OCV-soc curve self-learning method and device, electronic equipment and medium

Abstract

The present application relates to the technical field of battery, and provides an OCV-SOC curve self-learning method and device, electronic equipment and medium, the method comprises: determining the first health state, SOC standard trigger value and SOC real-time trigger value of the battery based on actual operation data and cell test standard data; in response to the error between the first health state and the second health state of the battery obtained and the error between the SOC standard trigger value and the SOC real-time trigger value satisfying the preset update condition, obtaining the corrected OCV-SOC curve based on the actual operation data and the SOC real-time value corresponding to different time; updating the original OCV-SOC curve based on the corrected OCV-SOC curve. Through the closed-loop process of "accurate determination of basic parameters - screening of reliable scenarios and curve correction - real-time updating of curve", the SOC estimation accuracy of Kalman filtering algorithm under different working conditions is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and more specifically, to an OCV-SOC curve self-learning method, apparatus, electronic device, and medium. Background Technology

[0002] In fields such as new energy vehicles and energy storage systems, solid-state batteries have become one of the core directions of current battery technology research and application due to their advantages such as high energy density, superior safety, and long cycle life. State of Charge (SOC), as a key parameter characterizing the remaining capacity of a solid-state battery, directly determines the accuracy of range prediction, the safety of charge and discharge control, and the effectiveness of battery life protection during battery use. High-precision SOC estimation ensures a high degree of match between the displayed range of new energy vehicles and the actual driving range, avoiding inconvenience caused by misjudgments of range. It also provides reliable status data for the Battery Management System (BMS), preventing performance degradation or safety risks caused by overcharging or over-discharging, and plays a crucial supporting role in the large-scale application of solid-state batteries.

[0003] In related technologies, solid-state battery BMS often uses the OCV-SOC curve (the relationship between open circuit voltage (OCV) and state of charge) generated by ideal static testing in the laboratory at the time of cell delivery, and employs the Kalman filter algorithm for SOC estimation. However, the OCV model of solid-state batteries exhibits hysteresis characteristics, and these characteristics fluctuate with changes in operating conditions, leading to a deviation between the OCV-SOC curve used in the model and the actual OCV-SOC relationship of the battery. This deviation causes the SOC estimation by the Kalman filter algorithm to deviate from the true value, making it difficult to guarantee the accuracy of SOC estimation. Summary of the Invention

[0004] The problem addressed by this invention is how to improve the accuracy of SOC estimation.

[0005] To address the above problems, this invention provides an OCV-SOC curve self-learning method, apparatus, electronic device, and medium.

[0006] In a first aspect, the present invention provides an OCV-SOC curve self-learning method, comprising:

[0007] Based on actual operating data and cell testing standard data, the first health state of the battery, the standard SOC trigger value, and the real-time SOC trigger value are determined. The standard SOC trigger value is the standard SOC value at the preset standard state trigger time, and the real-time SOC trigger value is the real-time SOC value at the preset standard state trigger time.

[0008] In response to the error between the first health state and the acquired second health state of the battery and the error between the SOC standard trigger value and the SOC real-time trigger value satisfying the preset update conditions, a corrected OCV-SOC curve is obtained based on the actual operating data and the SOC real-time values ​​corresponding to different times. The second health state is used to characterize the true health state of the battery.

[0009] The original OCV-SOC curve is updated based on the modified OCV-SOC curve.

[0010] Optionally, the actual operating data includes calendar life, and the cell testing standard data includes standard cycle life and standard OCV-SOC curve; determining the battery's first state of health, standard SOC trigger value, and real-time SOC trigger value based on the actual operating data and cell testing standard data includes:

[0011] The initial healthy life of the battery is determined based on the standard cycle life and the calendar life, and the initial healthy life is corrected by the capacity range method to obtain the first healthy state.

[0012] The SOC standard value of the battery at the preset standard state trigger time is retrieved according to the standard OCV-SOC curve to obtain the SOC standard trigger value;

[0013] Based on the first health state, the SOC real-time value is obtained using the ampere-hour integration method, and the SOC real-time trigger value is obtained by retrieving the SOC real-time value at the preset standard state trigger time.

