SOH estimation method and system of a power battery and a medium. The method comprises the following steps of iteratively executing the following steps in the charging and discharging process of the power battery: determining model parameters of a circuit equivalent model of a battery pack of the power battery; measuring state parameters of the battery pack at the current moment, wherein the state parameters comprise battery end voltage and charging and discharging current; according to the model parameters and the state parameters, estimating the battery state of the battery pack by adopting a dual-extended Kalman filter; and obtaining the SOC estimation value, the SOH estimation value and the estimation error of the battery pack when the estimation result converges. According to the method, high-precision estimation of the SOC and SOH of the battery pack can be realized, and the estimation speed can be improved while the estimation precision is kept, so that the reliability and the safety of the power battery are ensured; furthermore, the method has relatively high calculation efficiency and relatively high applicability.

Description

Technical Field

[0001] The present invention relates to the technical field of power battery management, and in particular to a method and system for estimating the SOC and SOH of a power battery, and a readable storage medium. Background Art

[0002] As the main source of energy for new energy vehicles, the power battery (battery pack) must work safely and reliably, so the battery management system needs to control it accurately and reliably. When designing a battery management system, the state of charge (SOC) and state of health (SOH) are key parameters of the power battery, where SOC is a parameter indicating the internal charge state of the power battery, and SOH is a parameter indicating the health state of the power battery. These parameters are very important for protecting the power battery, extending the life of the power battery, and ensuring the safety of user operations. Since the SOC and SOH of the power battery cannot be measured directly, and there is no one-to-one linear relationship between its voltage and SOC during the charging and discharging process of the power battery; further, with the changes in temperature and current, the relationship between voltage and SOC is more variable and may be accompanied by delays or hysteresis. Therefore, the voltage obtained by actual measurement cannot be directly converted into SOC. Based on this, the estimation method is usually used to obtain the SOC value and SOH value.

[0003] In the prior art, one of the commonly used estimation methods is to estimate the SOC value by summing / integrating the charge entering or leaving the battery. However, since the influence of measurement errors and the like is not taken into account, the SOC value obtained by this estimation method is often not very accurate. In addition, this method can only be used when the initial state of the battery SOC is known, and its applicability is poor; another method is to estimate the SOC by constant current discharge using the characterization parameter method. However, the constant current discharge SOC estimation experiment is only applicable to the experimental environment, and the experiment requires a long period of rest until the power battery is in a steady state before measuring the data, so the applicability of the remaining capacity method is also poor.

[0004] It should be noted that the information disclosed in the background technology section of the invention is only intended to deepen the understanding of the general background technology of the invention, and should not be regarded as an admission or suggestion in any form that the information constitutes prior art already known to those skilled in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system and readable storage medium for estimating the SOC and SOH of a power battery in order to address one or more of the problems of low accuracy and poor applicability of the power battery SOC estimation method in the prior art. The present invention can achieve high-precision estimation of the SOC and SOH of a battery pack, and can improve the estimation speed while maintaining the estimation accuracy, thereby ensuring the reliability and safety of the power battery; further, the present invention has high computational efficiency and strong applicability.

[0006] In order to achieve the above object, the present invention is implemented by the following technical solution: a method for estimating the SOC and SOH of a power battery, comprising iteratively performing the following steps during the charging and discharging process of the power battery:

[0007] Determining model parameters of a circuit equivalent model of a battery pack of the power battery;

[0008] Measuring the state parameters of the battery pack at the current moment, wherein the state parameters include the battery terminal voltage and the charge and discharge current;

[0009] estimating the battery state of the battery pack using a double extended Kalman filter according to the model parameters and the state parameters;

[0010] The SOC estimation value, SOH estimation value and estimation error of the battery pack are obtained when the estimation results converge.

[0011] Optionally, before determining the model parameters of the circuit equivalent model, the estimation method further includes:

[0012] A circuit equivalent model of the battery pack is established; the circuit equivalent model includes a second-order circuit equivalent model; the second-order circuit equivalent model includes a voltage source and a series resistor, a first RC circuit and a second RC circuit connected in series with the voltage source in sequence; wherein the first RC circuit and the second RC circuit both include a resistor and a capacitor connected in parallel.

[0013] Optionally, determining the model parameters of the circuit equivalent model of the battery pack includes:

[0014] Predicting an open circuit voltage state of the battery pack according to a predicted state of charge of the battery pack, wherein the predicted state of charge of the battery pack is calculated using a counting equation;

[0015] Acquire the first RC circuit voltage and the second RC circuit voltage according to the current law, and convert the first RC circuit voltage and the second RC circuit voltage into a discrete time model;

[0016] Obtaining a parameter state equation of the circuit equivalent model according to the charge prediction state and the first RC circuit voltage and the second RC circuit voltage converted into a discrete time model;

[0017] Obtaining a predicted open circuit voltage of the battery pack according to a parameter state equation of the circuit equivalent model and the open circuit voltage state;

[0018] Minimize the error between the predicted open circuit voltage and the battery terminal voltage to obtain model parameters of the circuit equivalent model; wherein the model parameters include the resistance value of the series resistor, the resistance value of the resistor and the capacitance value of the capacitor of the first RC circuit, and the resistance value of the resistor and the capacitance value of the capacitor of the second RC circuit.

[0019] Optionally, the estimating the battery state of the battery pack by using a double extended Kalman filter includes:

[0020] Using a first extended Kalman filter to obtain an estimated voltage of the battery pack, and obtaining an estimated SOC value of the battery pack according to the estimated voltage;

[0021] A second extended Kalman filter is used to obtain an estimated internal resistance and an estimated capacity of the battery pack, and an estimated SOH value of the battery pack is calculated based on the estimated internal resistance or the estimated capacity.

