Power battery state estimation method applicable to full power range in multiple working conditions

Abstract

The present invention specifically relates to a power battery state estimation method applicable to a full charge interval under multiple operating conditions, including: establishing an equivalent circuit model of a power battery, and performing parameter identification on the equivalent circuit model; collecting open circuit voltage characteristic data of the power battery and fitting the SOC‑OCV curve of the power battery; generating corresponding terminal voltage prediction values ​​and SOC estimation values ​​based on the equivalent circuit model, dynamic characteristic data and SOC‑OCV curve in combination with various filtering algorithms; assigning corresponding weighted values ​​to the SOC estimation values ​​of various filtering algorithms based on the voltage residual between the terminal voltage prediction value and the corresponding measured value in combination with the OWA operator; performing weighted calculation based on the SOC estimation values ​​and corresponding weighted values ​​of various filtering algorithms, and obtaining a fused SOC estimation value of the power battery as its state estimation result. The present invention can effectively integrate the SOC estimation results of multiple filtering algorithms and realize the complementarity of multiple filtering algorithms, thereby realizing the global optimal SOC estimation of the full charge interval under multiple operating conditions of the power battery.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle power battery management, and in particular to a power battery state estimation method applicable to full power ranges under multiple operating conditions. Background Art

[0002] As environmental pollution and energy crisis become increasingly serious, electric vehicles, as green and economical means of transportation, have gradually won the market and are widely used. As an important energy storage component of electric vehicles, the performance of power batteries is closely related to the normal operation and good work of the vehicle. Unlike fuel vehicles, the power batteries in electric vehicles need to undergo continuous charging and discharging processes during use. Different charging and discharging strategies have a certain impact on the performance and life of the battery.

[0003] The state of charge (SOC) of a power battery is used to indicate the ratio of the remaining power in the power battery to its rated capacity. It can accurately characterize the state of the power battery and can be used to optimize the performance of the power battery. For example, the Chinese patent with publication number CN111098755A discloses a method for estimating the SOC of a power battery for an electric vehicle, which includes: measuring the current and voltage of the power battery by an intermittent constant current discharge method, determining the functional relationship of the SOC-OCV of the power battery by a polynomial fitting method; establishing a second-order RC equivalent circuit model of the power battery and deriving its identification model; constructing the process of an improved chaotic gravitational search algorithm to complete the identification; establishing a state space model of the battery for a nonlinear system of a lithium-ion battery, constructing the process of a Gauss-Hermitian filtering algorithm, and forming a joint filtering algorithm with the improved chaotic gravitational search algorithm.

[0004] The power battery SOC estimation method in the above-mentioned existing scheme improves the real-time performance of SOC estimation by jointly estimating model parameter identification and SOC estimation. The applicant found that the existing power battery SOC estimation method generally relies on a single filtering algorithm to complete SOC estimation. However, the vehicle is always in a complex driving environment with many uncertain factors. It is difficult to effectively resist external interference by relying on a single algorithm, and it is difficult to achieve the most accurate estimation of the power battery state in the entire charging and discharging interval, that is, it is impossible to achieve the optimal SOC estimation throughout the entire charging and discharging interval. For this reason, the applicant thought of using a variety of filtering algorithms to achieve global SOC estimation of the power battery, so that the filtering algorithm can be used to achieve the optimal SOC estimation of the power battery throughout the charging and discharging interval. However, how to design a framework that can effectively integrate the SOC estimation results of multiple filtering algorithms and realize the complementarity of multiple filtering algorithms is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of the deficiencies of the above-mentioned prior art, the technical problem to be solved by the present invention is: how to provide a power battery state estimation method suitable for a full charge range under multiple operating conditions, so as to effectively integrate the SOC estimation results of multiple filtering algorithms and realize the complementarity of multiple filtering algorithms, thereby realizing the global optimization of the SOC estimation of the power battery in the full charge range under multiple operating conditions, thereby ensuring the accuracy of the SOC estimation of the power battery.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A power battery state estimation method applicable to full power range of multiple working conditions includes the following steps:

[0008] S1: Establish an equivalent circuit model of the power battery and perform parameter identification on the equivalent circuit model;

[0009] S2: In the open circuit voltage experiment, the open circuit voltage characteristic data of the power battery is collected at intervals of the set sampling time; then the SOC-OCV curve of the power battery is fitted based on the open circuit voltage characteristic data;

[0010] S3: In the dynamic stress test, the dynamic characteristic data of the power battery is collected at intervals of the set sampling time;

[0011] S4: Based on the equivalent circuit model, dynamic characteristic data and SOC-OCV curve, various filtering algorithms are combined to generate the corresponding terminal voltage prediction value and SOC estimation value;

[0012] S5: Based on the voltage residual between the terminal voltage prediction value and the corresponding measured value, the OWA operator is combined to assign corresponding weighted values ​​to the SOC estimation values ​​of various filtering algorithms;

[0013] S6: Perform weighted calculation based on the SOC estimation values ​​of various filtering algorithms and the corresponding weighted values ​​to obtain a fused SOC estimation value of the power battery as its state estimation result.

[0014] Preferably, in step S1, the equivalent circuit model of the power battery includes a Rint model, a Thevenin model and a DP model.

[0015] Preferably, in step S1, the equivalent circuit model of the power battery is a Thevenin model, and its circuit equation is as follows:

[0016]

[0017]

[0018] Where: U t Indicates the terminal voltage of the power battery; U DIndicates the voltage drop of the RC parallel link of the power battery; U oc Represents the ideal voltage source of the power battery; R i Indicates the ohmic internal resistance of the power battery; R D Indicates the polarization resistance of the power battery; C D Represents the polarization capacitance of the power battery; i L Indicates the current flowing through the power battery.

[0019] Preferably, in step S1, in the hybrid power pulse test of the power battery, the terminal voltage, charge and discharge current and surface temperature data of the power battery are collected at intervals of a set sampling time, and then parameter identification of the equivalent circuit model is performed based on the terminal voltage, charge and discharge current and surface temperature data.

[0020] Preferably, in step S2, the open circuit voltage characteristic data includes the charge and discharge current and the open circuit voltage, i.e., OCV;

[0021] The corresponding charging OCV and discharging OCV and the corresponding SOC value are recorded respectively; then the charging OCV and the discharging OCV are averaged to obtain the corresponding OCV value; finally, based on multiple OCV values ​​and the corresponding SOC values, the corresponding SOC-OCV curve is obtained by linear interpolation.

[0022] Preferably, in step S3, the dynamic characteristic data includes terminal voltage, charge and discharge current and surface temperature data.

[0023] Preferably, in step S4, the filtering algorithm includes an AEKF filtering algorithm, a CDKF filtering algorithm and a HIF filtering algorithm.

[0024] Preferably, in step S5, the voltage residual is calculated by the following formula:

[0025]

[0026] Where: r k,i represents the voltage residual of the i-th filtering algorithm at time k; U k,i represents the measured value of the terminal voltage corresponding to the i-th filtering algorithm at time k; represents the terminal voltage prediction value of the i-th filtering algorithm at time k;

[0027] The voltage residual r k,i Substitute it into the OWA operator and calculate the weighted value using the following formula:

[0028]

[0029] Where: k,i represents the weighted value of the i-th filtering algorithm at time k; N represents the number of filtering algorithms; r k,iRepresents the voltage residual of the i-th filtering algorithm at time k.

[0030] Preferably, in step S5, the voltage residual is calculated by the following formula:

[0031]

[0032] Where: r k,i represents the voltage residual of the i-th filtering algorithm at time k; U k,i represents the measured value of the terminal voltage corresponding to the i-th filtering algorithm at time k; represents the terminal voltage prediction value of the i-th filtering algorithm at time k;

[0033] The voltage residual r k,i The true covariance is calculated by combining the following formula:

[0034]

[0035] Where: H k,i represents the true covariance of the i-th filtering algorithm at time k; r k,i represents the voltage residual of the i-th filtering algorithm at time k; N represents the number of filtering algorithms; the superscript T represents transposition;

[0036] The true covariance H k,i Substitute it into the OWA operator and calculate the weighted value using the following formula:

[0037]

[0038] Where: k,i represents the weighted value of the i-th filtering algorithm at time k; N represents the number of filtering algorithms; H k,i Represents the true covariance of the i-th filtering algorithm at time k.