[0014] Optionally, after determining the battery's first state of health, SOC standard trigger value, and SOC real-time trigger value based on actual operating data and cell testing standard data, the method further includes:

[0015] An online parameter identification algorithm is used to identify and process the actual operating data to obtain the real-time OCV value;

[0016] Based on the real-time OCV value and the standard OCV-SOC curve, the SOC estimate is obtained using the Kalman filter algorithm.

[0017] Optionally, the preset update conditions include:

[0018] The error between the first health state and the second health state is less than a preset health error;

[0019] The error between the SOC standard trigger value and the SOC real-time trigger value is less than the preset SOC accuracy error;

[0020] The error between the estimated SOC value and the corresponding real-time SOC value is greater than the preset correction accuracy error.

[0021] Optionally, the step of retrieving the SOC standard value of the battery at the preset standard state trigger time based on the standard OCV-SOC curve to obtain the SOC standard trigger value includes:

[0022] After the battery is detected to meet the resting conditions, the OCV resting value corresponding to the resting time is obtained, and the SOC value corresponding to the OCV resting value in the standard OCV-SOC curve is retrieved to obtain the SOC resting standard value.

[0023] Obtain the OCV full charge value corresponding to at least one full charge time before the resting time, and retrieve the SOC value corresponding to the OCV full charge value in the standard OCV-SOC curve to obtain the standard SOC full charge value.

[0024] The process of retrieving the real-time SOC value at the preset standard state trigger time to obtain the real-time SOC trigger value includes:

[0025] The SOC real-time value at rest is obtained by retrieving the corresponding SOC real-time value at the resting time, and the SOC real-time value at full charge is obtained by retrieving the corresponding SOC real-time value at the full charge time.

[0026] Optionally, the process of obtaining the corrected OCV-SOC curve based on the actual operating data and the real-time SOC values ​​at different times includes:

[0027] The actual operating data is identified and processed using an online parameter identification algorithm to obtain the real-time OCV value;

[0028] The real-time values ​​of OCV and SOC at multiple different times are fitted to generate the modified OCV-SOC curve.

[0029] Optionally, after updating the original OCV-SOC curve based on the modified OCV-SOC curve, the method further includes:

[0030] After running the modified OCV-SOC curve for a preset stabilization time, return to the steps of determining the first health state, the SOC standard value at the preset standard state trigger time, and the real-time SOC trigger value at the preset standard state trigger time based on actual operating data and cell testing standards.

[0031] Secondly, the present invention provides an OCV-SOC curve self-learning device, comprising:

[0032] The acquisition module is used to determine the first health state of the battery, the standard SOC trigger value, and the real-time SOC trigger value based on actual operating data and cell test standard data. The standard SOC trigger value is the standard SOC value at the preset standard state trigger time, and the real-time SOC trigger value is the real-time SOC value at the preset standard state trigger time.

[0033] The judgment module is used to respond to the error between the first health state and the acquired second health state of the battery and the error between the SOC standard trigger value and the SOC real-time trigger value satisfying the preset update conditions, and to obtain the corrected OCV-SOC curve based on the actual operating data and the SOC real-time value corresponding to different times.

[0034] The update module is used to update the original OCV-SOC curve based on the modified OCV-SOC curve.

[0035] Thirdly, the present invention provides an electronic device, including a memory and a processor;

[0036] The memory is used to store computer programs;

[0037] The processor is configured to implement the OCV-SOC curve self-learning method as described in the first aspect when executing the computer program.

[0038] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the OCV-SOC curve self-learning method as described in the first aspect.

