Battery state of charge (SOC) estimation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a battery state of charge (SOC) estimation method and device, electronic equipment and a storage medium, and relates to the technical field of batteries. The method comprises the following steps: acquiring operation parameters of a battery at a current moment in a charging and discharging stage; a Kalman filtering algorithm is adopted, SOC is estimated according to the operation parameters, and a first SOC value of the battery is obtained; an ampere-hour integral method is adopted, SOC is estimated according to the operation parameters, and a second SOC value of the battery is obtained; if it is determined that the first SOC value is larger than the first threshold value and smaller than the second threshold value, performing weighted fusion on the first SOC value and the second SOC value to obtain a target SOC value; according to the method, the target SOC value serves as the SOC estimation value of the battery at the current moment, when it is determined that the first SOC value is within the interval formed by the first threshold value and the second threshold value, the Kalman filtering algorithm and the ampere-hour integral method are combined to conduct SOC estimation to obtain the target SOC value, the advantages of the two SOC estimation methods can be fully utilized, and the SOC estimation precision is effectively improved.

Description

Technical Field

[0001] The present disclosure relates to the field of battery technology, and in particular to a method, device, electronic device and storage medium for estimating a battery state of charge (SOC). Background Art

[0002] The battery state of charge (SOC) represents the remaining capacity of the battery. SOC estimation is the top priority of the battery management system. Since the battery SOC has a highly nonlinear relationship with other parameters, such as voltage, current, temperature, etc., it is difficult to accurately estimate the SOC.

[0003] In recent years, relevant researchers have proposed many SOC estimation methods. Among the related technologies, the most widely used are the Ampere-hour Integration method and Kalman filtering algorithms. Among them, the Ampere-hour Integration method is an open-loop prediction method that cannot be corrected in real time, has cumulative errors, and is highly dependent on the initial value of SOC. During long-term estimation, the reliability and estimation accuracy will drop sharply. Kalman filtering algorithms are heavily dependent on the accuracy of the equivalent circuit model of the battery, and cannot take into account both the accuracy of SOC estimation and the computational complexity of the algorithm.

[0004] It can be seen that the current battery state of charge SOC estimation methods all have the problem of low SOC estimation accuracy. Summary of the invention

[0005] The embodiments of the present disclosure provide a method, device, electronic device and storage medium for estimating the state of charge (SOC) of a battery, which are used to solve the technical problem of low SOC estimation accuracy in the method for estimating the state of charge (SOC) of a battery in the prior art.

[0006] According to one aspect of an embodiment of the present disclosure, a method for estimating a battery state of charge (SOC) is provided, comprising: Obtain the operating parameters of the battery at the current moment in the charging and discharging stage; The Kalman filter algorithm is used to estimate the SOC from the operating parameters to obtain the first SOC value of the battery; the ampere-hour integration method is used to estimate the SOC from the operating parameters to obtain the second SOC value of the battery; If it is determined that the first SOC value is greater than the first threshold value and less than the second threshold value, weighted fusion is performed on the first SOC value and the second SOC value to obtain a target SOC value; The target SOC value is used as the estimated SOC value of the battery at the current moment; Among them, the first threshold value and the second threshold value are determined according to the distribution of errors at different charging and discharging moments of the battery in the test phase, and the error is the difference between the reference SOC value and the measured SOC value at the corresponding charging and discharging moment; the type of reference SOC value includes at least one of the first SOC value, the second SOC value and the target SOC value.

[0007] According to another aspect of an embodiment of the present disclosure, a device for estimating a battery state of charge (SOC) is provided, comprising: An operating parameter acquisition module is used to obtain the operating parameters of the battery at the current moment in the charging and discharging stage; An SOC value estimation module is used to estimate the SOC from the operating parameters using a Kalman filter algorithm to obtain a first SOC value of the battery; and to estimate the SOC from the operating parameters using an ampere-hour integration method to obtain a second SOC value of the battery; An SOC value processing module, configured to perform weighted fusion on the first SOC value and the second SOC value to obtain a target SOC value if it is determined that the first SOC value is greater than a first threshold value and less than a second threshold value; An SOC value determination module, used for taking the target SOC value as the SOC estimation value of the battery at the current moment; Among them, the first threshold value and the second threshold value are determined according to the distribution of errors at different charging and discharging moments of the battery in the test phase, and the error is the difference between the reference SOC value and the measured SOC value at the corresponding charging and discharging moment; the type of reference SOC value includes at least one of the first SOC value, the second SOC value and the target SOC value.

[0008] According to another aspect of an embodiment of the present disclosure, an electronic device is provided. The electronic device includes a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of the method provided in any of the above embodiments.

[0009] According to another aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method provided in any of the above embodiments are implemented.

[0010] According to one aspect of an embodiment of the present disclosure, a computer program product is provided, including a computer program, and the computer program is executed by a processor to perform the steps of the method provided in any of the above embodiments.

[0011] The technical solution provided by the embodiments of the present disclosure brings the following beneficial effects: The technical solution provided by the embodiment of the present disclosure pre-sets a first threshold and a second threshold. At the current moment, a Kalman filter algorithm is used to obtain a first SOC value of the battery, and an ampere-hour integration method is used to obtain a second SOC value of the battery. When it is determined that the first SOC value is within the interval formed by the first threshold and the second threshold, the Kalman filter algorithm and the ampere-hour integration method are used to jointly perform SOC estimation, and the first SOC value and the second SOC value are weightedly fused to obtain a target SOC value as the SOC estimation value at the current moment. The advantages of the two SOC estimation methods can be fully utilized, the accuracy and robustness of the SOC estimation are improved, and the SOC estimation precision is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for describing the embodiments of the present disclosure are briefly introduced below.

[0013] Figure 1 A flowchart of a method for estimating the state of charge (SOC) of a battery provided in an embodiment of the present disclosure; Figure 2 A flow chart of a method for determining an SOC value provided in an embodiment of the present disclosure; Figure 3 A schematic diagram of the structure of a second-order equivalent circuit model provided by an embodiment of the present disclosure; Figure 4 A schematic diagram of the structure of a battery state of charge (SOC) estimation device provided in an embodiment of the present disclosure; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0014] The embodiments of the present disclosure are described below in conjunction with the drawings in the present disclosure. It should be understood that the implementation methods described below in conjunction with the drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present disclosure and do not constitute a limitation on the technical solutions of the embodiments of the present disclosure.