[0039] Preferably, in step S6, the fused SOC estimation value of the power battery is calculated by the following formula:

[0040]

[0041] Where: Z F,k Indicates the fusion SOC value of the power battery pack; Z k,i represents the SOC estimation value of the i-th filtering algorithm at time k; ω k,i Represents the weighted value of the i-th filtering algorithm at time k.

[0042] The power battery state estimation method applicable to the full power interval of multiple working conditions in the present invention has the following beneficial effects:

[0043] The present invention realizes SOC estimation of the power battery through an equivalent circuit model, dynamic characteristic data and SOC-OCV curve in combination with a variety of filtering algorithms, and can then use the filtering algorithm to achieve the most accurate estimation of the power battery state in the entire charge and discharge interval, that is, it can achieve global optimization of SOC estimation in the full power interval of multiple working conditions of the power battery, thereby ensuring the accuracy of the SOC estimation of the power battery.

[0044] At the same time, based on the voltage residual between the terminal voltage prediction value and the corresponding measured value, the present invention combines the OWA operator to assign corresponding weighted values ​​to the SOC estimation values ​​of various filtering algorithms, so that the SOC estimation results of multiple filtering algorithms can be effectively fused and the complementarity of multiple filtering algorithms can be achieved through weight allocation and weighted calculation. Compared with a single filtering algorithm, a smoother and more accurate SOC estimation result can be obtained. That is, a complementary SOC multi-algorithm fusion prediction framework is established to achieve the effective fusion of multi-algorithm SOC estimation values, thereby improving the accuracy of power battery pack state estimation.

[0045] In addition, the present invention collects dynamic characteristic data of the power battery under dynamic stress test conditions, so that it can simulate the operating condition changes of the power battery under the actual driving state of the electric vehicle, and can further improve the accuracy of the power battery SOC estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to make the purpose, technical solution and advantages of the invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0047] Figure 1 The logical block diagram of the power battery state estimation method applicable to the full power range of multiple working conditions;

[0048] Figure 2 A flow chart of a power battery state estimation method applicable to full power ranges under multiple operating conditions;

[0049] Figure 3 (a) is a schematic diagram showing the comparison between the estimated SOC value and the true SOC value before and after the fusion of each filtering algorithm;

[0050] Figure 3 (b) and (c) are Figure 3 (a) Schematic diagram of two different locations.

[0051] Figure 4 The SOC estimation error trend before and after the fusion of each algorithm under DST condition;

[0052] Figure 5 The analysis results of SOC estimation values ​​before and after fusion of each algorithm under DST conditions. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages in the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but only represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0054] It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In the description of the present invention, it should be noted that the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invention product is usually placed when used, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance. In addition, the terms "horizontal", "vertical", etc. do not mean that the components are absolutely horizontal or suspended, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted. In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] The following is a further detailed description through specific implementation methods:

[0056] Embodiment 1:

[0057] This embodiment discloses a power battery state estimation method applicable to full power ranges under multiple operating conditions.

[0058] like Figure 1 and Figure 2 As shown, the power battery state estimation method applicable to the full power range of multiple working conditions includes the following steps:

[0059] S1: Establish an equivalent circuit model of the power battery and perform parameter identification on the equivalent circuit model;

[0060] S2: In the open circuit voltage experiment, the open circuit voltage characteristic data of the power battery is collected at intervals of the set sampling time; then the SOC-OCV curve of the power battery is fitted based on the open circuit voltage characteristic data;

[0061] In this embodiment, the open circuit voltage characteristic data includes the charge and discharge current and the open circuit voltage, namely, OCV;

[0062] The corresponding charging OCV and discharging OCV and the corresponding SOC value are recorded respectively; then the charging OCV and the discharging OCV are averaged to obtain the corresponding OCV value; finally, based on multiple OCV values ​​and the corresponding SOC values, the corresponding SOC-OCV curve is obtained by linear interpolation.