[0039] The beneficial effects of the OCV-SOC curve self-learning method of the present invention are as follows: Based on actual operating data and cell testing standards, the first health state (first SOH), the standard SOC value at the preset standard state trigger time, and the real-time SOC trigger value are determined. This breaks the limitation of relying solely on laboratory static test data in the traditional method, and incorporates the dynamic operating condition data of the battery in actual use into the basic parameter determination process. This allows the first SOH to reflect the actual aging state of the battery, rather than remaining at the theoretical value of the factory static test. Furthermore, by clearly defining the standard SOC trigger value and the real-time trigger value under the preset standard state (such as the fully discharged state), a "benchmark-real-time" comparison basis is provided for subsequent accuracy verification, avoiding subsequent curve correction deviations caused by inaccurate basic parameters, and ensuring the reliability of the basic data for OCV-SOC curve optimization from the source. By detecting the errors between the first State of Health (SOH) and the second State of Health (SOH), as well as the errors between the standard trigger value and the real-time trigger value of State of Charge (SOC), curve correction is only initiated when the basic parameters (SOH, SOC) meet the preset update conditions. This allows for the selection of reliable correction scenarios, avoiding invalid corrections caused by SOH deviations or inaccurate SOC benchmarks, and ensuring the targeted and accurate nature of the correction process. Simultaneously, based on actual operating data and real-time SOC values ​​at different times, a corrected OCV-SOC curve is obtained. This curve fully integrates the OCV characteristics of the battery under actual operating conditions such as dynamic charge and discharge currents, complex ambient temperatures, and cyclic aging. It can also adapt to individual differences in different batteries due to manufacturing processes and usage habits, providing accurate curve basis for subsequent SOC estimation. By correcting the OCV-SOC curve and updating the original curve, the OCV-SOC curve adapted to the actual operating conditions is directly applied to the Kalman filter SOC estimation model of the solid-state battery BMS. Compared with the traditional method of using the factory static curve, the updated corrected OCV-SOC curve can match the current electrochemical state of the battery in real time, eliminate the deviation between the original curve and the actual OCV hysteresis characteristics, improve the SOC estimation accuracy of the Kalman filter algorithm from the core input parameter level, avoid the deviation of the estimation result caused by the curve deviation, and provide accurate SOC data support for the BMS charge and discharge control strategy.

[0040] This invention, through a closed-loop process of "accurate determination of basic parameters - screening of reliable scenarios and curve correction - real-time curve update," comprehensively solves the problem of mismatch between the OCV-SOC curve and actual working conditions and individual differences in the prior art, significantly improving the SOC estimation accuracy of the Kalman filter algorithm. At the same time, the BMS charge and discharge control strategy based on accurate SOC data can avoid overcharging and discharging or improper power control, slow down the rate of battery performance degradation, reduce safety hazards, and ultimately realize the full performance and long-term reliability guarantee of solid-state batteries in practical applications, providing key technical support for the large-scale application of solid-state batteries. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the OCV-SOC curve self-learning method according to an embodiment of the present invention. Figure 1 ;

[0042] Figure 2 This is a flowchart illustrating the OCV-SOC curve self-learning method according to an embodiment of the present invention. Figure 2 ;

[0043] Figure 3 This is a schematic diagram of the structure of the OCV-SOC curve self-learning device according to an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the invention. Detailed Implementation

[0045] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0046] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0047] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0048] It should be noted that the terms "one" and "more" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0049] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0050] Among existing methods for estimating the State of Charge (SOC) of solid-state batteries, the Kalman filter algorithm is widely used in the SOC calculation model of battery management systems (BMS) due to its excellent dynamic error correction capability. The accuracy of this algorithm depends on the accurate modeling of the battery's electrochemical characteristics. The correspondence between the Open Voltage Volume (OCV) and the State of Charge (SOC), i.e., the OCV-SOC curve, is one of the core input parameters, and its accuracy directly affects the SOC estimation result of the Kalman filter algorithm. However, the OCV model of solid-state batteries exhibits significant hysteresis. This hysteresis is not constant but closely related to the actual operating conditions of the battery. For example, under different charge and discharge current intensities, differences in the ion migration rate and concentration distribution inside the battery lead to changes in the degree of OCV hysteresis. Under different ambient temperature conditions, such as high and low temperatures, changes in electrolyte conductivity and electrode reactivity further exacerbate the fluctuations in the OCV hysteresis state. Simultaneously, as the number of charge and discharge cycles increases, the battery's State of Health (SOH) is affected, leading to the evolution of the electrode material structure and the decay of active materials, which also causes continuous changes in the OCV hysteresis characteristics.

