Battery SOC (State of Charge) estimation method, device, equipment, storage medium and product

Abstract

The invention discloses a battery SOC estimation method, device and equipment, a storage medium and a product. Acquiring real-time operation parameters of the to-be-tested battery; on the basis of the real-time operation parameters, corresponding initial SOC values are obtained through calculation by using at least two different battery state of charge SOC estimation algorithms; determining the adaptive weight of each SOC estimation algorithm based on the current working condition of the to-be-tested battery and the real-time estimation error of each SOC estimation algorithm; and determining a target SOC value based on the initial SOC values and the adaptive weights of the SOC estimation algorithms corresponding to the initial SOC values. The initial SOC value is calculated by combining at least two different SOC estimation algorithms, then the adaptive weight is determined according to the current working condition of the battery and the real-time estimation error of each algorithm, and finally the weighted initial SOC values are fused to obtain the target SOC value. And through a self-adaptive fusion mechanism, the SOC estimation precision is effectively improved, and the adaptability to different working conditions and the anti-interference capability are enhanced.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a battery SOC estimation method, apparatus, device, storage medium, and product. Background Technology

[0002] Battery State of Charge (SOC) refers to the ratio of a battery's current remaining charge to its rated capacity. It is a crucial parameter in the Battery Management System (BMS), and its estimation accuracy directly affects the battery's efficiency, lifespan, and safety. In fields such as electric vehicles and energy storage systems, accurate SOC estimation can provide users with reliable driving range information and prevent damage to the battery caused by overcharging or over-discharging.

[0003] Currently, commonly used SOC estimation methods include the open-circuit voltage method, the ampere-hour integration method, the Kalman filter method, and the neural network method. However, these methods all have certain limitations. The open-circuit voltage method requires the battery to be stationary for a long time, making it difficult to apply under dynamic operating conditions; the ampere-hour integration method is prone to accumulating errors and is greatly affected by the accuracy of current measurement and the initial SOC; the Kalman filter method's estimation accuracy will significantly decrease when the battery model is inaccurate or has high noise; and the neural network method requires a large amount of training data, and its generalization ability needs to be improved.

[0004] Therefore, developing a battery SOC estimation method and system that can maintain high estimation accuracy, strong anti-interference ability and wide applicability under different operating conditions is of great practical significance. Summary of the Invention

[0005] This invention provides a battery SOC estimation method, apparatus, device, storage medium, and product to solve the problems of insufficient accuracy, weak anti-interference ability, and limited applicability of existing SOC estimation methods under different operating conditions.

[0006] According to one aspect of the present invention, a battery SOC estimation method is provided, comprising:

[0007] Obtain the real-time operating parameters of the battery under test;

[0008] Based on the real-time operating parameters, the corresponding preliminary SOC values ​​are calculated using at least two different battery state of charge (SOC) estimation algorithms.

[0009] Based on the current operating condition of the battery under test and the real-time estimation error of each SOC estimation algorithm, the adaptive weight of each SOC estimation algorithm is determined.

[0010] The target SOC value is determined based on the preliminary SOC values ​​and the adaptive weights of the SOC estimation algorithm corresponding to each preliminary SOC value.

[0011] According to another aspect of the present invention, a battery SOC estimation device is provided, comprising:

[0012] The parameter acquisition module is used to acquire the real-time operating parameters of the battery under test.

[0013] The preliminary value calculation module is used to calculate the corresponding preliminary SOC value based on the real-time operating parameters using at least two different battery state of charge (SOC) estimation algorithms.

[0014] The weight determination module is used to determine the adaptive weights of each SOC estimation algorithm based on the current operating conditions of the battery under test and the real-time estimation errors of each SOC estimation algorithm.

[0015] The target value calculation module is used to determine the target SOC value based on the preliminary SOC values ​​and the adaptive weights of the SOC estimation algorithm corresponding to each preliminary SOC value.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the battery SOC estimation method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the battery SOC estimation method according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the battery SOC estimation method according to any embodiment of the present invention.