[0015] It will be understood by those skilled in the art that, unless specifically stated, the singular forms "one", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the terms "including" and "comprising" used in the embodiments of the present disclosure refer to that the corresponding features can be implemented as the presented features, information, data, steps, operations, elements and / or components, but do not exclude the implementation as other features, information, data, steps, operations, elements, components and / or combinations thereof supported by the technical field. It should be understood that when we say that an element is "connected" or "coupled" to another element, the one element may be directly connected or coupled to the other element, or it may refer to that the one element and the other element establish a connection relationship through an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein indicates at least one of the items defined by the term, for example, "A and / or B" may be implemented as "A", or as "B", or as "A and B".

[0016] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0017] The following describes several exemplary embodiments to illustrate the technical solutions of the embodiments of the present disclosure and the technical effects produced by the technical solutions of the present disclosure. It should be noted that the following embodiments can refer to, draw on or combine with each other, and the same terms, similar features and similar implementation steps in different embodiments will not be described repeatedly.

[0018] The following is a description of the related technologies involved in this application: Battery energy storage systems have gradually become a hot topic in research and application under the background of global energy structure adjustment and enhanced environmental protection awareness. With the continuous advancement of technology and the reduction of costs, battery energy storage systems play a very important role in today's energy system.

[0019] The battery energy storage system is a technology that can convert electrical energy into chemical energy and store it. It can convert chemical energy back into electrical energy for use when needed, so it requires a specific system to manage it. The battery management system has become an indispensable part of the energy storage system, responsible for battery status detection, battery status analysis, battery information management, energy control management, charge and discharge control, fault detection and SOC estimation, etc. Among them, SOC estimation is the top priority of the battery management system.

[0020] The following describes several exemplary embodiments to illustrate the technical solutions of the embodiments of the present disclosure and the technical effects produced by the technical solutions of the present disclosure. It should be noted that the following embodiments can refer to, draw on or combine with each other, and the same terms, similar features and similar implementation steps in different embodiments will not be described repeatedly.

[0021] It can be understood that in the method for estimating the battery state of charge (SOC) provided by the embodiment of the present disclosure, any method step can be executed by the terminal and / or the server, and all the steps in the method can be executed independently by the terminal or the server, or jointly by the terminal and the server.

[0022] The server may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services. The terminal may be a smart phone, tablet computer, laptop computer, desktop computer, intelligent voice interaction device (such as smart speaker), wearable electronic device (such as smart watch), vehicle-mounted terminal, smart home appliance (such as smart TV), AR / VR device, etc., but is not limited thereto.

[0023] The following describes the embodiments of the present disclosure using a server as an execution subject; however, this does not constitute a limitation to the embodiments of the present disclosure.

[0024] Figure 1 A flow chart of a method for estimating the state of charge (SOC) of a battery provided in an embodiment of the present disclosure is shown in FIG. Figure 1 As shown, the technical solution provided by the embodiment of the present disclosure includes the following steps: Step S101, obtaining the current operating parameters of the battery in the charging and discharging stage; Specifically, taking any battery as an example, the battery SOC estimation method provided by the embodiment of the present disclosure is described in detail. It can be understood that the specific type of the battery in the embodiment of the present disclosure can be a lithium battery, a lithium iron phosphate battery, etc., which can be determined according to actual needs.

[0025] The current moment may be any moment in the charge and discharge stage of the battery. In order to obtain the SOC estimation value of the battery at the current moment, in step S101, the operating parameters of the battery at the current moment in the charge and discharge stage are obtained.

[0026] The operating parameters refer to the parameters that describe the various physical and electrical characteristics of the battery under a specific working state, and the performance, health and state of the battery can be monitored through the various parameters. In the embodiment of the present disclosure, the Kalman filter algorithm and the ampere-hour integration method are used to estimate the SOC, and the corresponding operating parameters refer to the relevant parameters used for SOC estimation, including but not limited to voltage, current, temperature, initial SOC, battery rated capacity, time step, etc.

[0027] Step S102, using a Kalman filter algorithm to estimate the SOC from the operating parameters to obtain a first SOC value of the battery; using an ampere-hour integration method to estimate the SOC from the operating parameters to obtain a second SOC value of the battery; Specifically, after obtaining the operating parameters of the battery at the current moment, in step S102, the Kalman filter algorithm is used to estimate the SOC of the battery at the current moment from the operating parameters, and the SOC value obtained by the Kalman filter algorithm is recorded as the first SOC value of the battery. The ampere-hour integration method is used to estimate the SOC of the battery at the current moment from the operating parameters, and the SOC value obtained by the ampere-hour integration method is recorded as the second SOC value of the battery.

[0028] It can be understood that the specific types of the Kalman filter algorithm and the ampere-hour integration method adopted in the embodiments of the present disclosure can be standard Kalman filter algorithm and ampere-hour integration method, or variants of the corresponding methods, such as an extended Kalman filter algorithm or an improved ampere-hour integration method, etc., which can be determined according to actual needs.

[0029] When the Kalman filter algorithm and the ampere-hour integration method are used to estimate the SOC value, the types of operating parameters required are not exactly the same. The specific types and quantities of the operating parameters can be determined according to actual needs.

[0030] Step S103: If it is determined that the first SOC value is greater than the first threshold value and less than the second threshold value, weighted fusion is performed on the first SOC value and the second SOC value to obtain a target SOC value.

[0031] Among them, the first threshold value and the second threshold value are determined according to the distribution of errors at different charging and discharging moments of the battery in the test phase, and the error is the difference between the reference SOC value and the measured SOC value at the corresponding charging and discharging moment; the type of reference SOC value includes at least one of the first SOC value, the second SOC value and the target SOC value.

[0032] Specifically, the Kalman filter algorithm is an effective recursive filter that estimates the state of a linear dynamic system based on an assumed battery equivalent model. Considering the characteristics of the Kalman filter algorithm, at high SOC values, the Kalman filter usually converges quickly, the battery behavior is close to linear, and the SOC estimation error is relatively small. As the battery discharges, at medium SOC values, the SOC estimation error is relatively stable at a low level. When the battery is close to a fully discharged state, that is, a low SOC value, the SOC estimation error may increase due to the enhanced nonlinear characteristics of the battery.

[0033] In order to estimate the battery SOC more accurately, the embodiment of the present disclosure adopts the ampere-hour integration method to correct the SOC estimation value when the accuracy of the battery equivalent model is low. A first threshold and a second threshold are set in advance, and the first threshold is less than the second threshold. Based on the relationship between the first SOC value and the first threshold and the second threshold, it is determined whether it is necessary to use the Kalman filter algorithm and the ampere-hour integration method to jointly estimate the SOC, so as to reduce the impact of the Kalman filter algorithm on the estimation accuracy caused by the insufficient accuracy of the corresponding model.