[0022] Optionally, before adopting the first extended Kalman filter to obtain the estimated voltage of the battery pack, the method further includes: establishing a state equation and an observation equation of the first extended Kalman filter according to the circuit equivalent model.

[0023] Optionally, before adopting the second extended Kalman filter to obtain the estimated internal resistance and estimated capacity of the battery pack, the method further includes: establishing a state equation and an observation equation of the second extended Kalman filter according to the battery internal resistance and battery capacity of the battery pack.

[0024] Optionally, the estimating the battery state of the battery pack by using a double extended Kalman filter comprises iteratively executing the following steps until the battery state converges:

[0025] Performing a first state prediction and a first covariance error prediction on the battery pack using a first extended Kalman filter;

[0026] Performing a second state prediction and a second covariance error prediction on the battery pack using a second extended Kalman filter;

[0027] Using a first extended Kalman filter to obtain a first Kalman gain, and updating the first state prediction and updating the first covariance error;

[0028] A second extended Kalman filter is used to obtain a second Kalman gain, and the second state prediction and the second covariance error are updated.

[0029] Optionally, calculating the estimated SOH value of the battery pack according to the estimated internal resistance or the estimated capacity includes:

[0030] Obtaining an estimated SOH value of the battery pack according to the estimated internal resistance and a calibrated internal resistance of the battery pack;

[0031] Alternatively, an estimated SOH value of the battery pack is obtained according to the estimated capacity and the calibrated capacity of the battery pack.

[0032] In order to achieve the above-mentioned purpose, the present invention also provides a battery management system, which includes a processor and a memory, and a computer program is stored in the memory. When the computer program is executed by the processor, the SOC and SOH estimation method of the power battery described in any one of the above items is implemented.

[0033] In order to achieve the above-mentioned object, the present invention further provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the SOC and SOH estimation method of the power battery described in any one of the above-mentioned items is implemented.

[0034] Compared with the prior art, the SOC and SOH estimation method, system and readable storage medium of a power battery provided by the present invention have the following advantages:

[0035] The present invention provides a method for estimating the SOC and SOH of a power battery. The model parameters of the circuit equivalent model of the battery pack are determined during the charging and discharging process of the power battery, so that the characteristics of the battery pack can be accurately described, thereby laying a solid foundation for realizing high-precision estimation of the SOC and SOH of the battery pack. Furthermore, the dual extended Kalman filter can estimate the fast-changing state (such as the SOC value of the battery pack) and the slow-changing state (such as the internal resistance and capacity of the battery pack) respectively, effectively solving the nonlinear problem of battery state change, thereby significantly improving the accuracy of SOC and SOH estimation. Furthermore, the dual extended Kalman filter is used to combine the Kalman filter with the extended state vector, so that the estimation speed can be improved while maintaining the estimation accuracy, thereby ensuring the reliability and safety of the power battery. Further, since the SOC and SOH estimation method of the power battery provided by the present invention can be applied to the SOC and SOH estimation of the power battery charging process as well as the power battery discharging process. During the charging process, accurate SOC estimation can help determine the charging progress and the remaining charging time, improve charging efficiency and user experience. During the discharge process (vehicle driving), accurate SOH estimation helps determine the health of the battery, detect potential faults and abnormal conditions in advance, and ensure the reliability and safety of the battery pack. Furthermore, the present invention does not impose too many restrictions on the type of the power battery, and has high calculation efficiency and strong applicability.

[0036] Since the battery management system and the readable storage medium provided by the present invention belong to the same inventive concept as the SOC and SOH estimation method for the power battery provided by the present invention, the battery management system and the readable storage medium provided by the present invention at least have all the advantages of the SOC and SOH estimation method for the power battery provided by the present invention. For details on the beneficial effects of the battery management system and the readable storage medium provided by the present invention, please refer to the above description of the beneficial effects of the SOC and SOH estimation method for the power battery provided by the present invention, and no further details will be given here. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of the overall process of a method for estimating SOC and SOH of a power battery provided in one embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of a circuit equivalent model of a power battery according to one embodiment of the present invention;

[0039] Figure 3 A schematic diagram of an estimation process of a double extended Kalman filter according to one embodiment of the present invention;

[0040] Figure 4A schematic diagram of the block structure of a battery management system provided in one embodiment of the present invention;

[0041] The reference numerals are as follows:

[0042] Voltage source-E, series resistor-R0, first RC circuit-RC1, second RC circuit-RC2, resistors-R1, R2; capacitors-C1, C2, open circuit voltage-V ocv , the first RC circuit voltage -V1, the second RC circuit voltage -V2, the battery terminal voltage -V b , charge and discharge current -I;

[0043] Processor 110 , memory 120 , communication interface 130 , communication bus 140 . DETAILED DESCRIPTION

[0044] The following is a further detailed description of a method, system and readable storage medium for estimating the SOC and SOH of a power battery proposed by the present invention in conjunction with the accompanying drawings. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the drawings are in a very simplified form and are all in non-precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. In order to make the purposes, features and advantages of the present invention more obvious and easy to understand, please refer to the accompanying drawings. It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention. Any modification of the structure, change in the proportional relationship or adjustment of the size, in the case of the same or similar effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical content disclosed by the present invention. The specific design features of the present invention disclosed herein include, for example, specific dimensions, directions, positions and shapes, which will be determined in part by the specific environment to be applied and used. And, in the embodiments described below, sometimes the same figure mark is used in common between different drawings to represent the same part or part with the same function, and its repeated description is omitted. In this specification, similar reference numerals and letters are used to refer to similar items, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures. In addition, if the method described herein includes a series of steps, the order of these steps presented herein is not necessarily the only order in which these steps can be performed, and some of the steps described may be omitted and / or some other steps not described herein may be added to the method.