[0022] The technical solution of this invention involves acquiring real-time operating parameters of the battery under test; based on these parameters, calculating preliminary SOC values ​​using at least two different battery state of charge (SOC) estimation algorithms; determining adaptive weights for each SOC estimation algorithm based on the current operating condition of the battery under test and the real-time estimation errors of each algorithm; and determining a target SOC value based on the preliminary SOC values ​​and the corresponding adaptive weights of the SOC estimation algorithms. By combining at least two different SOC estimation algorithms to calculate preliminary SOC values, and then determining the adaptive weights of each algorithm based on the current operating condition of the battery under test and the real-time estimation errors, the target SOC value is finally obtained by fusing these weighted preliminary SOC values. This adaptive fusion mechanism effectively improves the accuracy of SOC estimation and enhances adaptability and anti-interference capabilities under different operating conditions.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart of a battery SOC estimation method is provided in Embodiment 1 of the present invention;

[0026] Figure 2 This is a schematic diagram of a battery SOC estimation device provided in Embodiment 2 of the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1 This is a flowchart illustrating a battery SOC estimation method according to Embodiment 1 of the present invention. This embodiment is applicable to situations involving battery SOC estimation. The method can be executed by a battery SOC estimation device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0032] S110: Obtain the real-time operating parameters of the battery under test.

[0033] Among them, the battery under test is the battery whose state of charge needs to be monitored and estimated.

[0034] Specifically, real-time operating parameters of the battery under test are acquired through data acquisition equipment. To ensure timely capture of dynamic changes in the battery under test, the acquisition frequency of the data acquisition equipment can be set to 10-100Hz. Data acquisition equipment includes, but is not limited to, voltage sensors, current sensors, temperature sensors, and internal resistance testers.

[0035] Optionally, to ensure data quality, the collected data can be preprocessed, including but not limited to filtering, noise reduction, and normalization, to remove interference signals from the data.

[0036] S120. Based on the real-time operating parameters, at least two different battery state of charge (SOC) estimation algorithms are used to calculate the corresponding preliminary SOC values.

[0037] Specifically, due to the limitations of using a single algorithm to calculate the SOC value, at least two different battery state-of-charge (SOC) estimation algorithms are used based on real-time operating parameters to calculate the corresponding preliminary SOC values. This embodiment does not limit the specific SOC estimation algorithm; for example, the SOC estimation algorithm can be the ampere-hour integration method, the Kalman filter method, or the neural network method, etc.

[0038] S130. Based on the current operating condition of the battery under test and the real-time estimation error of each SOC estimation algorithm, determine the adaptive weight of each SOC estimation algorithm.

[0039] In this embodiment, the real-time estimation error is used to initially assess the reliability of each SOC estimation algorithm at the current moment. The smaller the real-time estimation error, the more reliable the estimation result of the corresponding estimation algorithm. The adaptive weighting dynamically adjusts the proportion of each algorithm in the calculation of the target SOC value based on the current operating condition of the battery under test and the real-time estimation error of each SOC estimation algorithm.

[0040] The current operating condition refers to the current working state of the battery under test. It's important to note that the determination of the operating condition is based on the dynamic operating characteristics of the battery under test. By real-time monitoring parameters such as the rate of change of charging and discharging current, resting time, and absolute current value, the operating condition can be classified into types such as "static operating condition," "dynamic operating condition," and "transitional operating condition." The criteria for determining a static operating condition are: the absolute value of the charging and discharging current of the battery under test ≤ 0.05C (C is the rated capacity), and the duration ≥ 5 minutes (adjustable as needed). For example, when an electric vehicle is turned off and left idle, or when an energy storage battery is not charged or discharged at night, the current is close to 0, which falls under a static operating condition. The criteria for determining a dynamic operating condition are: the absolute value of the charging and discharging current of the battery under test > 0.1C, or the rate of change of current > 0.1C / second (such as during acceleration, braking, or fast charging). For example, when an electric vehicle is in motion (especially during acceleration / deceleration), or when an energy storage battery is rapidly charging and discharging during peak and off-peak hours, these are both considered dynamic operating conditions. The criteria for determining a transitional operating condition are: a current level between static and dynamic (e.g., current 0.05C~0.1C, or just starting / stopping), serving as a buffer between the two operating conditions. Real-time current data collected by sensors allows direct determination of the current operating condition type, providing a basis for weight allocation.