[0034] It should be noted that, in order to facilitate the description of the solution provided by the embodiment of the present disclosure, the application stage or the test stage of the battery is not limited, and the SOC value obtained by the Kalman filter algorithm is recorded as the first SOC value of the battery, and the SOC value obtained by the ampere-hour integration method is recorded as the second SOC value of the battery.

[0035] It is understandable that the battery adopts a constant current pulse working condition during the test phase. Under the constant current pulse working condition, the battery is pulse-discharged, the real voltage of the battery terminal at different charging and discharging times is measured, and the operating parameters corresponding to different charging and discharging times are obtained. The Kalman filter algorithm and the ampere-hour integration method are used to obtain the corresponding output voltages, which are recorded as the first SOC value and the second SOC value. At any time, the first SOC value and the second SOC value are weighted and fused to obtain the target SOC value, and the weight of the weighted fusion can be determined according to actual needs.

[0036] A reference SOC value is selected, wherein the type of the reference SOC value includes at least one of a first SOC value, a second SOC value and a target SOC value. The type of the reference SOC value can be determined according to actual needs, and the error is determined by the difference between the reference SOC value and the measured SOC value at the corresponding charging and discharging moment.

[0037] The first threshold and the second threshold are determined according to the distribution of errors at different charging and discharging moments of the battery in the test phase. The difference between the actual voltage and the reference SOC value at each moment is calculated to obtain the error at each moment. The first threshold and the second threshold are determined according to the error. The first threshold is denoted as a and the second threshold is denoted as b. The value range of the battery SOC value can be divided into three intervals: [0, a], [a, b], and [b, 1].

[0038] It should be noted that the first threshold and the second threshold are determined according to the distribution of errors at different charging and discharging moments of the battery during the test phase. The type of reference SOC value selected and the specific method for determining the first threshold and the second threshold can be determined according to actual needs, and the embodiments of the present disclosure are not limited to this.

[0039] For example, the complete discharge cycle can be used as the time axis, and according to the errors at each moment, the terminal voltage error curve estimated by the Kalman filter algorithm under the pulse discharge condition can be obtained (the error curve corresponding to the first SOC value as the reference SOC value), and the first threshold and the second threshold can be determined by the change trend of the error curve, or, according to the error curve, multiple groups of first thresholds and second thresholds can be set based on preliminary experience. For each group of first thresholds and second thresholds, the error curves corresponding to the first SOC value, the second SOC value and the target SOC value are used respectively. By analyzing and calculating the total error of the data set, the first threshold and the second threshold corresponding to the optimal group are determined from the multiple groups, or, the first threshold and the second threshold are iterated using an optimization algorithm to find the optimal solution for the first threshold and the second threshold group that meets the conditions, etc.

[0040] After obtaining the first SOC value and the second SOC value in step S102, the first SOC value is compared with the first threshold value and the second threshold value. In step S103, if it is determined that the first SOC value is greater than the first threshold value and less than the second threshold value, that is, it is determined that the first SOC value is within the interval [a, b], the first SOC value and the second SOC value are weightedly fused to obtain the target SOC value. The weight of the weighted fusion can be determined according to actual needs.

[0041] Step S104, taking the target SOC value as the estimated SOC value of the battery at the current moment; Specifically, in step S104, the target SOC value determined in step S103 is used as the estimated SOC value of the battery at the current moment.

[0042] The technical solution provided by the embodiment of the present disclosure pre-sets a first threshold and a second threshold. At the current moment, a Kalman filter algorithm is used to obtain a first SOC value of the battery, and an ampere-hour integration method is used to obtain a second SOC value of the battery. When it is determined that the first SOC value is within the interval formed by the first threshold and the second threshold, the Kalman filter algorithm and the ampere-hour integration method are used to jointly perform SOC estimation, and the first SOC value and the second SOC value are weightedly fused to obtain a target SOC value as the SOC estimation value at the current moment. The advantages of the two SOC estimation methods can be fully utilized, the accuracy and robustness of the SOC estimation are improved, and the SOC estimation precision is effectively improved.

[0043] In a possible implementation, the first SOC value and the second SOC value are obtained, and then the following steps are further included: If it is determined that the first SOC value is not greater than the first threshold, the second SOC value is used as the target SOC value.

[0044] Specifically, after obtaining the first SOC value and the second SOC value of the battery at the current moment, the first SOC value is compared with the first threshold value and the second threshold value. If it is determined that the first SOC value is not greater than the first threshold value, that is, the first SOC value is within the interval [0, a], the second SOC value estimated by the ampere-hour integration method is used as the target SOC value, and the target SOC value is used as the estimated SOC value of the battery at the current moment.

[0045] The technical solution provided by the embodiment of the present disclosure divides the battery SOC value into three intervals by setting a first threshold and a second threshold, and uses the first SOC value estimated by the Kalman filter algorithm as the object of comparison. When it is determined that the first SOC value is not greater than the first threshold, that is, when the battery is in a low-power state, the second SOC value estimated by the ampere-hour integration method is used as the SOC estimation value of the battery at the current moment, which can effectively reduce the impact of the reduction in estimation accuracy caused by insufficient accuracy of the corresponding model of the Kalman filter algorithm.

[0046] It is understandable that under low battery conditions, the error of the battery corresponding model of the established Kalman filter algorithm increases, which will reduce the accuracy of the Kalman filter algorithm. In order to ensure the accuracy of SOC estimation, while considering the computational complexity of the algorithm, the introduction of the ampere-hour integration method in the later stage of discharge can overcome the defects of the two algorithms themselves, have higher reliability, and effectively improve the estimation precision and accuracy.

[0047] In a possible implementation, the first SOC value and the second SOC value are obtained, and then the following steps are further included: If it is determined that the first SOC value is not less than the second threshold, the first SOC value is used as the target SOC value.

[0048] Specifically, after obtaining the first SOC value and the second SOC value of the battery at the current moment, the first SOC value is compared with the first threshold and the second threshold. If it is determined that the first SOC value is not less than the second threshold, that is, the first SOC value is within the interval [b, 1], the first SOC value estimated by the Kalman filtering algorithm is used as the target SOC value, and the target SOC value is used as the estimated SOC value of the battery at the current moment.

[0049] The technical solution provided by the embodiment of the present disclosure divides the battery SOC value into three intervals by setting a first threshold and a second threshold, and uses the first SOC value estimated by the Kalman filter algorithm as a comparison object. When it is determined that the first SOC value is not less than the second threshold, that is, the battery is in a high power state, the first SOC value estimated by the Kalman filter algorithm is used as the SOC estimation value of the battery at the current moment, thereby providing a more accurate SOC estimation value.