[0063] In this embodiment, OCV refers to open circuit voltage.

[0064] S3: In the dynamic stress test, the dynamic characteristic data of the power battery is collected at intervals of the set sampling time;

[0065] In this embodiment, the dynamic stress test includes a dynamic stress test (DST) condition, a city cycle (UDDS) condition and a new European driving cycle (NEDC) condition. The dynamic characteristic data includes terminal voltage, charge and discharge current and surface temperature data.

[0066] S4: Based on the equivalent circuit model, dynamic characteristic data and SOC-OCV curve, various filtering algorithms are combined to generate the corresponding terminal voltage prediction value and SOC estimation value;

[0067] S5: Based on the voltage residual between the terminal voltage prediction value and the corresponding measured value, the OWA operator is combined to assign corresponding weighted values ​​to the SOC estimation values ​​of various filtering algorithms;

[0068] In this embodiment, the OWA operator refers to an ordered weighted average operator, and its framework is:

[0069]

[0070] Where: k,i represents the weighted value of the i-th filtering algorithm at time k; N represents the number of filtering algorithms; x k,i Represents the eigenvalue of the i-th filtering algorithm at time k.

[0071] S6: Perform weighted calculation based on the SOC estimation values ​​of various filtering algorithms and the corresponding weighted values ​​to obtain a fused SOC estimation value of the power battery as its state estimation result.

[0072] The present invention realizes SOC estimation of the power battery through an equivalent circuit model, dynamic characteristic data and SOC-OCV curve in combination with a variety of filtering algorithms, and can then use the filtering algorithm to achieve the most accurate estimation of the power battery state in the entire charge and discharge interval, that is, it can achieve global optimization of SOC estimation in the full power interval of multiple working conditions of the power battery, thereby ensuring the accuracy of the SOC estimation of the power battery.

[0073] At the same time, based on the voltage residual between the terminal voltage prediction value and the corresponding measured value, the present invention combines the OWA operator to assign corresponding weighted values ​​to the SOC estimation values ​​of various filtering algorithms, so that the SOC estimation results of multiple filtering algorithms can be effectively fused and the complementarity of multiple filtering algorithms can be achieved through weight allocation and weighted calculation. Compared with a single filtering algorithm, a smoother and more accurate SOC estimation result can be obtained. That is, a complementary SOC multi-algorithm fusion prediction framework is established to achieve the effective fusion of multi-algorithm SOC estimation values, thereby improving the accuracy of power battery pack state estimation.

[0074] In addition, the present invention collects dynamic characteristic data of the power battery under dynamic stress test conditions, so that it can simulate the operating condition changes of the power battery under the actual driving state of the electric vehicle, and can further improve the accuracy of the power battery SOC estimation.

[0075] During the specific implementation, the equivalent circuit models of the power battery include the Rint model, the Thevenin model and the DP model.

[0076] This embodiment uses the Thevenin model, and its circuit equation is as follows:

[0077]

[0078]

[0079] Where: U t Indicates the terminal voltage of the power battery; U D Indicates the voltage drop of the RC parallel link of the power battery; U oc Represents the ideal voltage source of the power battery; R i Indicates the ohmic internal resistance of the power battery; R D Indicates the polarization resistance of the power battery; C D Represents the polarization capacitance of the power battery; i L Indicates the current flowing through the power battery.

[0080] The present invention can effectively combine various filtering algorithms to generate terminal voltage prediction values ​​and SOC estimation values ​​through the Thevenin model mentioned above, thereby assisting in realizing SOC estimation of the power battery.

[0081] During the specific implementation process, in the hybrid power pulse test of the power battery, the terminal voltage, charge and discharge current and surface temperature data of the power battery are collected at intervals of the set sampling time, and then the parameters of the equivalent circuit model are identified based on the terminal voltage, charge and discharge current and surface temperature data.