[0041] Specifically, the current operating condition of the battery under test is first determined, and the real-time estimation error of each SOC estimation algorithm is calculated. Then, based on the current operating condition of the battery under test and the real-time estimation error of each SOC estimation algorithm, the adaptive weight of each SOC estimation algorithm is determined. The adaptive weight is determined such that the adaptive weight value is positively correlated with the matching degree between the corresponding algorithm and the current operating condition, and negatively correlated with the real-time estimation error of the corresponding algorithm.

[0042] S140. Based on the preliminary SOC values ​​and the adaptive weights of the SOC estimation algorithm corresponding to each preliminary SOC value, determine the target SOC value.

[0043] Specifically, for each preliminary SOC value, the current preliminary SOC value is multiplied by the adaptive weight of the SOC estimation algorithm corresponding to the current preliminary SOC value to obtain a weighted SOC value; these weighted SOC values ​​are summed to obtain the target SOC value.

[0044] The technical solution provided in Embodiment 1 of this invention obtains the real-time operating parameters of the battery under test; based on the real-time operating parameters, at least two different battery state of charge (SOC) estimation algorithms are used to calculate the corresponding preliminary SOC values; based on the current operating condition of the battery under test and the real-time estimation errors of each SOC estimation algorithm, adaptive weights for each SOC estimation algorithm are determined; based on the preliminary SOC values ​​and the adaptive weights of the corresponding SOC estimation algorithms, a target SOC value is determined. By combining at least two different SOC estimation algorithms to calculate the preliminary SOC values ​​separately, and then determining the adaptive weights of each algorithm based on the current operating condition of the battery under test and the real-time estimation errors of each algorithm, the target SOC value is finally obtained by fusing these weighted preliminary SOC values. This adaptive fusion mechanism effectively improves the accuracy of SOC estimation and enhances adaptability and anti-interference capability under different operating conditions.

[0045] In some embodiments, the real-time operating parameters include at least terminal voltage, charge / discharge current, battery temperature, and battery internal resistance. Comprehensive monitoring of these parameters can significantly improve the accuracy and reliability of battery SOC estimation, thereby optimizing battery performance and extending its lifespan.

[0046] In some embodiments, the real-time estimation error of each SOC estimation algorithm is determined based on each preliminary SOC value and the current reference SOC value, wherein the current reference SOC value is the SOC value determined by the open-circuit voltage method when the battery under test most recently met the resting conditions.

[0047] In this embodiment, the resting condition can be understood as the battery under test being in a resting state for a period of time exceeding a preset time threshold (e.g., 30 minutes). The current reference SOC value is the SOC value determined by the open-circuit voltage method when the battery under test most recently met the resting condition.

[0048] Specifically, the current reference SOC value is determined as follows: when the resting time of the battery under test reaches a preset time threshold, the resting condition is deemed met. Then, using the open-circuit voltage of the battery under test when the resting condition is met, and based on the correlation curve between the battery open-circuit voltage and the SOC value, the current reference SOC value is determined. Furthermore, based on the absolute value of the deviation between each preliminary SOC value and the current reference SOC value, the real-time estimation error of each SOC estimation algorithm is determined. The correlation curve between the battery open-circuit voltage and the SOC value was established experimentally.