[0050] It is understandable that under high power conditions, the estimation error of the ampere-hour integration method may accumulate, resulting in a decrease in estimation accuracy, but the Kalman filter algorithm can handle nonlinear changes in voltage and measurement noise, with higher reliability, estimation accuracy and precision.

[0051] Figure 2 A flow chart of a method for determining an SOC value provided by an embodiment of the present disclosure is shown in FIG. Figure 2 As shown, Figure 2 As shown, taking any moment of any battery in the charging and discharging stage as an example, each step of the method for estimating the battery state of charge SOC is described in detail.

[0052] The current time is recorded as t, the operating parameters of the battery at the current time are obtained, and based on the operating parameters, the SOC value of the battery is estimated using the Kalman filter algorithm and the ampere-hour integration method respectively. The first SOC value estimated by the Kalman filter algorithm is recorded as , the second SOC value estimated by the ampere-hour integration method is recorded as .

[0053] Compare the obtained The relationship between the size of the preset first threshold a and the second threshold b.

[0054] If confirmed is in the interval [0, a], As the target SOC value, that is, the estimated SOC value of the battery at the current moment .

[0055] If confirmed is in the interval [a, b], then and Perform weighted fusion to and As the first SOC value and the second SOC value The corresponding weights are calculated As the target SOC value, that is, the estimated SOC value of the battery at the current moment .

[0056] If confirmed is in the interval [b, 1], As the target SOC value, that is, the estimated SOC value of the battery at the current moment .

[0057] It can be understood that the intervals [0, a], [a, b] and [b, 1] are three consecutive intervals obtained by dividing the interval [0, 1] by the first threshold a and the second threshold b. The upper and lower limits of the intervals are fixed, and writing them as open intervals or closed intervals does not affect the actual implementation of the technical solution.

[0058] In one possible implementation, the reference SOC value is the target SOC value; The first threshold and the second threshold are obtained in the following way: Pre-setting a plurality of threshold groups, each threshold group includes a reference first threshold and a reference second threshold, and the reference first threshold is less than the reference second threshold; Obtain the error corresponding to the charge and discharge time of the battery under each threshold group condition during the test phase, where the error is the difference between the measured SOC value and the target SOC value; According to the error corresponding to each moment under each threshold group condition, the mean square error corresponding to each threshold group is calculated; A target threshold group corresponding to the minimum mean square error among the mean square errors is obtained, and a reference first threshold corresponding to the target threshold group is used as the first threshold, and a reference second threshold corresponding to the target threshold group is used as the second threshold.

[0059] Specifically, when determining the first threshold and the second threshold, the target SOC value is used as a reference SOC value. It can be understood that the target SOC value has different values ​​in different intervals and is determined by the first SOC value and the second SOC value.

[0060] A plurality of threshold groups are preset, each of which includes a reference first threshold and a reference second threshold, wherein the reference first threshold is less than the reference second threshold. The determination method and number of the threshold groups can be determined according to actual needs. For example, the interval between the first threshold and the second threshold can be determined based on the staff's analysis of the error distribution, and the values ​​are randomly determined from the intervals corresponding to the first threshold and the second threshold to form a threshold group.

[0061] The battery adopts constant current pulse working conditions during the test phase. Under the constant current pulse working conditions, the actual voltage of the battery terminal at different charging and discharging moments is measured, and the operating parameters corresponding to each different charging and discharging moment are obtained. The Kalman filter algorithm and the ampere-hour integration method are used to obtain the corresponding output voltages, which are recorded as the first SOC value and the second SOC value.

[0062] For each threshold group, under the conditions of each threshold group, the target SOC value corresponding to each charging and discharging moment is determined. It can be understood that the method for determining the target SOC value corresponding to each charging and discharging moment is related to the reference threshold in each threshold group. The determination method is described above and will not be repeated here.

[0063] At each moment, the actual voltage of the battery terminal is obtained as the measured SOC value, and the difference between the measured SOC value and the target SOC value is calculated, and the difference is taken as the error at that moment.

[0064] For each threshold group, the mean square error corresponding to each threshold group is calculated according to the error corresponding to each moment under the condition of each threshold group.

[0065] For example, in the time axis of the test phase, n moments are selected according to the time coordinates. For any moment j, the measured SOC value corresponding to moment j is recorded as , the target SOC value is recorded as , the error corresponding to time j is , j=1,……,n.

[0066] Calculate the mean square error MSE at n moments. The MSE calculation formula is as follows:

[0067] For each threshold group, the corresponding mean square error MSE is calculated, and the threshold group corresponding to the minimum mean square error among the mean square errors is taken as the target threshold group, the reference first threshold corresponding to the target threshold group is taken as the first threshold, and the reference second threshold corresponding to the target threshold group is taken as the second threshold.

[0068] It can be understood that the method of selecting each moment when calculating the mean square error includes but is not limited to random selection, uniform selection based on the SOC value interval, or uniform selection based on the time length interval, etc. The selection method and number of each moment can be determined according to actual needs, and the embodiments of the present disclosure do not limit this.

[0069] The technical solution provided by the embodiment of the present disclosure is to pre-set multiple threshold groups, calculate the mean square error of the errors between the actually measured SOC values ​​and the target SOC values ​​at multiple moments in the test phase corresponding to each threshold group, and select a threshold group that can minimize the mean square error from the multiple threshold groups to determine the first threshold and the second threshold that are most suitable for the battery characteristics, thereby reducing the deviation between the actually measured SOC value and the target SOC value and improving the accuracy of SOC estimation.

[0070] In one possible implementation, the reference SOC value is the target SOC value; The first threshold and the second threshold are obtained in the following way: Obtaining a first SOC value, a second SOC value, and a measured SOC value corresponding to the charging and discharging moments of the battery in a test phase; Define an objective function, which is used to measure the comprehensive error of the battery in the test phase. The comprehensive error is determined according to the difference between the measured SOC value and the target SOC value corresponding to different charge and discharge moments. The target SOC value is determined according to the first SOC value, the second SOC value and the threshold value group. The threshold value group includes a reference first threshold value and a reference second threshold value. With the goal of minimizing the objective function, the optimal solution of the threshold group is determined; A reference first threshold in the optimal solution of the threshold group is used as the first threshold, and a reference second threshold is used as the second threshold.

[0071] Specifically, in order to further improve the overall accuracy of SOC estimation and reduce the comprehensive error between the measured SOC value and the target SOC value, the embodiment of the present disclosure adopts an optimization algorithm to determine the first threshold and the second threshold.