[0045] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements. The singular forms "a", "an" and "the" include plural objects, the term "or" is generally used in a sense including "and / or", the term "several" is generally used in a sense including "at least one", and the term "at least two" is generally used in a sense including "two or more". In addition, the terms "first", "second" and "third" are used for descriptive purposes only and are not to be understood as indicating or suggesting relative importance or implicitly indicating the number of the indicated technical features.

[0046] The core idea of ​​the present invention is to provide a method, system and readable storage medium for estimating the SOC and SOH of a power battery. The present invention can achieve high-precision estimation of the SOC and SOH of a battery pack, and can improve the estimation speed while maintaining the estimation accuracy, thereby ensuring the reliability and safety of the power battery; further, the present invention has high computational efficiency and strong applicability.

[0047] It should be noted that the SOC and SOH estimation method of the power battery provided by the present invention and the readable storage medium can be applied to the battery management system provided by the present invention, and the SOC and SOH estimation method, system and readable storage medium of the power battery provided by the present invention can be applied to vehicles. It should be understood that the terms "vehicle" or "vehicle" or other similar terms used herein include general motor vehicles, such as passenger vehicles including sports utility vehicles (SUVs), buses, trucks, various commercial vehicles, and hybrid vehicles, electric vehicles, and plug-in hybrid electric vehicles.

[0048] In order to realize the above idea, one embodiment of the present invention provides a method for estimating the SOC and SOH of a power battery. For example, see Figure 1 , Figure 1 The overall flow chart of the SOC and SOH estimation method of the power battery provided in one embodiment of this embodiment. Figure 1It can be seen that the method for estimating the SOC and SOH of the power battery provided in this embodiment includes iteratively performing the following steps during the charging and discharging process of the power battery:

[0049] S100: Determine model parameters of a circuit equivalent model of a battery pack of the power battery;

[0050] S200: measuring the state parameters of the battery pack at the current moment, wherein the state parameters include the battery terminal voltage and the charge and discharge current;

[0051] S300: estimating the battery state of the battery pack using a double extended Kalman filter according to the model parameters and the state parameters;

[0052] S400: Obtaining an estimated SOC value, an estimated SOH value, and an estimation error of the battery pack when the estimation result converges.

[0053] It can be seen that the present invention provides a method for estimating the SOC and SOH of a power battery, and the model parameters of the circuit equivalent model of the battery pack are determined during the charging and discharging process of the power battery, so that the characteristics of the battery pack can be accurately described, thereby laying a solid foundation for realizing high-precision estimation of the SOC and SOH of the battery pack. Further, the dual extended Kalman filter can estimate the fast-changing state (such as the SOC value of the battery pack) and the slow-changing state (such as the internal resistance and capacity of the battery pack) respectively, effectively solving the nonlinear problem of battery state change, thereby significantly improving the accuracy of SOC and SOH estimation. Further, the dual extended Kalman filter is used to combine the Kalman filter with the extended state vector, so that the estimation speed can be improved while maintaining the estimation accuracy, thereby ensuring the reliability and safety of the power battery. Further, since the SOC and SOH estimation method of the power battery provided by the present invention can be applied to the SOC and SOH estimation of the power battery charging process as well as the power battery discharging process. During the charging process, accurate SOC estimation can help determine the charging progress and the remaining charging time, improve charging efficiency and user experience. During the discharge process (vehicle driving), accurate SOH estimation helps determine the health of the battery, detect potential faults and abnormal conditions in advance, and ensure the reliability and safety of the battery pack. Furthermore, the present invention does not impose too many restrictions on the type of the power battery, and has high calculation efficiency and strong applicability.

[0054] It should be noted that, as can be understood by those skilled in the art, the present invention does not impose too many restrictions on the type of the power battery. The power battery may be, but is not limited to, a ternary lithium battery, a lithium iron phosphate battery, a lithium titanate battery, etc. Further, the present invention does not impose too many restrictions on the repetitive strategy for executing steps S100, S200, S300, and S400. For example, in some embodiments, for example, it is executed at a fixed period according to a preset sampling frequency (for example, once every 1s, that is, the time difference between the current sampling moment and the next sampling moment is 1s); in some other embodiments, different execution cycles may also be used according to the interval in which the SOC value of the battery pack is located, for example, when the SOC value is [0%, 10%], the first sampling frequency is used, when the SOC value is (0%, 90%], the second sampling frequency is used, and when the SOC value is (90%, 100%], the third sampling frequency is used. Here, they are not listed one by one.

[0055] In some exemplary implementations, before determining the model parameters of the circuit equivalent model in step S100, the estimation method further includes:

[0056] Establish a circuit equivalent model of the battery pack. For example, see Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a circuit equivalent model of a power battery according to one embodiment of the present invention. Figure 2 It can be seen that the circuit equivalent model includes a second-order circuit equivalent model; the second-order circuit equivalent model includes a voltage source E and a series resistor R0, a first RC circuit RC1 and a second RC circuit RC2 connected in series with the voltage source in sequence; wherein the first RC circuit RC1 includes a resistor R1 and a capacitor C1 connected in parallel, and the second RC circuit RC2 includes a resistor R2 and a capacitor C2 connected in parallel.