[0049] The above technical solutions enable more reliable evaluation and calibration of the performance of each SOC estimation algorithm, which helps to improve the overall accuracy and reliability of SOC estimation in the battery management system.

[0050] In some embodiments, determining the adaptive weights of each SOC estimation algorithm based on the current operating condition of the battery under test and the real-time estimation errors of each SOC estimation algorithm includes: determining the adaptive weights of each SOC estimation algorithm using a preset fuzzy control rule based on the current operating condition of the battery under test and the real-time estimation errors of each SOC estimation algorithm, wherein the sum of each adaptive weight is 1, and the preset fuzzy control rule includes: assigning a first weight value to SOC estimation algorithms whose real-time estimation errors are less than or equal to a preset threshold and are determined to be suitable for the current operating condition; assigning a second weight value to SOC estimation algorithms whose real-time estimation errors are less than or equal to the preset threshold but are not suitable for the current operating condition; and assigning a third weight value to SOC estimation algorithms whose real-time estimation errors are greater than the preset threshold, wherein the first weight value is greater than the second weight value, and the second weight value is greater than the third weight value.

[0051] In this embodiment, the preset threshold is a benchmark used to evaluate the real-time estimation error of each SOC estimation algorithm. When the real-time estimation error of an algorithm is not greater than this preset threshold, its estimation result is considered to have high reliability; otherwise, the reliability is low. The preset fuzzy control rules can be understood as a set of pre-set logical rules or standards used to dynamically determine each adaptive weight based on the current operating condition of the battery under test and the real-time estimation error of each algorithm. The preset fuzzy control rules include: assigning a first weight value to SOC estimation algorithms whose real-time estimation error is less than or equal to the preset threshold and is determined to be suitable for the current operating condition; assigning a second weight value to SOC estimation algorithms whose real-time estimation error is less than or equal to the preset threshold but is not suitable for the current operating condition; and assigning a third weight value to SOC estimation algorithms whose real-time estimation error is greater than the preset threshold, wherein the first weight value is greater than the second weight value, and the second weight value is greater than the third weight value.

[0052] Optionally, to enhance the system's adaptability, the first weight value, the second weight value, and the third weight value are not fixed constants, but can be dynamically determined within their respective preset value ranges (e.g., the first weight value range [0.5, 0.8], the second weight value range [0.2, 0.4], and the third weight value range [0, 0.1]).

[0053] Specifically, based on the current operating conditions of the battery under test and the real-time estimation errors of each SOC estimation algorithm, a preset fuzzy control rule is used to determine the adaptive weights of each SOC estimation algorithm. The sum of these weights is 1, so as to ensure that the most accurate SOC estimation results can be obtained under different operating conditions, thereby improving the performance and reliability of the battery management system.

[0054] For example: The execution logic of this rule is explained by combining specific numerical values:

[0055] Set a global error threshold (e.g., 3%).

[0056] Error is judged as follows: when the real-time estimation error of an algorithm is ≤3%, it is considered to have "small error"; when the real-time estimation error of an algorithm is >3%, it is considered to have "large error".

[0057] Operating condition adaptability determination: For example, the ampere-hour integration method has good adaptability under static operating conditions but poor adaptability under dynamic operating conditions; the Kalman filter method is the opposite.

[0058] Weight allocation logic: Based on the above dual determination, fuzzy control rules are applied:

[0059] Under static operating conditions:

[0060] If the error of the ampere-hour integration method is 2% (≤3%, small error) and it is suitable for static operating conditions, then a higher first weight value (e.g., 0.5) should be assigned to it.

[0061] If the Kalman filter error is 4% (>3%, which is a large error), then a lower third weight value (e.g., 0.1) will be assigned regardless of whether the filter is adapted or not.

[0062] If the neural network method has an error of 3% (≤3%, small error) but poor adaptability, a moderate second weight value (e.g., 0.4) can be assigned. After normalization, the weight allocation conforms to 0.5 + 0.1 + 0.4 = 1.