[0051] Currently, the OCV-SOC curves used in Kalman filter cell models are mostly generated based on test data from ideal static conditions in the laboratory during the cell's manufacturing phase. These curves only reflect the correspondence between OCV and SOC under standard test conditions. However, due to significant differences between real-world battery operating conditions such as dynamic charge / discharge current, complex ambient temperatures, and cycle aging, and laboratory test conditions, as well as individual variations in OCV hysteresis characteristics caused by subtle differences in manufacturing processes and usage habits, the OCV-SOC curve used in each battery's Kalman filter cell model deviates from the true correspondence between OCV and SOC during actual operation. This deviation prevents the Kalman filter algorithm from accurately matching the battery's actual electrochemical state during SOC estimation, leading to a shift in the SOC estimation results. This not only affects the reliability of range predictions but may also cause the BMS to execute charge / discharge control strategies based on inaccurate SOC information, increasing the rate of battery performance degradation and even posing safety hazards, thus hindering the performance and reliability of solid-state batteries in practical applications.

[0052] To address the problems existing in the aforementioned related technologies, this embodiment provides an OCV-SOC curve self-learning method, apparatus, electronic device, and medium.

[0053] like Figure 1As shown, an OCV-SOC curve self-learning method provided in this embodiment of the invention includes:

[0054] Step S1: Determine the first health state of the battery, the standard SOC trigger value, and the real-time SOC trigger value based on actual operating data and cell test standard data. The standard SOC trigger value is the standard SOC value at the preset standard state trigger time, and the real-time SOC trigger value is the real-time SOC value at the preset standard state trigger time.

[0055] Specifically, actual operating data may include historical charge / discharge current curves, voltage curves, temperature curves, and cycle count records of the battery. Cell testing standards may include the cell's nominal capacity at the factory, standard cycle life, and the corresponding standard OCV-SOC curve of the battery. The preset standard state is a state where the battery's electrochemical characteristics are stable, and the correspondence between SOC and key parameters (such as voltage and capacity) is clear and reliable. It provides a credible reference for SOC benchmark verification and OCV-SOC curve correction. The preset standard state can be a fully charged state, a fully discharged state, etc. The SOC standard trigger value is the SOC benchmark determined based on the cell testing standards under the preset standard state, obtained from the standard OCV-SOC curve. The SOC real-time trigger value is the real-time SOC calculated when the preset standard state is triggered, obtained from the real-time SOC value, which can be calculated using the time-integration method based on actual operating data.

[0056] Step S2: In response to the error between the first health state and the acquired second health state of the battery, and the error between the SOC standard trigger value and the SOC real-time trigger value satisfying the preset update conditions, a corrected OCV-SOC curve is obtained based on the actual operating data and the SOC real-time values ​​corresponding to different times. The second health state is used to characterize the true health state of the battery.

[0057] Specifically, the error between the first and second health states is used to characterize the consistency and accuracy of the battery aging benchmarks using two different algorithms. The first health state is obtained based on actual operating data and cell testing standard data, using, for example, a capacity range. The second health state can be calculated based on the dvdq curve of real-time operating data under slow charging conditions. The higher the error between the two, the more accurately the calculated first health state reflects the current aging level of the battery, thus improving the reliability of subsequent real-time SOC value calculations. The error between the SOC standard value and the real-time SOC trigger value is used to characterize the reliability of the real-time SOC value. The higher the error, the more reliable the real-time SOC value. Subsequent data pairs constructed using this real-time SOC value and the measured OCV can accurately reflect the actual OCV-SOC correspondence of the battery.