[0072] The battery adopts constant current pulse working conditions during the test phase. Under the constant current pulse working conditions, the actual voltage of the battery terminal at different charging and discharging moments is measured, and the operating parameters corresponding to each different charging and discharging moment are obtained. The Kalman filter algorithm and the ampere-hour integration method are used to obtain the corresponding output voltages, which are recorded as the first SOC value and the second SOC value.

[0073] The threshold group is used as the target optimization object, and the threshold group includes a reference first threshold and a reference second threshold. The determination method of the target SOC value corresponding to each charging and discharging moment is related to the reference threshold in the threshold group. The determination method is described above and will not be repeated here.

[0074] Define an objective function to measure the comprehensive error of the battery during the test phase. The comprehensive error is determined based on the difference between the measured SOC value and the target SOC value corresponding to different charging and discharging moments. For the estimation of the battery state of charge SOC, the objective function can be defined as mean square error (MSE) or root mean square error (RMSE).

[0075] With the goal of minimizing the objective function, an optimization algorithm is used to find the optimal solution of the threshold group that minimizes the objective function, the optimal solution of the threshold group is determined, and the reference first threshold in the optimal solution of the threshold group is used as the first threshold, and the reference second threshold is used as the second threshold.

[0076] It is understandable that the specific type of optimization algorithm used may include but is not limited to genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing algorithm (SA), etc., which can be determined according to actual needs.

[0077] For example, the optimization algorithm is a particle swarm algorithm, which defines each particle to represent a threshold group (a, b), and presets the value range of a and b.

[0078] Within the value range, initialize the particle swarm, set the size of the particle swarm, randomly initialize the position and speed of each particle, and set the historical optimal position and global optimal position of the particle.

[0079] The mean square error is defined as the objective function. In each iteration, the fitness value of each particle is calculated, the historical optimal position and the global optimal position of each particle are updated, and the position of the particle is adjusted according to the fitness value and speed of the particle.

[0080] When the preset number of iterations is reached or the termination condition is met (such as the convergence of the optimization algorithm), the iteration is stopped and the global optimal position is the optimal solution of the threshold group.

[0081] The technical solution provided by the embodiment of the present disclosure defines an objective function for measuring the comprehensive error of the battery in the test phase, and optimizes the threshold group to determine the optimal threshold group by minimizing the objective function. The optimization algorithm can automatically find the optimal or near-optimal threshold group, thereby reducing human errors and more accurately determining the first threshold and the second threshold that best suit the battery characteristics, thereby reducing the deviation between the measured SOC value and the target SOC value, and further improving the accuracy of SOC estimation.

[0082] In a possible implementation, estimating the SOC based on the operating parameters to obtain a first SOC value of the battery includes: Establish a second-order equivalent circuit model corresponding to the battery, which includes two RC links; According to the second-order equivalent circuit model, the state equation and observation equation are constructed. The state equation is used to describe the process of the state variables inside the battery changing over time, and the observation equation is used to describe the determination of the measurable output of the battery outside according to the state variables; The battery SOC value and the voltage values ​​on the two RC links are used as state variables of the state equation, the state variables and the terminal current value of the battery are used as input variables of the observation equation, and the terminal voltage value of the battery is used as the output variable of the observation equation. The state equation and the observation equation are discretized to obtain the discretized state equation and observation equation; According to the operating parameters of the battery in the charge and discharge stage, a discretized state equation and observation equation are used to obtain a first SOC value of the battery, and the operating parameters include the terminal current of the battery.

[0083] Specifically, the embodiment of the present disclosure estimates the SOC using the Extended Kalman Filter (EKF) algorithm in the Kalman filter algorithm to obtain the first SOC value of the battery. The Extended Kalman Filter algorithm has the advantages of recursive processing, linear processing, and robustness to noise.

[0084] Taking any battery as an example, a second-order equivalent circuit model corresponding to the battery is established, and the state of the linear dynamic system is estimated by the second-order equivalent circuit model. Figure 3 A schematic diagram of a second-order equivalent circuit model provided by an embodiment of the present disclosure is shown in FIG. Figure 3 As shown, is the open circuit voltage, I is the terminal current of the battery, is the ohmic internal resistance, and is the polarization internal resistance, and For polarized capacitors, the second-order equivalent circuit model includes two RC links. and , and They constitute the RC links respectively. and They are the voltages corresponding to the two RC links respectively.

[0085] According to the second-order equivalent circuit model, the state equation and observation equation are constructed. The state equation is used to describe the process of the state variables inside the battery changing over time, and the observation equation is used to describe the determination of the measurable output of the battery outside based on the state variables.

[0086] The battery SOC value and the voltage value on the two RC links are used as the state variables of the state equation x , taking the state variables and the terminal current value of the battery as the input variables of the observation equation u It can be understood that the state variables and input variables are expressed in the form of vectors.

[0087] The terminal voltage of the battery is used as the output variable of the observation equation y , the state equation and observation equation are discretized to obtain the discretized state equation and observation equation.

[0088] Time step k State variables The corresponding state equations, and output variables The corresponding observation equation is expressed as follows:

[0089] In the formula, is the time step k-1 status variable, is the time step k -1 input variable, Represents the time step k -1 state equation function, is the time step k -1 is the system noise of the linear dynamic system.

[0090] is the time step k The input variables are Represents the time step k The observation equation function is is the time step k The measurement noise of the linear dynamic system.

[0091] The discretized ampere-hour integral model is expressed as follows:

[0092] In the formula, Δt is the defined time step, It is in k The second SOC value at the end of the time step, For the k - the second SOC value at the end of 1 time step, is the Coulomb efficiency, is the actual capacity of the calibrated battery. For batteries k The average current in a time step.

[0093] Combined with the discretized ampere-hour integral model, the time step k State variables and output variables Discretize the time step of the battery model k State variables and output variables The linear discretization representation of .

[0094] Discretized time step k State variables The corresponding state equation is expressed as follows:

[0095] Discretized time step k Output variables The corresponding observation equation is expressed as follows:

[0096] In the formula, and are the voltages corresponding to the two RC links respectively, SOC represents the SOC value of the battery, , The resistances corresponding to the two RC links are: Indicates the terminal voltage of the battery, the corresponding foot mark k and k -1 represents the time step k and time step k -1, is the time step k The battery SOC value is The open circuit voltage at .

[0097] After determining the discretized state equation and observation equation, for any current moment, according to the operating parameters of the battery in the charge and discharge stages, the discretized state equation and observation equation are used to obtain the first SOC value of the battery at the current moment, and the operating parameters include the terminal current of the battery.