[0082] In this embodiment, the parameter identification method includes a recursive least squares algorithm, a Kalman filter, and a genetic algorithm.

[0083] In the specific implementation process, the filtering algorithms include AEKF filtering (Adaptive Extended Kalman Filtering) algorithm, CDKF filtering (Central Difference Kalman Filtering) algorithm and HIF filtering (H∞ filtering) algorithm. It is an existing mature method to predict and estimate SOC through AEKF filtering algorithm, CDKF filtering algorithm and HIF filtering algorithm, and the specific process will not be repeated here.

[0084] 1) AEKF filtering algorithm process:

[0085]

[0086]

[0087] 2)CDKF filtering algorithm process:

[0088]

[0089]

[0090] 3) HIF filtering algorithm flow:

[0091]

[0092] In the specific implementation process, the voltage residual is calculated by the following formula:

[0093]

[0094] Where: r k,i represents the voltage residual of the i-th filtering algorithm at time k; U k,i represents the measured value of the terminal voltage corresponding to the i-th filtering algorithm at time k; represents the terminal voltage prediction value of the i-th filtering algorithm at time k;

[0095] The voltage residual r k,i Substitute it into the OWA operator and calculate the weighted value using the following formula:

[0096]

[0097] Where: k,i represents the weighted value of the i-th filtering algorithm at time k; N represents the number of filtering algorithms; r k,i Represents the voltage residual of the i-th filtering algorithm at time k.

[0098] The fusion SOC estimation value of the power battery is calculated by the following formula:

[0099]

[0100] Where: Z F,k Indicates the fusion SOC value of the power battery pack; Z k,i represents the SOC estimation value of the i-th filtering algorithm at time k; ω k,i Represents the weighted value of the i-th filtering algorithm at time k.

[0101] The present invention is based on the voltage residual between the terminal voltage prediction value and the corresponding measured value, and combines the OWA operator to assign corresponding weighted values ​​to the SOC estimation values ​​of various filtering algorithms, so that the SOC estimation results of multiple filtering algorithms can be effectively fused and the complementarity of multiple filtering algorithms can be achieved through weight allocation and weighted calculation. Compared with a single filtering algorithm, a smoother and more accurate SOC estimation result can be obtained, that is, a complementary SOC multi-algorithm fusion prediction framework is established to achieve the effective fusion of multi-algorithm SOC estimation values, thereby further improving the accuracy of power battery pack state estimation.

[0102] Embodiment 2:

[0103] This embodiment discloses another method for calculating weighted values ​​based on the first embodiment.

[0104] In this embodiment, the voltage residual is calculated by the following formula:

[0105]

[0106] Where: r k,i represents the voltage residual of the i-th filtering algorithm at time k; U k,i represents the measured value of the terminal voltage corresponding to the i-th filtering algorithm at time k; represents the terminal voltage prediction value of the i-th filtering algorithm at time k;

[0107] The voltage residual r k,i The true covariance is calculated by combining the following formula:

[0108]

[0109] Where: H k,i represents the true covariance of the i-th filtering algorithm at time k; r k,i represents the voltage residual of the i-th filtering algorithm at time k; N represents the number of filtering algorithms; the superscript T represents transposition;

[0110] The true covariance H k,i Substitute it into the OWA operator and calculate the weighted value using the following formula:

[0111]

[0112] Where: k,i represents the weighted value of the i-th filtering algorithm at time k; N represents the number of filtering algorithms; H k,i Represents the true covariance of the i-th filtering algorithm at time k.