[0058] The preset update conditions corresponding to dual errors are set to determine the accuracy of the real-time SOC value from the dual perspectives of SOH accuracy and SOC accuracy under preset standard conditions. That is, when the errors of SOH and SOC both meet the preset update conditions, it can be considered that the accuracy of the real-time SOC value at this time meets the replacement standard. Then, a corrected OCV-SOC curve for updating is generated based on the actual operating data and the real-time SOC value at the corresponding time. The preset update conditions can be set based on the battery accuracy requirements. For example, if the difference between the first health state and the second health state meets the preset update conditions (e.g., the difference is less than 1%), and the difference between the SOC standard value and the SOC real-time trigger value also meets the preset update conditions, then the accuracy of the SOC real-time value is considered to meet the update requirements. In this case, a corrected OCV-SOC curve will be generated based on the actual operating data and the SOC real-time values ​​at different times, which will be used to replace the original OCV-SOC curve in the subsequent Kalman filter algorithm. Conversely, if the difference between the first health state and the second health state does not meet the preset update conditions, and the difference between the SOC standard value and the SOC real-time trigger value also does not meet the preset update conditions, then the SOC real-time value is considered to have a large deviation and does not meet the update requirements. In this case, the original OCV-SOC curve in the Kalman filter algorithm will still be used for SOC estimation.

[0059] Step S3: Update the original OCV-SOC curve based on the modified OCV-SOC curve.

[0060] Specifically, the corrected OCV-SOC curve is stored in a local storage module in response to the algorithm's SOC estimation. During SOC estimation using the Kalman filter algorithm, the corrected OCV-SOC curve stored in the storage module is used to replace the original OCV-SOC curve as input parameters for the Kalman filter algorithm. Simultaneously, the corrected OCV-SOC curve is periodically verified. When a significant change in the battery's operating conditions is detected, or after a certain number of cycles, steps S1 to S2 are retried to update the corrected OCV-SOC curve. It should be noted that the original OCV-SOC curve can be a standard OCV-SOC curve based on cell testing, or it can be the previously updated corrected OCV-SOC curve.

[0061] In one specific embodiment, it is applied to a vehicle-to-cloud interactive system, such as Figure 2As shown, the vehicle-side determines the first health state, the SOC standard value at the preset standard state trigger time, and the real-time SOC trigger value at the preset standard state trigger time based on actual operating data and cell testing standards, and then sends these values ​​to the cloud. The cloud can obtain the second health state based on the dvdq curve characteristics under slow charging conditions, and determine whether the error between the first and second health states, as well as the error between the SOC standard value and the real-time SOC trigger value, meets the preset update conditions. If yes, a corrected OCV-SOC curve is generated based on the vehicle-side's real-time operating data and the real-time SOC value calculated by the vehicle-side, and the corrected OCV-SOC curve is fed back to the vehicle-side. If not, the process returns to the step of obtaining vehicle-side information for real-time judgment. The vehicle-side responds to the corrected OCV-SOC curve fed back from the cloud, stores the corrected OCV-SOC curve, and replaces the original OCV-SOC curve of the Kalman filter algorithm in the vehicle-side with the corrected OCV-SOC curve. In the next time period, the Kalman filter algorithm estimates the SOC based on the corrected OCV-SOC curve.