[0057] Therefore, the SOC and SOH estimation method of the power battery provided by the present invention adopts a second-order circuit equivalent model to model the battery pack. The circuit equivalent model includes parameters such as voltage source, series resistance and RC circuit element, which can accurately describe the characteristics of the battery, provide sufficient accuracy and fast calculation time, and thus lay a solid foundation for realizing high-precision estimation of the SOC and SOH of the battery pack. Therefore, the present invention is particularly suitable for new energy vehicles.

[0058] For details, please refer to Figure 2 According to the equivalent circuit model and Kirchhoff's voltage law, at any time, the battery terminal voltage V b , the open circuit voltage V of the voltage source E ocvThere is a corresponding relationship between the charge and discharge current I, the resistance value of the series resistor R0, the first RC circuit voltage V1 and the second RC circuit voltage V2. Exemplarily, the corresponding relationship can be expressed by the following formula (1):

[0059] V b (k) = V ocv (z(k))-I(k)R0-V1(k)-V2(k) (1)

[0060] In formula (1), V b (k) is the battery terminal voltage measured by the battery pack at time k, z(k) is the state of charge (i.e., SOC) of the battery pack, V ocv (z(k)) is the open circuit voltage at time k, I(k) is the charge and discharge current of the battery pack measured at time k, R0 is the resistance value of the series resistor, V1(k) is the voltage of the first RC circuit at time k, and V2(k) is the voltage of the second RC circuit at time k.

[0061] It should be noted that, as can be understood by those skilled in the art, the SOC and SOH estimation method of the power battery provided by the present invention, the way in which the battery pack is modeled using a second-order circuit equivalent model is only an exemplary description of a preferred embodiment, and is not a limitation of the present invention. In other embodiments, the battery pack may be modeled using other methods other than the second-order circuit equivalent model, for example, the equivalent circuit model of the battery pack may also be, but is not limited to, a first-order circuit equivalent model.

[0062] In some exemplary embodiments, step S100 determines the model parameters of the circuit equivalent model of the battery pack, including:

[0063] S110: predicting an open circuit voltage state of the battery pack according to the predicted charge state of the battery pack, wherein the predicted charge state of the battery pack is calculated using a counting equation;

[0064] S120: acquiring the first RC circuit voltage and the second RC circuit voltage according to the current law, and converting the first RC circuit voltage and the second RC circuit voltage into a discrete time model;

[0065] S130: Obtaining a parameter state equation of the circuit equivalent model according to the charge prediction state, and the first RC circuit voltage and the second RC circuit voltage converted into a discrete time model;

[0066] S140: Obtaining a predicted open circuit voltage of the battery pack according to a parameter state equation of the circuit equivalent model and the open circuit voltage state;

[0067] S150: Minimize the error between the predicted open circuit voltage and the battery terminal voltage to obtain model parameters of the circuit equivalent model; wherein the model parameters include the resistance value of the series resistor, the resistance value of the resistor and the capacitance value of the capacitor of the first RC circuit, and the resistance value of the resistor and the capacitance value of the capacitor of the second RC circuit.

[0068] It can be seen that in the SOC and SOH estimation method of the power battery provided by the present invention, the model parameters of the circuit equivalent model of the battery pack are determined during the charging and discharging process of the power battery, so that the characteristics of the battery pack can be accurately described, thereby laying a solid foundation for realizing high-precision estimation of the SOC and SOH of the battery pack; further, the circuit equivalent model realizes parameter fitting in an offline manner, and the calculation cost is low, thereby laying a solid foundation for improving the estimation speed while maintaining the estimation accuracy. Moreover, the model parameters of the circuit equivalent model are determined based on the minimum error between the predicted open circuit voltage and the battery terminal voltage, which can further improve the accuracy of the circuit equivalent module.

[0069] In some exemplary embodiments, step S110 predicts the open circuit voltage state of the battery pack according to the predicted state of charge of the battery pack, including:

[0070] S111: Determine a polynomial function relationship between the state of charge and the open circuit voltage of the battery pack;

[0071] S112: Determine a correspondence between the state of charge of the battery pack at the current moment and the state of charge at the next moment according to the battery capacity, the battery efficiency, the charge and discharge current at the current moment, and the time difference between the current moment and the next moment.

[0072] Therefore, the open circuit voltage state of the battery pack can be predicted based on the current state of charge of the battery pack, the polynomial function relationship between the charge state of the battery pack and the open circuit voltage, and the corresponding relationship between the current state of charge of the battery pack and the state of charge at the next moment.

[0073] Exemplarily, the polynomial function relationship between the state of charge and the open circuit voltage of the battery pack in step S111 can be approximately represented by a fifth-order polynomial, specifically, as shown in the following formula (2):

[0074] V ocv (z(k))=p1z(k) 5 +p2z(k) 4 +p3z(k) 3 +p4z(k) 2 +p5z(k)+p6....(2)

[0075] In formula (2), Vocv (z(k)) is the open circuit voltage of the battery pack at time k, p1, p2, p3, p4, p5 and p6 are constants, and z(k) is the state of charge of the battery pack.

[0076] It is understandable that the polynomial function relationship between the charge state and the open circuit voltage of the battery pack using the fifth-order polynomial function of formula (2) is only an exemplary description of a preferred embodiment, and is not a limitation of the present invention. In other embodiments, other methods besides the fifth-order polynomial function may also be used, which may be but not limited to a third-order polynomial function, a seventh-order polynomial function or other function types.