[0098] In a possible implementation, the weight corresponding to the first SOC value and the weight corresponding to the second SOC value are obtained in the following manner: Acquire a first SOC value and a second SOC value of the battery corresponding to different reference times in a test phase, wherein the first SOC value corresponding to the reference time is greater than a first threshold value and less than a second threshold value; For each reference moment, the difference between the first SOC value and the SOC average value is calculated to obtain a first SOC deviation value, the difference between the second SOC value and the SOC average value is calculated to obtain a second SOC deviation value, and the SOC average value is the average of the first SOC value and the second SOC value; A covariance matrix is ​​constructed using the first SOC deviation value and the second SOC deviation value corresponding to each reference moment, wherein the two diagonal elements of the covariance matrix are the variances between the first SOC deviation value and the second SOC deviation value, and the two non-diagonal elements are the covariances between the first SOC deviation value and the second SOC deviation value; Performing eigendecomposition on the covariance matrix to obtain a first eigenvalue, a second eigenvalue, and eigenvectors corresponding to each eigenvalue, wherein the eigenvalue represents the variance of the covariance matrix in the direction of the corresponding eigenvector, and each eigenvector includes a first component and a second component, wherein the first component represents the weight of the first SOC value on the corresponding eigenvector, and the second component represents the weight of the second SOC value on the corresponding eigenvector; Determine the larger value of the first eigenvalue and the second eigenvalue, and use the eigenvector corresponding to the larger value as the first principal component; The first component and the second component corresponding to the first principal component are normalized to obtain a weight corresponding to the first SOC value and a weight corresponding to the second SOC value, respectively.

[0099] Specifically, within the interval [a, b] constructed by the first threshold and the second threshold, the principal component analysis (PCA) method is used to determine the corresponding weight when the first SOC value and the second SOC value are fused, so that the first SOC value and the second SOC value are fused according to the contribution rate.

[0100] The weight corresponding to the first SOC value and the weight corresponding to the second SOC value are obtained in the following way: Obtain the first SOC value and the second SOC value corresponding to different reference times of the battery in the test phase. The first SOC value corresponding to the reference time is greater than the first threshold value and less than the second threshold value. That is, for any reference time t, the first SOC value corresponding to time t must be satisfied. It is within the interval [a, b]. It can be understood that the determination method and number of reference moments can be determined according to actual needs.

[0101] For each reference time t, the first SOC value is , the second SOC value is ,Will and The mean is the SOC average value. , and calculate separately and and The difference between and the second SOC deviation value .

[0102]

[0103]

[0104] The covariance matrix is ​​constructed by using the first SOC deviation value and the second SOC deviation value corresponding to each reference time, and the covariance matrix is ​​recorded as .

[0105]

[0106] In the formula, when i is 1 or 2, They represent the first SOC deviation value and the second SOC deviation value respectively.

[0107] Diagonal elements refer to the elements located on the main diagonal from the upper left corner to the lower right corner. The covariance matrix The two diagonal elements are the variance of the first SOC deviation value and the variance between the second SOC deviation value .

[0108] =

[0109] =

[0110] The two off-diagonal elements are the covariance between the first SOC deviation values The covariance between the second SOC deviation value .

[0111] =

[0112] =

[0113] Perform eigendecomposition on the covariance matrix and solve =0, get the first eigenvalue , the second eigenvalue , I is the unit matrix, and the eigenvalue represents the variance of the covariance matrix in the direction of the corresponding eigenvector.

[0114] For each eigenvalue , with equation =0, determine the first eigenvalue The corresponding first eigenvector , the second eigenvalue The corresponding eigenvector Each feature vector includes a first component and a second component, the first component represents the weight of the first SOC value on the corresponding feature vector, and the second component represents the weight of the second SOC value on the corresponding feature vector.

[0115] Determine the larger value of the first eigenvalue and the second eigenvalue, and use the eigenvector corresponding to the larger value as the first principal component. Usually the eigenvectors have been normalized, that is, their length (or norm) is 1. If it is determined that the first principal component has been normalized, the first component and the second component corresponding to the first principal component are used as the weight corresponding to the first SOC value and the weight corresponding to the second SOC value, respectively.

[0116] If it is determined that the first principal component is not normalized, the first component and the second component corresponding to the first principal component are normalized, and each component is divided by the norm to obtain the normalized first component as the weight corresponding to the first SOC value, and the normalized second component is obtained as the weight corresponding to the second SOC value.

[0117] The technical solution provided by the embodiment of the present disclosure uses the principal component analysis method to determine the weight when the first SOC value and the second SOC value are merged. The respective weights can be dynamically adjusted according to the characteristics of the actual first SOC value and the second SOC value, so as to obtain more accurate and stable SOC estimation results under different working conditions, and better optimize the combined use effect of the Kalman filter algorithm and the ampere-hour integration method.

[0118] It should be understood that the embodiments of the present disclosure are only used as a specific example to explain the present invention in detail, and the described embodiments are only intended to facilitate the understanding of the present invention and do not have any limiting effect on the present invention.

[0119] Figure 4 A schematic diagram of a battery state of charge (SOC) estimation device provided in an embodiment of the present disclosure is shown in FIG. Figure 4 As shown, the battery state of charge SOC estimation device 400 includes: The operating parameter acquisition module 401 is used to obtain the operating parameters of the battery at the current moment in the charging and discharging stage; The SOC value estimation module 402 is used to estimate the SOC from the operating parameters using a Kalman filter algorithm to obtain a first SOC value of the battery; and to estimate the SOC from the operating parameters using an ampere-hour integration method to obtain a second SOC value of the battery; The SOC value processing module 403 is used to perform weighted fusion on the first SOC value and the second SOC value to obtain a target SOC value if it is determined that the first SOC value is greater than the first threshold value and less than the second threshold value; An SOC value determination module 404 is used to use the target SOC value as an estimated SOC value of the battery at the current moment; Among them, the first threshold value and the second threshold value are determined according to the distribution of errors at different charging and discharging moments of the battery in the test phase, and the error is the difference between the reference SOC value and the measured SOC value at the corresponding charging and discharging moment; the type of reference SOC value includes at least one of the first SOC value, the second SOC value and the target SOC value.

[0120] In a possible implementation, a first SOC value and a second SOC value are obtained, and then an SOC value processing module is used to: If it is determined that the first SOC value is not greater than the first threshold, the second SOC value is used as the target SOC value.

[0121] In a possible implementation, a first SOC value and a second SOC value are obtained, and then an SOC value processing module is used to: If it is determined that the first SOC value is not less than the second threshold, the first SOC value is used as the target SOC value.