[0063] Under dynamic operating conditions:

[0064] If the error of the ampere-hour integration method is 5% (>3%, which is a large error), then a third weight value (e.g., 0.1) is assigned.

[0065] If both the Kalman filter method and the neural network method have an error of 2% (≤3%, small error) and are both suitable for dynamic operating conditions, then both are assigned a first weight value (e.g., a baseline value of 0.45). After normalization, the weight allocation is w1=0.1, w2=0.45, w3=0.45, with a total of 1.

[0066] The above technical solutions can significantly improve the accuracy and robustness of battery SOC estimation, while enhancing the adaptability of the battery management system to different operating conditions, thereby optimizing battery performance and extending its service life.

[0067] In some embodiments, the method further includes: obtaining a calibrated SOC value determined based on the open-circuit voltage method under preset conditions; calculating the deviation between the target SOC value and the calibrated SOC value; and if the absolute value of the deviation is greater than a preset calibration threshold, correcting the target SOC value using the calibrated SOC value. This effectively detects and corrects long-term accumulated errors, thereby ensuring the continuous accuracy and reliability of SOC estimation.

[0068] In this embodiment, the preset conditions can be understood as pre-set conditions for triggering the calibration process. For example, it may be triggered periodically (e.g., hourly) or when the battery under test completes a full charge-discharge cycle. The calibration SOC value is the SOC value measured using the open-circuit voltage method when the preset conditions are met. This value serves as the benchmark for calibrating the final fusion result (target SOC value). The difference between the calibration SOC value and the current reference SOC value is that the calibration SOC value is used to correct the target SOC value under specific conditions, while the current reference SOC value is used to evaluate the reliability of the algorithm in real time, i.e., to calculate the real-time estimation error of each algorithm. The preset calibration threshold is a pre-set error limit used to determine whether the target SOC value needs correction. If the deviation between the target SOC value and the calibration SOC value exceeds this threshold (e.g., 5%), it is considered that there is a significant cumulative error, and the target SOC value needs to be corrected using the calibration SOC value.

[0069] Specifically, when preset conditions are met (such as reaching a periodic calibration cycle or completing a charge-discharge cycle), a calibration SOC value determined based on the open-circuit voltage method is obtained; then, the deviation between the target SOC value and the calibration SOC value is calculated; if the absolute value of this deviation is greater than a preset calibration threshold (such as 5%), the target SOC value is corrected using the calibration SOC value to eliminate accumulated errors. This embodiment does not limit the specific correction method; for example, a weighted average method or a filtering fusion method can be used to achieve the correction.

[0070] In some embodiments, the at least two different SOC estimation algorithms include the ampere-hour integral method with a temperature compensation coefficient, the extended Kalman filter method with dynamic parameter correction, and the BP neural network algorithm.

[0071] Specifically, this embodiment employs at least two different SOC estimation algorithms running in parallel to complement each other's strengths:

[0072] Ampere-hour integration method with temperature compensation coefficient: This algorithm introduces a temperature compensation coefficient into the traditional ampere-hour integration method. This coefficient is determined by looking up a table or calculation based on the real-time battery temperature and the current SOC value calculated by the ampere-hour integration method itself (e.g., 0.9-0.95 for temperatures below 0℃, 1.0 for temperatures between 0-40℃, and 0.95-1.0 for temperatures above 40℃). This compensation mechanism effectively reduces the impact of temperature on current measurement accuracy and battery coulombic efficiency, significantly improving the long-term estimation accuracy of the ampere-hour integration method under complex temperature environments.

[0073] The Extended Kalman Filter (EKF) method with dynamically corrected parameters: This algorithm operates online based on a pre-defined second-order RC equivalent circuit model of the battery. During real-time estimation, the algorithm takes the battery terminal voltage, charging / discharging current, and battery temperature as inputs, and calls a correction formula pre-fitted from experimental data to describe the relationship between internal resistance and temperature and state of charge (SOC) to dynamically update the model parameters. Then, the EKF algorithm is used to calculate the initial SOC value. This online parameter correction mechanism allows the model to continuously track changes in battery state, thus maintaining high estimation accuracy.