[0062] In this embodiment, based on actual operating data and cell testing standards, the first state of health (first SOH), the standard SOC value at the preset standard state trigger time, and the real-time SOC trigger value are determined. This breaks through the limitations of traditional methods that rely solely on static laboratory test data. It incorporates dynamic operating data from actual battery use into the basic parameter determination process, enabling the first SOH to reflect the battery's true aging state rather than remaining at the theoretical value from factory static testing. Furthermore, by clearly defining the standard SOC trigger value and the real-time trigger value under the preset standard state (such as the fully discharged state), a "benchmark-real-time" comparison basis is provided for subsequent accuracy verification. This avoids deviations in subsequent curve corrections caused by inaccurate basic parameters, ensuring the reliability of the basic data for OCV-SOC curve optimization from the source. By detecting the errors between the first State of Health (SOH) and the second State of Health (SOH), as well as the errors between the standard trigger value and the real-time trigger value of State of Charge (SOC), curve correction is only initiated when the basic parameters (SOH, SOC) meet the preset update conditions. This allows for the selection of reliable correction scenarios, avoiding invalid corrections caused by SOH deviations or inaccurate SOC benchmarks, and ensuring the targeted and accurate nature of the correction process. Simultaneously, based on actual operating data and real-time SOC values ​​at different times, a corrected OCV-SOC curve is obtained. This curve fully integrates the OCV characteristics of the battery under actual operating conditions such as dynamic charge and discharge currents, complex ambient temperatures, and cyclic aging. It can also adapt to individual differences in different batteries due to manufacturing processes and usage habits, providing accurate curve basis for subsequent SOC estimation. By correcting the OCV-SOC curve and updating the original curve, the OCV-SOC curve adapted to the actual operating conditions is directly applied to the Kalman filter SOC estimation model of the solid-state battery BMS. Compared with the traditional method of using the factory static curve, the updated corrected OCV-SOC curve can match the current electrochemical state of the battery in real time, eliminate the deviation between the original curve and the actual OCV hysteresis characteristics, improve the SOC estimation accuracy of the Kalman filter algorithm from the core input parameter level, avoid the deviation of the estimation result caused by the curve deviation, and provide accurate SOC data support for the BMS charge and discharge control strategy.

[0063] This invention, through a closed-loop process of "accurate determination of basic parameters - screening of reliable scenarios and curve correction - real-time curve update," comprehensively solves the problem of mismatch between the OCV-SOC curve and actual working conditions and individual differences in the prior art, significantly improving the SOC estimation accuracy of the Kalman filter algorithm. At the same time, the BMS charge and discharge control strategy based on accurate SOC data can avoid overcharging and discharging or improper power control, slow down the rate of battery performance degradation, reduce safety hazards, and ultimately realize the full performance and long-term reliability guarantee of solid-state batteries in practical applications, providing key technical support for the large-scale application of solid-state batteries.

[0064] Optionally, the actual operating data includes calendar life, and the cell testing standards include standard cycle life and standard OCV-SOC curves; determining the battery's first state of health, standard SOC trigger value, and real-time SOC trigger value based on the actual operating data and cell testing standard data includes:

[0065] The initial healthy lifespan of the battery is determined based on the standard cycle life and the calendar lifespan, and the initial healthy lifespan is corrected using the capacity range method to obtain the first healthy state.

[0066] Specifically, standard cycle life refers to the number of cycles a battery undergoes under rated charge and discharge conditions until its capacity decays to 80% of its initial capacity, as specified in the cell testing standard. For example, the standard cycle life of a ternary solid-state battery is 1000 cycles. Calendar life refers to the actual idle time of the battery during use and the corresponding storage conditions. Initial health life is an estimated value of the battery's health under ideal conditions, calculated solely based on the combined calculation of cycle aging and calendar aging.

[0067] In one embodiment, the cumulative charge-discharge cycle count is first extracted, and calendar life-related data are statistically analyzed. Secondly, based on the standard cycle life and the health status characterization relationship, the initial healthy life of the battery is calculated. Finally, a capacity range method is used for correction. This involves obtaining the current usable capacity through a complete charge-discharge test (e.g., initial capacity 100Ah, current usable capacity 82Ah, accounting for 82%), and applying the correction coefficient for the corresponding range to dynamically update the first state of health (SOH). This more accurately captures the capacity decay characteristics of the battery in actual use and the deviation between standard cell test data and actual operating condition data.

[0068] The SOC standard value of the battery at the preset standard state trigger time is retrieved based on the standard OCV-SOC curve to obtain the SOC standard trigger value.