[0122] In a possible implementation, the reference SOC value is a target SOC value; the battery state of charge SOC estimation device further includes: a threshold determination module, which is used to: Pre-setting a plurality of threshold groups, each threshold group includes a reference first threshold and a reference second threshold, and the reference first threshold is less than the reference second threshold; Obtain the error corresponding to the charge and discharge time of the battery under each threshold group condition during the test phase, where the error is the difference between the measured SOC value and the target SOC value; According to the error corresponding to each moment under each threshold group condition, the mean square error corresponding to each threshold group is calculated; A target threshold group corresponding to the minimum mean square error among the mean square errors is obtained, and a reference first threshold corresponding to the target threshold group is used as the first threshold, and a reference second threshold corresponding to the target threshold group is used as the second threshold.

[0123] In a possible implementation, the reference SOC value is a target SOC value; and the threshold determination module is used to: Obtaining a first SOC value, a second SOC value, and a measured SOC value corresponding to the charging and discharging moments of the battery in a test phase; Define an objective function, which is used to measure the comprehensive error of the battery in the test phase. The comprehensive error is determined according to the difference between the measured SOC value and the target SOC value corresponding to different charge and discharge moments. The target SOC value is determined according to the first SOC value, the second SOC value and the threshold value group. The threshold value group includes a reference first threshold value and a reference second threshold value. With the goal of minimizing the objective function, the optimal solution of the threshold group is determined; A reference first threshold in the optimal solution of the threshold group is used as the first threshold, and a reference second threshold is used as the second threshold.

[0124] In a possible implementation, the SOC value estimation module is used to: Establish a second-order equivalent circuit model corresponding to the battery, which includes two RC links; According to the second-order equivalent circuit model, the state equation and observation equation are constructed. The state equation is used to describe the process of the state variables inside the battery changing over time, and the observation equation is used to describe the determination of the measurable output of the battery outside according to the state variables; The battery SOC value and the voltage values ​​on the two RC links are used as state variables of the state equation, the state variables and the terminal current value of the battery are used as input variables of the observation equation, and the terminal voltage value of the battery is used as the output variable of the observation equation. The state equation and the observation equation are discretized to obtain the discretized state equation and observation equation; According to the operating parameters of the battery in the charge and discharge stage, a discretized state equation and observation equation are used to obtain a first SOC value of the battery, and the operating parameters include the terminal current of the battery.

[0125] In a possible implementation, the battery state of charge SOC estimation device further includes: a weight determination module, which is used to: Acquire a first SOC value and a second SOC value of the battery corresponding to different reference times in a test phase, wherein the first SOC value corresponding to the reference time is greater than a first threshold value and less than a second threshold value; For each reference moment, the difference between the first SOC value and the SOC average value is calculated to obtain a first SOC deviation value, the difference between the second SOC value and the SOC average value is calculated to obtain a second SOC deviation value, and the SOC average value is the average of the first SOC value and the second SOC value; A covariance matrix is ​​constructed using the first SOC deviation value and the second SOC deviation value corresponding to each reference moment, wherein the two diagonal elements of the covariance matrix are the variances between the first SOC deviation value and the second SOC deviation value, and the two non-diagonal elements are the covariances between the first SOC deviation value and the second SOC deviation value; Performing eigendecomposition on the covariance matrix to obtain a first eigenvalue, a second eigenvalue, and eigenvectors corresponding to each eigenvalue, wherein the eigenvalue represents the variance of the covariance matrix in the direction of the corresponding eigenvector, and each eigenvector includes a first component and a second component, wherein the first component represents the weight of the first SOC value on the corresponding eigenvector, and the second component represents the weight of the second SOC value on the corresponding eigenvector; Determine the larger value of the first eigenvalue and the second eigenvalue, and use the eigenvector corresponding to the larger value as the first principal component; The first component and the second component corresponding to the first principal component are normalized to obtain a weight corresponding to the first SOC value and a weight corresponding to the second SOC value, respectively.

[0126] The device of the embodiments of the present disclosure can execute the method provided by the embodiments of the present disclosure, and the implementation principles are similar. The actions performed by each module in the device of each embodiment of the present disclosure correspond to the steps in the method of each embodiment of the present disclosure. For the detailed functional description of each module of the device, please refer to the description in the corresponding method shown in the previous text, which will not be repeated here.

[0127] In addition, in the embodiments of the present disclosure, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories), or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0128] An embodiment of the present disclosure provides an electronic device (computer device / equipment / system), including a memory, a processor, and a computer program stored in the memory, and the processor executes the above-mentioned computer program to implement the steps of the method provided in any optional embodiment of the present disclosure and achieve corresponding technical effects.

[0129] In an optional embodiment, an electronic device is provided. Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure is shown in FIG. Figure 5 As shown, the electronic device 500 includes: a processor 501 and a memory 503. The processor 501 and the memory 503 are connected, such as through a bus 502. Optionally, the electronic device 500 may also include a transceiver 504, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 504 is not limited to one, and the structure of the electronic device 500 does not constitute a limitation on the embodiments of the present disclosure.

[0130] Processor 501 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 501 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0131] The bus 502 may include a path to transmit information between the above components. The bus 502 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 502 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0132] The memory 503 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage medium, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.

[0133] The memory 503 is used to store the computer program for executing the embodiment of the present disclosure, and the execution is controlled by the processor 501. The processor 501 is used to execute the computer program stored in the memory 503 to implement the steps shown in the above method embodiment.

[0134] The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), wearable devices, etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0135] An embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps and corresponding contents of the aforementioned method embodiment can be implemented.

[0136] The embodiments of the present disclosure also provide a computer program product, including a computer program, which can implement the steps and corresponding contents of the aforementioned method embodiments when executed by a processor.

[0137] It should be noted that the computer-readable storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), 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.

[0138] In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a 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. A computer-readable signal medium 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 combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0139] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, 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 cases involving 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).

[0140] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than that shown or described in the text.

[0141] It should be understood that, although the flowchart of the embodiment of the present disclosure indicates each operation step by arrows, the implementation order of these steps is not limited to the order indicated by the arrows. Unless clearly stated herein, in some implementation scenarios of the embodiment of the present disclosure, the implementation steps in each flowchart can be executed in other orders according to demand. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage in these sub-steps or stages can also be executed at different times. In scenarios with different execution times, the execution order of these sub-steps or stages can be flexibly configured according to demand, and the embodiment of the present disclosure does not limit this.

[0142] The above is only an optional implementation method for some implementation scenarios of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, without departing from the technical concept of the scheme of the present disclosure, other similar implementation methods based on the technical ideas of the present disclosure are also within the protection scope of the embodiments of the present disclosure.