[0074] Backpropagation (BP) Neural Network Algorithm: During the training phase, a three-layer BP neural network is constructed. The input layer includes battery terminal voltage, charging / discharging current, temperature, and internal resistance, while the output layer is the State of Charge (SOC) value. Historical charging / discharging data is used to train the neural network, and adaptive learning rate and momentum factor are employed during training to improve training speed and accuracy. After training, real-time collected data is input into the neural network during practical applications to obtain preliminary SOC values. This method leverages the learning capability of neural networks, training with a large amount of historical data, enabling the neural network to provide accurate SOC estimations in real-world applications.

[0075] Example 2

[0076] Figure 2 This is a schematic diagram of a battery SOC estimation device provided in Embodiment 2 of the present invention. Figure 2 As shown, the device includes:

[0077] The parameter acquisition module 21 is used to acquire the real-time operating parameters of the battery under test;

[0078] The preliminary value calculation module 22 is used to calculate the corresponding preliminary SOC value based on the real-time operating parameters using at least two different battery state of charge (SOC) estimation algorithms.

[0079] The weight determination module 23 is used to determine the adaptive weight of each SOC estimation algorithm based on the current operating condition of the battery under test and the real-time estimation error of each SOC estimation algorithm.

[0080] The target value calculation module 24 is used to determine the target SOC value based on the preliminary SOC values ​​and the adaptive weights of the SOC estimation algorithm corresponding to each preliminary SOC value.

[0081] The technical solution provided in Embodiment 2 of this invention calculates a preliminary SOC value by combining at least two different SOC estimation algorithms. Then, adaptive weights are determined based on the current battery operating condition and the real-time estimation errors of each algorithm. Finally, these weighted preliminary SOC values ​​are fused to obtain the target SOC value. Through the adaptive fusion mechanism, the accuracy of SOC estimation is effectively improved, and the adaptability to different operating conditions and anti-interference ability are enhanced.

[0082] Optionally, the weight determination module 23 is specifically used to: determine the adaptive weights of each SOC estimation algorithm based on the current operating condition of the battery under test and the real-time estimation error of each SOC estimation algorithm using preset fuzzy control rules, wherein the sum of each adaptive weight is 1, and the preset fuzzy control rules include: assigning a first weight value to SOC estimation algorithms whose real-time estimation error is less than or equal to a preset threshold and is determined to be suitable for the current operating condition; assigning a second weight value to SOC estimation algorithms whose real-time estimation error is less than or equal to the preset threshold but is not suitable for the current operating condition; and assigning a third weight value to SOC estimation algorithms whose real-time estimation error is greater than the preset threshold, wherein the first weight value is greater than the second weight value, and the second weight value is greater than the third weight value.

[0083] Optionally, the real-time estimation error of each SOC estimation algorithm is determined based on each preliminary SOC value and the current reference SOC value, wherein the current reference SOC value is the SOC value determined by the open-circuit voltage method when the battery under test most recently met the resting conditions.

[0084] Optionally, the device further includes:

[0085] The calibration value acquisition module is used to acquire the calibration SOC value determined based on the open-circuit voltage method under preset conditions.

[0086] A deviation calculation module is used to calculate the deviation between the target SOC value and the calibrated SOC value;

[0087] The target correction module is used to correct the target SOC value using the calibration SOC value if the absolute value of the deviation is greater than a preset calibration threshold.

[0088] Optionally, the at least two different SOC estimation algorithms include the ampere-hour integral method with a temperature compensation coefficient, the extended Kalman filter method with dynamic parameter correction, and the BP neural network algorithm.

[0089] Optionally, the real-time operating parameters include at least terminal voltage, charging / discharging current, battery temperature, and battery internal resistance.