[0077] Further, preferably, the corresponding relationship between the state of charge of the battery pack at the current moment and the state of charge at the next moment in step S112 can be expressed by the counting equation of the following formula (3):

[0078]

[0079] In formula (3), ΔT is the sampling time interval (i.e., the time interval between the current time k and the next time k+1), Q is the battery capacity of the battery pack, η is the battery efficiency at time k, and I(k) is the charge and discharge current at time k. Wherein, the value of η is ≤1. For example, when the power battery is a brand new battery, the value of η is 1. For more details on how to obtain the battery efficiency, please refer to the relevant technology known to those skilled in the art, which will not be elaborated in this article.

[0080] Exemplarily, step S120 acquires the first RC circuit voltage and the second RC circuit voltage according to the current law, and converts the first RC circuit voltage and the second RC circuit voltage into a discrete time model, specifically including:

[0081] For the first RC circuit, the voltage is derived from the current law and converted into a discrete time model as follows (4):

[0082]

[0083] In formula (4), V1(k+1) is the voltage of the first RC circuit at time k+1, V1(k) is the voltage of the first RC circuit at time k, ΔT is the sampling time interval (i.e., the time interval between the current time k and the next time k+1), R1 is the resistance value of the resistor R1 of the first RC circuit RC1, C1 is the capacitance value of the capacitor C1 of the first RC circuit RC1, and I(k) is the charge and discharge current at time k.

[0084] Similarly, for the first RC circuit, the voltage is derived from the current law and converted into a discrete time model, as shown in formula (5):

[0085]

[0086] In formula (5), V2(k+1) is the voltage of the second RC circuit at time k+1, V2(k) is the voltage of the second RC circuit at time k, ΔT is the sampling time interval (i.e., the time interval between the current time k and the next time k+1), R2 is the resistance value of the resistor R2 of the second RC circuit RC2, C2 is the capacitance value of the capacitor C2 of the first RC circuit RC2, and I(k) is the charge and discharge current at time k.

[0087] Correspondingly, step S130 obtains the parameter state equation of the circuit equivalent model according to the charge prediction state and the first RC circuit voltage and the second RC circuit voltage after conversion into a discrete time model, specifically including using the following formula (6) as the parameter state equation of the circuit equivalent model:

[0088] x(k+1)=Ax(k)+Bu(k)+w(k) (6)

[0089] Combining equation (6) with equation (3), equation (4) and equation (5), the parameter state matrix of the circuit equivalent model can be obtained as shown in equation (7):

[0090]

[0091] Furthermore, step S140 obtains the predicted open circuit voltage of the battery pack according to the parameter state equation of the circuit equivalent model and the open circuit voltage state, specifically including:

[0092] According to formula (7), the state of charge z(k+1) of the battery pack can be predicted, and then according to the state of charge and combined with formula (2), the open circuit voltage state V can be obtained. ocv (z(k+1)); then according to the open circuit voltage state V ocv (z(k+1)), the first RC circuit voltage V1(k+1) and the second RC circuit voltage V2(k+1) predicted according to formula (7), the resistance value of the series resistor R0 and the charge and discharge current combined with formula (1) can obtain the predicted open circuit voltage V(k+1) of the battery pack.

[0093] Correspondingly, step S150 minimizes the error between the predicted open circuit voltage and the battery terminal voltage to obtain the model parameters of the circuit equivalent model, specifically including:

[0094] By minimizing the error between the predicted open circuit voltage predicted by the circuit equivalent model and the battery terminal voltage (i.e., the actual voltage measured), the model parameters of the circuit equivalent model can be determined using a nonlinear optimization method. Exemplarily, the optimization method is shown in the following formula (8a).

[0095] minf(X) (8a)

[0096] because:

[0097] g i (x)=0,i=1,…,m (8b)

[0098] L i ≤X≤U i ,i=1,…,m (8c)

[0099] Where X is a vector of variables used in the optimization (such as the first RC circuit voltage, the second RC circuit voltage, and the state of charge of the battery pack), g i (x) represents the error constraint, L i and U i are the lower and upper boundaries respectively, and i represents the sampling time point.

[0100] In some exemplary embodiments, step S300 uses a double extended Kalman filter to estimate the battery state of the battery pack, specifically including:

[0101] S310: using a first extended Kalman filter to obtain an estimated voltage of the battery pack, and obtaining an estimated SOC value of the battery pack according to the estimated voltage;

[0102] S320: using a second extended Kalman filter to obtain an estimated internal resistance and an estimated capacity of the battery pack, and calculating an estimated SOH value of the battery pack according to the estimated internal resistance or the estimated capacity.

[0103] The SOC and SOH estimation method of the power battery provided by the present invention adopts a first extended Kalman filter to estimate the SOC estimation value of the battery pack, and adopts a second extended Kalman filter to estimate the estimated internal resistance and estimated capacity of the battery pack. The first extended Kalman filter can be used to estimate the fast-changing state, and the second extended Kalman filter can be used to estimate the slow-changing state, thereby solving the nonlinear problem of battery state change and significantly improving the accuracy of SOC and SOH estimation.

[0104] Furthermore, in some exemplary embodiments, before adopting the first extended Kalman filter to obtain the estimated voltage of the battery pack in step S310, the method further includes: establishing a state equation and an observation equation of the first extended Kalman filter according to the circuit equivalent model.

[0105] In some exemplary embodiments, before using the second extended Kalman filter in step S320 to obtain the estimated internal resistance and estimated capacity of the battery pack, the method further includes: establishing a state equation and an observation equation of the second extended Kalman filter based on the battery internal resistance and battery capacity of the battery pack.