Claims

1. A method for estimating a battery state of charge (SOC), characterized in that: include: Obtain the operating parameters of the battery at the current moment in the charging and discharging stage; Using a Kalman filter algorithm, estimating the SOC based on the operating parameters, and obtaining a first SOC value of the battery; estimating the SOC according to the operating parameters using an ampere-hour integration method to obtain a second SOC value of the battery; If it is determined that the first SOC value is greater than a first threshold value and less than a second threshold value, weighted fusion is performed on the first SOC value and the second SOC value to obtain a target SOC value; Taking the target SOC value as the estimated SOC value of the battery at the current moment; Among them, the first threshold value and the second threshold value are determined according to the distribution of errors of the battery at different charging and discharging moments in the test phase, and the error is the difference between the reference SOC value and the measured SOC value at the corresponding charging and discharging moment; the type of the reference SOC value includes at least one of the first SOC value, the second SOC value and the target SOC value.

2. The method for estimating the battery state of charge (SOC) according to claim 1, characterized in that: The first SOC value and the second SOC value are obtained, and then the following steps are further included: If it is determined that the first SOC value is not greater than the first threshold, the second SOC value is used as the target SOC value.

3. The method for estimating the battery state of charge (SOC) according to claim 1, characterized in that: The first SOC value and the second SOC value are obtained, and then the following steps are further included: If it is determined that the first SOC value is not less than the second threshold, the first SOC value is used as the target SOC value.

4. The method for estimating the battery state of charge (SOC) according to claim 1, characterized in that: The reference SOC value is a target SOC value; The first threshold and the second threshold are obtained in the following manner: Pre-setting a plurality of threshold groups, each threshold group comprising a reference first threshold and a reference second threshold, the reference first threshold being smaller than the reference second threshold; Obtaining the error corresponding to the charge and discharge time of the battery under each threshold value group condition during the test phase, wherein the error is the difference between the measured SOC value and the target SOC value; According to the error corresponding to each moment under each threshold group condition, the mean square error corresponding to each threshold group is calculated; A target threshold group corresponding to the minimum mean square error among the mean square errors is obtained, and a reference first threshold corresponding to the target threshold group is used as the first threshold, and a reference second threshold corresponding to the target threshold group is used as the second threshold.

5. The method for estimating the battery state of charge (SOC) according to claim 1, characterized in that: The reference SOC value is a target SOC value; The first threshold and the second threshold are obtained in the following manner: Obtaining a first SOC value, a second SOC value, and a measured SOC value corresponding to the charging and discharging moments of the battery in a test phase; defining an objective function, the objective function being used to measure a comprehensive error of the battery in a test phase, the comprehensive error being determined according to a difference between the measured SOC value and a target SOC value corresponding to different charge and discharge moments, the target SOC value being determined according to a first SOC value, a second SOC value, and a threshold value group, the threshold value group comprising a reference first threshold value and a reference second threshold value; Determine the optimal solution of the threshold value group with the goal of minimizing the objective function; A reference first threshold in an optimal solution of the threshold group is used as the first threshold, and a reference second threshold is used as the second threshold.

6. The method for estimating the battery state of charge (SOC) according to any one of claims 1 to 5, characterized in that: The estimating the SOC according to the operating parameters to obtain a first SOC value of the battery includes: Establishing a second-order equivalent circuit model corresponding to the battery, wherein the second-order equivalent circuit model includes two RC links; According to the second-order equivalent circuit model, construct a state equation and an observation equation, wherein the state equation is used to describe the process in which the state variable inside the battery changes over time, and the observation equation is used to describe the determination of a measurable output outside the battery according to the state variable; The battery SOC value and the voltage values ​​on the two RC links are used as state variables of the state equation, the state variables and the terminal current value of the battery are used as input variables of the observation equation, and the terminal voltage value of the battery is used as the output variable of the observation equation, and the state equation and the observation equation are discretized to obtain the discretized state equation and observation equation; According to the operating parameters of the battery in the charge and discharge stage, the discretized state equation and observation equation are used to obtain a first SOC value of the battery, wherein the operating parameters include the terminal current of the battery.

7. The method for estimating the battery state of charge (SOC) according to any one of claims 1 to 5, characterized in that: The weight corresponding to the first SOC value and the weight corresponding to the second SOC value are obtained in the following manner: Acquire a first SOC value and a second SOC value of the battery corresponding to different reference times in a test phase, wherein the first SOC value corresponding to the reference time is greater than a first threshold value and less than a second threshold value; For each reference moment, calculating the difference between the first SOC value and the SOC average value to obtain a first SOC deviation value, calculating the difference between the second SOC value and the SOC average value to obtain a second SOC deviation value, wherein the SOC average value is the average of the first SOC value and the second SOC value; Constructing a covariance matrix using the first SOC deviation value and the second SOC deviation value corresponding to each reference moment, wherein two diagonal elements of the covariance matrix are the variances between the first SOC deviation value and the second SOC deviation value, and two non-diagonal elements are the covariances between the first SOC deviation value and the second SOC deviation value; Performing eigendecomposition on the covariance matrix to obtain a first eigenvalue, a second eigenvalue, and eigenvectors corresponding to each eigenvalue, wherein the eigenvalue represents the variance of the covariance matrix in the direction of the corresponding eigenvector, and each eigenvector includes a first component and a second component, wherein the first component represents the weight of the first SOC value on the corresponding eigenvector, and the second component represents the weight of the second SOC value on the corresponding eigenvector; Determine a larger value between the first eigenvalue and the second eigenvalue, and use an eigenvector corresponding to the larger value as a first principal component; The first component and the second component corresponding to the first principal component are normalized to obtain a weight corresponding to the first SOC value and a weight corresponding to the second SOC value, respectively.

8. A battery state of charge (SOC) estimation device, characterized in that: include: An operating parameter acquisition module is used to obtain the operating parameters of the battery at the current moment in the charging and discharging stage; An SOC value estimation module, configured to estimate the SOC based on the operating parameters using a Kalman filter algorithm to obtain a first SOC value of the battery; estimating the SOC according to the operating parameters using an ampere-hour integration method to obtain a second SOC value of the battery; An SOC value processing module, configured to perform weighted fusion on the first SOC value and the second SOC value to obtain a target SOC value if it is determined that the first SOC value is greater than a first threshold value and less than a second threshold value; An SOC value determination module, configured to use the target SOC value as an estimated SOC value of the battery at a current moment; Among them, the first threshold value and the second threshold value are determined according to the distribution of errors of the battery at different charging and discharging moments in the test phase, and the error is the difference between the reference SOC value and the measured SOC value at the corresponding charging and discharging moment; the type of the reference SOC value includes at least one of the first SOC value, the second SOC value and the target SOC value.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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