[0113] The applicant found in actual research that in addition to using the voltage residual as an indicator for weighted value allocation, the weighted value allocation can also be completed through other characteristic values, such as the true covariance. The present invention calculates the true covariance based on the voltage residual between the terminal voltage prediction value and the corresponding measured value, and then allocates corresponding weighted values ​​to the SOC estimation values ​​of various filtering algorithms through the true covariance combined with the OWA operator, so that the SOC estimation results of multiple filtering algorithms can be effectively fused and the complementarity of multiple filtering algorithms can be achieved through weight allocation and weighted calculation. Compared with a single filtering algorithm, a smoother and more accurate SOC estimation result can be obtained, that is, a complementary SOC multi-algorithm fusion prediction framework is established to achieve the effective fusion of multi-algorithm SOC estimation values, thereby improving the accuracy of power battery pack state estimation.

[0114] In this embodiment, the voltage residual is more focused on the deviation at the current moment, and the true covariance is a feature value obtained from a window, which is more focused on a range. In comparison, the weight allocation and fusion effect based on the voltage residual is better.

[0115] In order to better illustrate the advantages of the power battery state estimation method in the present invention, the applicant conducted corresponding experiments, in which:

[0116] The power battery pack uses NMC lithium battery pack.

[0117] Three filtering algorithms are selected: AEKF filtering algorithm, CDKF filtering algorithm and HIF filtering algorithm.

[0118] Figure 3 (a) is a schematic diagram showing the comparison between the estimated SOC value and the true SOC value before and after the fusion of each filtering algorithm. Figure 3(b) and (c) show that the power battery state estimation method proposed in the present invention (weight allocation and fusion based on voltage residual and true covariance) has a smoother estimation result than each single filtering algorithm and has better followability in the whole process of estimating SOC. Among them, the method of weight allocation and fusion based on voltage residual has the best followability in the whole process of estimating SOC.

[0119] Figure 4 is the trend of SOC estimation error before and after fusion of each algorithm under DST condition. Figure 4 (a) is the SOC estimation error of the AEKF filtering algorithm; Figure 4 (b) is the SOC estimation error of the CDKF filtering algorithm; Figure 4 (c) is the SOC estimation error of the HIF filtering algorithm; Figure 4 (d) is the SOC estimation error after weight allocation and fusion based on the true covariance; Figure 4 (e) is the SOC estimation error after weight allocation and fusion based on voltage residual.

[0120] Combination Figure 5 (a) and (b) show that the average errors of the weight allocation and fusion based on the true covariance and voltage residual of the present invention are 0.00012 and 0.00004 respectively, of which 0.00012 is lower than the results of the two algorithms, and 0.00004 is lower than the results of the three algorithms. By comparing the estimated error after fusion with the errors of other branches, it can be found that under the DST working condition, the prediction result of the power battery multi-algorithm fusion SOC filtering algorithm based on the OWA operator of the present invention is more accurate and stable.

[0121] Compared with the three single algorithms, the SOC estimation error of the power battery multi-algorithm fusion SOC filtering algorithm based on the OWA operator of the present invention is smaller overall in the entire range, and can reliably and accurately predict the SOC of the power battery.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A power battery state estimation method applicable to full power intervals under multiple working conditions, characterized in that: The following steps are involved: S1: Establish an equivalent circuit model of the power battery and perform parameter identification on the equivalent circuit model; S2: In the open circuit voltage experiment, the open circuit voltage characteristic data of the power battery is collected at intervals of the set sampling time; then the SOC-OCV curve of the power battery is fitted based on the open circuit voltage characteristic data; In step S2, the open circuit voltage characteristic data includes the charge and discharge current and the open circuit voltage (OCV); The corresponding charging OCV and discharging OCV and the corresponding SOC value are recorded respectively; then the corresponding OCV value is obtained by taking the average value of the charging OCV and the discharging OCV; finally, based on multiple OCV values ​​and the corresponding SOC values, the corresponding SOC-OCV curve is obtained by linear interpolation; S3: In the dynamic stress test, the dynamic characteristic data of the power battery is collected at intervals of the set sampling time; S4: Based on the equivalent circuit model, dynamic characteristic data and SOC-OCV curve, various filtering algorithms are combined to generate the corresponding terminal voltage prediction value and SOC estimation value; S5: Based on the voltage residual between the terminal voltage prediction value and the corresponding measured value, the OWA operator is combined to assign corresponding weighted values ​​to the SOC estimation values ​​of various filtering algorithms; S6: Perform weighted calculation based on the SOC estimation values ​​of various filtering algorithms and the corresponding weighted values ​​to obtain a fused SOC estimation value of the power battery as its state estimation result.