[0106] For some of the exemplary embodiments, see Figure 3 , Figure 3 The following is a schematic diagram of the estimation process of a dual extended Kalman filter provided in one embodiment of the present invention. Figure 3 It can be seen that step S300 uses a double extended Kalman filter to estimate the battery state of the battery pack, including iteratively executing the following steps until the battery state converges, specifically including:

[0107] SA1: using a first extended Kalman filter to perform a first state prediction and a first covariance error prediction on the battery pack;

[0108] SA2: using a second extended Kalman filter to perform a second state prediction and a second covariance error prediction on the battery pack;

[0109] SA3: using a first extended Kalman filter to obtain a first Kalman gain, and updating the first state prediction and the first covariance error;

[0110] SA4: Use a second extended Kalman filter to obtain a second Kalman gain, and update the second state prediction and update the second covariance error.

[0111] Therefore, in the SOC and SOH estimation method of the power battery provided by the present invention, the first extended Kalman filter for fast-changing state estimation runs first, and the second extended Kalman filter for slow-changing state estimation runs later, and both perform state prediction and covariance prediction first, and then obtain the Kalman gain, update the state prediction and covariance prediction, so that parameters can be estimated by estimating the error, thereby making the predicted SOC estimation value more accurate.

[0112] Exemplarily, as mentioned above, X represents the variables used in the optimization in formula (8a) (characterizing the system state, such as the first RC circuit voltage, the second RC circuit voltage, and the state of charge of the battery pack), and θ represents the system parameters (such as the internal resistance and battery capacity of the battery pack). Assume that P, S, Q, R represent the error covariance of the state, the error covariance of the parameter, the process noise covariance, and the measurement noise covariance, respectively, and assume that K, A, C, y represent the Kalman gain, the Jacobian from the matrix A of the system, the Jacobian from the matrix C of the system, and the output of the system.

[0113] Specifically, for the first extended Kalman filter, the first state prediction can be performed by the following formula (9a), the first covariance error prediction can be performed by the following formula (9b), the first Kalman gain can be obtained by the following formula (9c), the first state prediction can be updated by the following formula (9d), and the first covariance error can be updated by the following formula (9e).

[0114]

[0115]

[0116] Specifically, for the second extended Kalman filter, the first state prediction can be performed by the following formula (10a), the first covariance error prediction can be performed by the following formula (10b), the first Kalman gain can be obtained by the following formula (10c), the first state prediction can be updated by the following formula (10d), and the first covariance error can be updated by the following formula (10e).

[0117]

[0118] More specifically, for the first extended Kalman filter, the dynamic formulas of its three parameters can be defined by equations (6) and (7); wherein, since no charge and discharge current flows in the initial state, the initial conditions of the first RC circuit RC1 and the second circuit RC2 are 0, and the polynomial circuit equivalent model is obtained by equation (1), and then the model parameters of the circuit equivalent model are obtained by a nonlinear method, thereby obtaining the predicted open circuit voltage of the power battery.

[0119] For the second extended Kalman filter, its dynamic formula can be defined by the following equations (11) and (12):

[0120] θ k+1 =θ k +q k (11)

[0121]

[0122] In formula (11), θ represents the performance parameter of the power battery, q krepresents the noise in the system; in formula (12), R0 and C p They are the internal resistance and battery capacity of the battery pack, respectively. It should be noted that, as can be understood by those skilled in the art, the present invention does not specifically limit the method for obtaining the initial value of the internal resistance of the battery pack and the initial value of the battery capacity and the specific value thereof. Exemplarily, in a specific embodiment, the initial value of the internal resistance of the battery pack can be obtained by a measurement method, and the initial value of the battery capacity can be the nominal battery capacity in the data table of the factory parameters of the power battery; the initial value of the internal resistance is 0.03mΩ, and the initial value of the battery capacity is 1.4Ah.

[0123] Further, in some exemplary embodiments, step S320 calculates the estimated SOH value of the battery pack according to the estimated internal resistance or the estimated capacity, and specifically includes:

[0124] Obtaining an estimated SOH value of the battery pack according to the estimated internal resistance and a calibrated internal resistance of the battery pack;

[0125] Alternatively, an estimated SOH value of the battery pack is obtained according to the estimated capacity and the calibrated capacity of the battery pack.

[0126] For example, in some exemplary embodiments, when determining the health state of the power battery, the estimated value of SOH is calculated using the capacity decay method, as shown in the following formula (13):

[0127]

[0128] In formula (13), C k is the estimated capacity of the battery pack at time k predicted by the double extended Kalman filter, and C0 is the calibrated capacity of the battery pack.

[0129] For example, in some other exemplary embodiments, the SOH estimation value may also be calculated based on the estimated internal resistance and the calibrated internal resistance of the battery pack, as shown in the following formula (14):

[0130]

[0131] In formula (14), R eol is the internal resistance of the battery pack at the end of the power battery life, R bat is the estimated internal resistance of the battery pack predicted by the double extended Kalman filter, R new It is the initial internal resistance of the battery pack when the power battery is brand new.

[0132] Further, as mentioned above, in step (3), the battery model is modeled by using formula (1) to obtain a polynomial battery model, and then the parameters of the model are estimated by nonlinearity to obtain the estimated voltage of the battery pack, as shown in the following formula (15), formula (1) and formula (16):

[0133]

[0134] because:

[0135] V b (k) = V ocv (z(k))-I(k)R0-V1(k)-V2(k) (1)

[0136] C1, C2, R1, R2>0 (16)

[0137] Where V b is the measured battery terminal voltage, is the estimated voltage obtained by estimating the equivalent circuit model.