[0090] The battery SOC estimation device provided in this embodiment of the invention can execute the battery SOC estimation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0091] Example 3

[0092] Figure 3 This is a schematic diagram of an electronic device provided in Embodiment 3 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0093] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0094] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0095] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as battery SOC estimation methods.

[0096] In some embodiments, the battery SOC estimation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the battery SOC estimation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the battery SOC estimation method by any other suitable means (e.g., by means of firmware).

[0097] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0098] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0099] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0100] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0101] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0102] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0103] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0104] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0105] This invention also provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implements the battery SOC estimation method as provided in any embodiment of this application.

[0106] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone 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 remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A battery SOC estimation method characterized by, The method comprises: acquiring real-time operation parameters of a battery to be measured; based on the real-time operation parameters, using at least two different battery state of charge (SOC) estimation algorithms to respectively calculate corresponding preliminary SOC values; based on the current working condition of the battery to be measured and the real-time estimation errors of each SOC estimation algorithm, determining adaptive weights of each SOC estimation algorithm; based on the preliminary SOC values and the adaptive weights of the SOC estimation algorithms corresponding to the preliminary SOC values, determining a target SOC value.

2. The method of claim 1, wherein, The method comprises: based on the current working condition of the battery to be measured and the real-time estimation errors of each SOC estimation algorithm, using a preset fuzzy control rule to determine the adaptive weights of each SOC estimation algorithm, wherein the sum of the adaptive weights is 1, and the preset fuzzy control rule comprises: assigning a first weight value to an SOC estimation algorithm whose real-time estimation error is less than or equal to a preset threshold and is determined to be adaptive to the current working condition; assigning a second weight value to an SOC estimation algorithm whose real-time estimation error is less than or equal to the preset threshold but is not adaptive to the current working condition; and assigning a third weight value to an SOC estimation algorithm whose real-time estimation error is greater than the preset threshold, wherein the first weight value is greater than the second weight value, and the second weight value is greater than the third weight value.

3. The method of claim 1, wherein, The real-time estimation error of each SOC estimation algorithm is determined based on the preliminary SOC values and a current reference SOC value, wherein the current reference SOC value is an SOC value determined by an open-circuit voltage method when the battery to be measured last met a static condition.

4. The method of claim 1, wherein, The method further comprises: when a preset condition is met, acquiring a calibration SOC value determined based on an open-circuit voltage method; calculating a deviation between the target SOC value and the calibration SOC value; if the absolute value of the deviation is greater than a preset calibration threshold, using the calibration SOC value to correct the target SOC value.

5. The method of claim 1, wherein, The at least two different SOC estimation algorithms comprise an ampere-hour integral method with a temperature compensation coefficient, an extended Kalman filter method with dynamic parameter correction, and a back propagation (BP) neural network algorithm.

6. The method of claim 1, wherein, The real-time operation parameters at least include terminal voltage, charging and discharging current, battery temperature, and battery internal resistance.

7. A battery SOC estimation device characterized by comprising: The method comprises: a parameter acquisition module configured to acquire real-time operation parameters of a battery to be measured; a preliminary value calculation module configured to, based on the real-time operation parameters, use at least two different battery state of charge (SOC) estimation algorithms to respectively calculate corresponding preliminary SOC values; a weight determination module configured to, based on the current working condition of the battery to be measured and the real-time estimation errors of each SOC estimation algorithm, determine adaptive weights of each SOC estimation algorithm; a target value calculation module configured to, based on the preliminary SOC values and the adaptive weights of the SOC estimation algorithms corresponding to the preliminary SOC values, determine a target SOC value.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; and The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the battery SOC estimation method in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the battery SOC estimation method in any one of claims 1-6 when executed.

10. A computer program product, characterised in that, The computer program product comprises a computer program which, when executed by the processor, implements the battery SOC estimation method in any one of claims 1-6.

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