2. The power battery state estimation method applicable to full power intervals of multiple operating conditions as claimed in claim 1, characterized in that: In step S1, the equivalent circuit model of the power battery includes a Rint model, a Thevenin model and a DP model.

3. The power battery state estimation method applicable to full power intervals of multiple operating conditions as claimed in claim 2, characterized in that: In step S1, the equivalent circuit model of the power battery is the Thevenin model, and its circuit equation is as follows: Where: U t Indicates the terminal voltage of the power battery; U D Indicates the voltage drop of the RC parallel link of the power battery; U oc Represents the ideal voltage source of the power battery; R i Indicates the ohmic internal resistance of the power battery; R D Indicates the polarization resistance of the power battery; C D Represents the polarization capacitance of the power battery; i L Indicates the current flowing through the power battery.

4. The power battery state estimation method applicable to full power intervals of multiple operating conditions as claimed in claim 1, characterized in that: In step S1, in the hybrid power pulse test of the power battery, the terminal voltage, charge and discharge current and surface temperature data of the power battery are collected at intervals of a set sampling time, and then the parameters of the equivalent circuit model are identified based on the terminal voltage, charge and discharge current and surface temperature data.

5. The power battery state estimation method applicable to full power intervals of multiple operating conditions as claimed in claim 1, characterized in that: In step S3, the dynamic characteristic data includes terminal voltage, charge and discharge current and surface temperature data.

6. The power battery state estimation method applicable to full power intervals of multiple operating conditions as claimed in claim 1, characterized in that: In step S4, the filtering algorithms include AEKF filtering algorithm, CDKF filtering algorithm and HIF filtering algorithm.

7. The power battery state estimation method applicable to full power intervals of multiple operating conditions as claimed in claim 1 is characterized in that: In step S5, the voltage residual is calculated by the following formula: Where: r k,i represents the voltage residual of the i-th filtering algorithm at time k; U k,i represents the measured value of the terminal voltage corresponding to the i-th filtering algorithm at time k; represents the terminal voltage prediction value of the i-th filtering algorithm at time k; The voltage residual r k,i Substitute it into the OWA operator and calculate the weighted value using the following formula: Where: k,i represents the weighted value of the i-th filtering algorithm at time k; N represents the number of filtering algorithms; r k,i Represents the voltage residual of the i-th filtering algorithm at time k.

8. The power battery state estimation method applicable to full charge intervals under multiple operating conditions as claimed in claim 1, characterized in that: In step S5, the voltage residual is calculated by the following formula: Where: r k,i represents the voltage residual of the i-th filtering algorithm at time k; U k,i represents the measured value of the terminal voltage corresponding to the i-th filtering algorithm at time k; represents the terminal voltage prediction value of the i-th filtering algorithm at time k; Through the voltage residual r k,i The true covariance is calculated by combining the following formula: Where: H k,i represents the true covariance of the i-th filtering algorithm at time k; r k,i represents the voltage residual of the i-th filtering algorithm at time k; N represents the number of filtering algorithms; the superscript T represents transposition; The true covariance H k,i Substitute it into the OWA operator and calculate the weighted value using the following formula: Where: k,i represents the weighted value of the i-th filtering algorithm at time k; N represents the number of filtering algorithms; H k,i Represents the true covariance of the i-th filtering algorithm at time k.

9. The power battery state estimation method applicable to full charge intervals of multiple operating conditions as claimed in claim 1, characterized in that: In step S6, the fusion SOC estimation value of the power battery is calculated by the following formula: Where: Z F,k Indicates the fusion SOC value of the power battery pack; Z k,i represents the SOC estimation value of the i-th filtering algorithm at time k; ω k,i Represents the weighted value of the i-th filtering algorithm at time k.

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