[0138] As can be understood by those skilled in the art, the amount of battery resistance will increase as the battery is used. The longer the battery life, the internal resistance of the battery will increase. This is because more chemical reactions occur in the battery, resulting in deformation of materials, corrosion of conductors and electrodes in the battery, and a reduction in electrolytes that affect the increase in internal resistance. The increase in battery internal resistance will reduce the amount of battery current entering and leaving the battery. This will cause the voltage in the load to drop and the battery temperature to rise. Until the end of the battery life. In other words, as the battery ages, these parameters will change and may affect the estimation of SOC and SOH. It can be seen that the SOC and SOH estimation method of the power battery provided by the present invention, through a dual extended Kalman filter, iteratively predicts the estimated internal resistance and estimated capacity of the battery pack, so that the predicted SOC estimate value is more accurate.

[0139] Another embodiment of the present invention provides a battery management system. For example, see Figure 4 , Figure 4 FIG. 1 is a block diagram of a battery management system provided in one embodiment of the present invention. Figure 4As shown, the battery management system provided in this embodiment includes a processor 110 and a memory 120, and a computer program is stored in the memory 120. When the computer program is executed by the processor 110, the SOC and SOH estimation method of the power battery provided in any of the above embodiments is implemented. Since the battery management system provided in this embodiment and the SOC and SOH estimation method of the power battery provided in the present invention belong to the same inventive concept, the battery management system provided in this embodiment at least has all the advantages of the SOC and SOH estimation method of the power battery provided in the present invention. For details, please refer to the above description of the beneficial effects of the SOC and SOH estimation method of the power battery, which will not be repeated here.

[0140] For example, Figure 4 As shown, the battery management system may also include a communication interface 130 and a communication bus 140, wherein the processor 110, the communication interface 130, and the memory 120 communicate with each other through the communication bus 140. The communication bus 140 includes, but is not limited to, a CAN bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface 130 is used for communication between the above-mentioned battery management system and other controllers (such as motor controllers, vehicle controllers, automatic driving domain controllers, etc., not shown in the figure). The communication bus 140 connects the above-mentioned battery management system and other controllers (such as motor controllers, vehicle controllers, etc., not shown in the figure) and other dispersed nodes into a closed-loop system, so that each controller can perform multiple working states (parking state, charging state, starting state, running state, vehicle forward and backward state, feedback braking state, mechanical braking state, general fault state, major fault state) Communication and data transmission, thereby realizing the control function of the vehicle.

[0141] The processor 110 referred to in the present invention may be a microcontroller unit (MCU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 110 is the control center of the battery management system, and uses various interfaces and lines to connect various parts of the entire battery management system.

[0142] The memory 120 may be used to store the computer program. The processor 110 implements various functions of the battery management system by running or executing the computer program stored in the memory 120 and calling the data stored in the memory 120 .

[0143] The memory 120 may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0144] Another embodiment of the present invention provides a readable storage medium, wherein a computer program is stored in the readable storage medium, and when the computer program is executed by a processor, the SOC and SOH estimation method of the power battery described above can be implemented. Since the readable storage medium provided by the present invention and the SOC and SOH estimation method of the power battery provided by the present invention belong to the same inventive concept, the readable storage medium provided by the present invention at least has all the advantages of the SOC and SOH estimation method of the power battery provided by the present invention. For details of the beneficial effects of the readable storage medium provided by the present invention, please refer to the above description of the beneficial effects of the SOC and SOH estimation method of the power battery provided by the present invention, which will not be repeated here.

[0145] The readable storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared or semiconductor, or any combination of the above. More specific examples (non-exhaustive examples) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer hard disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this article, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0146] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0147] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate 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., via the Internet using an Internet service provider).

[0148] Compared with the prior art, the SOC and SOH estimation method, system and readable storage medium of a power battery provided by the present invention have the following advantages:

[0149] The present invention provides a method for estimating the SOC and SOH of a power battery. The model parameters of the circuit equivalent model of the battery pack are determined during the charging and discharging process of the power battery, so that the characteristics of the battery pack can be accurately described, thereby laying a solid foundation for realizing high-precision estimation of the SOC and SOH of the battery pack. Furthermore, the dual extended Kalman filter can estimate the fast-changing state (such as the SOC value of the battery pack) and the slow-changing state (such as the internal resistance and capacity of the battery pack) respectively, effectively solving the nonlinear problem of battery state change, thereby significantly improving the accuracy of SOC and SOH estimation. Furthermore, the dual extended Kalman filter is used to combine the Kalman filter with the extended state vector, so that the estimation speed can be improved while maintaining the estimation accuracy, thereby ensuring the reliability and safety of the power battery. Further, since the SOC and SOH estimation method of the power battery provided by the present invention can be applied to the SOC and SOH estimation of the power battery charging process as well as the power battery discharging process. During the charging process, accurate SOC estimation can help determine the charging progress and the remaining charging time, improve charging efficiency and user experience. During the discharge process (vehicle driving), accurate SOH estimation helps determine the health of the battery, detect potential faults and abnormal conditions in advance, and ensure the reliability and safety of the battery pack. Furthermore, the present invention does not impose too many restrictions on the type of the power battery, and has high calculation efficiency and strong applicability.

[0150] Since the battery management system and the readable storage medium provided by the present invention belong to the same inventive concept as the SOC and SOH estimation method for the power battery provided by the present invention, the battery management system and the readable storage medium provided by the present invention at least have all the advantages of the SOC and SOH estimation method for the power battery provided by the present invention. For details on the beneficial effects of the battery management system and the readable storage medium provided by the present invention, please refer to the above description of the beneficial effects of the SOC and SOH estimation method for the power battery provided by the present invention, and no further details will be given here.

[0151] It should be noted that the devices and methods disclosed in the embodiments of this article can also be implemented in other ways. The device implementation described above is only schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of this article. In this regard, each box in the flowchart or block diagram can represent a part of a module, program or code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical function, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented by a dedicated hardware-based system for performing a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0152] In addition, the functional modules in the various embodiments of this document may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0153] The above description is only a description of a preferred embodiment of a method, system and readable storage medium for estimating the SOC and SOH of a power battery provided by the present invention, and is not intended to limit the scope of the present invention. Any changes and modifications made by a person skilled in the art in the field of the present invention based on the above disclosure shall fall within the scope of protection of the present invention. Obviously, a person skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for estimating SOC and SOH of a power battery, characterized in that: In the process of charging and discharging the power battery, the following steps are iteratively performed: Determining model parameters of a circuit equivalent model of a battery pack of the power battery; Measuring the state parameters of the battery pack at the current moment, wherein the state parameters include the battery terminal voltage and the charge and discharge current; estimating the battery state of the battery pack using a double extended Kalman filter according to the model parameters and the state parameters; The SOC estimation value, SOH estimation value and estimation error of the battery pack are obtained when the estimation results converge.

2. The method for estimating SOC and SOH of a power battery according to claim 1, characterized in that: Before determining the model parameters of the circuit equivalent model, the estimation method further includes: A circuit equivalent model of the battery pack is established; the circuit equivalent model includes a second-order circuit equivalent model; the second-order circuit equivalent model includes a voltage source and a series resistor, a first RC circuit and a second RC circuit connected in series with the voltage source in sequence; wherein the first RC circuit and the second RC circuit both include a resistor and a capacitor connected in parallel.

3. The method for estimating SOC and SOH of a power battery according to claim 2, characterized in that: The determining of the model parameters of the circuit equivalent model of the battery pack includes: Predicting an open circuit voltage state of the battery pack according to a predicted state of charge of the battery pack, wherein the predicted state of charge of the battery pack is calculated using a counting equation; Acquire the first RC circuit voltage and the second RC circuit voltage according to the current law, and convert the first RC circuit voltage and the second RC circuit voltage into a discrete time model; Obtaining a parameter state equation of the circuit equivalent model according to the charge prediction state and the first RC circuit voltage and the second RC circuit voltage converted into a discrete time model; Obtaining a predicted open circuit voltage of the battery pack according to a parameter state equation of the circuit equivalent model and the open circuit voltage state; Minimize the error between the predicted open circuit voltage and the battery terminal voltage to obtain model parameters of the circuit equivalent model; wherein the model parameters include the resistance value of the series resistor, the resistance value of the resistor and the capacitance value of the capacitor of the first RC circuit, and the resistance value of the resistor and the capacitance value of the capacitor of the second RC circuit.

4. The method for estimating SOC and SOH of a power battery according to claim 1, characterized in that: The method of estimating the battery state of the battery pack by using a double extended Kalman filter comprises: Using a first extended Kalman filter to obtain an estimated voltage of the battery pack, and obtaining an estimated SOC value of the battery pack according to the estimated voltage; A second extended Kalman filter is used to obtain an estimated internal resistance and an estimated capacity of the battery pack, and an estimated SOH value of the battery pack is calculated based on the estimated internal resistance or the estimated capacity.

5. The method for estimating SOC and SOH of a power battery according to claim 4, characterized in that: Before using the first extended Kalman filter to obtain the estimated voltage of the battery pack, the method further includes: establishing a state equation and an observation equation of the first extended Kalman filter according to the circuit equivalent model.

6. The method for estimating SOC and SOH of a power battery according to claim 4, characterized in that: Before using the second extended Kalman filter to obtain the estimated internal resistance and estimated capacity of the battery pack, the method further includes: establishing a state equation and an observation equation of the second extended Kalman filter according to the battery internal resistance and battery capacity of the battery pack.

7. The method for estimating SOC and SOH of a power battery according to claim 4, characterized in that: The method of estimating the battery state of the battery pack by using a double extended Kalman filter comprises iteratively executing the following steps until the battery state converges: Performing a first state prediction and a first covariance error prediction on the battery pack using a first extended Kalman filter; Performing a second state prediction and a second covariance error prediction on the battery pack using a second extended Kalman filter; Using a first extended Kalman filter to obtain a first Kalman gain, and updating the first state prediction and updating the first covariance error; A second extended Kalman filter is used to obtain a second Kalman gain, and the second state prediction and the second covariance error are updated.

8. The method for estimating SOC and SOH of a power battery according to claim 4, characterized in that: The calculating the estimated SOH value of the battery pack according to the estimated internal resistance or the estimated capacity includes: Obtaining an estimated SOH value of the battery pack according to the estimated internal resistance and a calibrated internal resistance of the battery pack; Alternatively, an estimated SOH value of the battery pack is obtained according to the estimated capacity and the calibrated capacity of the battery pack.

9. A battery management system, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method for estimating the SOC and SOH of the power battery according to any one of claims 1 to 8 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for estimating the SOC and SOH of the power battery according to any one of claims 1 to 8 is implemented.

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