[0069] Specifically, the standard OCV-SOC curve data provided in the cell testing standard is read. This data must include the standard open-circuit voltage (OCV) values ​​corresponding to SOC from 0% to 100% (in 1% or 0.5% increments), and the data must be calibrated to ensure the accuracy of the curve. A preset standard state trigger time is defined. This time must meet the condition that the cell is in a stable open-circuit state. Specific trigger conditions may include: cell stop charging / discharging time ≥ preset resting time, current voltage fluctuation ≤ preset voltage fluctuation threshold, and temperature within a preset normal temperature range. The vehicle-side BMS monitors the cell's charging / discharging status, voltage fluctuation, and temperature parameters in real time. When all trigger conditions are simultaneously met, the current time is determined to be the preset standard state trigger time, and the current OCV at that time is recorded. After identifying the preset standard state trigger time, the BMS calls the standard OCV-SOC curve to obtain the SOC standard trigger value corresponding to the current OCV in the standard OCV-SOC curve.

[0070] Based on the first health state, the SOC real-time value is obtained using the ampere-hour integration method, and the SOC real-time trigger value is obtained by retrieving the SOC real-time value at the preset standard state trigger time.

[0071] Specifically, the ampere-hour integration algorithm is expressed as: SOC_Ah = SOC_ini + ΔQ / Cap_SOH, where SOC_Ah represents the real-time SOC value obtained using the ampere-hour integration method, SOC_ini represents the initial SOC value, i.e., the SOC value at the last charging cutoff, or the SOC value calibrated using the standard OCV-SOC curve, and Cap_SOH represents the first health state. Based on the above ampere-hour integration algorithm, real-time SOC values ​​at different times are obtained. When the BMS identifies the preset standard state trigger time, it records the timestamp of that time. The real-time SOC trigger value is obtained by retrieving the real-time SOC value corresponding to that timestamp from the real-time SOC values.

[0072] Optionally, after determining the battery's first state of health, SOC standard trigger value, and SOC real-time trigger value based on actual operating data and cell testing standard data, the method further includes:

[0073] An online parameter identification algorithm is used to identify and process the actual operating data to obtain the real-time OCV value;

[0074] Based on the real-time OCV value and the standard OCV-SOC curve, the SOC estimate is obtained using the Kalman filter algorithm.

[0075] Specifically, an online parameter identification algorithm is used to extract the real-time OCV value of the battery from the actual operating data, which may also include parameters such as the battery's internal resistance and capacitance. The real-time OCV value and / or internal resistance, capacitance, and standard OCV-SOC curve are used as inputs to the Kalman filter algorithm to obtain the SOC estimate. At this time, the SOC estimate represents the real-time SOC estimate under dynamic operating conditions based on the standard OCV-SOC curve or the previous OCV-SOC curve of the cell test. In subsequent steps, it can be compared with the real-time SOC value to determine whether the standard OCV-SOC curve or the previous OCV-SOC curve used at this time is invalid, thereby triggering cloud self-learning optimization to ensure that the SOC estimation accuracy of the Kalman filter algorithm always meets the requirements.

[0076] Optionally, the preset update conditions include:

[0077] The error between the first health state and the second health state is less than the preset health error.

[0078] Specifically, the preset health error is a quantitative standard for judging the consistency between the first SOH and the second SOH. It is a numerical threshold used to measure the acceptable range of deviation between the first SOH and the second SOH. The value of the preset health error needs to be determined in combination with the accuracy requirements of SOH calculation and the feasibility of engineering implementation, and is usually set to 1%. When the error between the first SOH and the second SOH is less than the preset health error, it means that the two algorithms are consistent in judging the degree of battery aging, the relevant parameters calculated based on the first SOH are reliable, and there is no deviation in the basic data for subsequent real-time SOC value calculation and error judgment. Conversely, it means that there is a large deviation between the two algorithms in judging the degree of battery aging. It is necessary to first investigate the cause of the SOH calculation deviation, such as data loss, abnormal model parameters, etc. Under the premise of incorrect SOH judgment, meaningless curve correction will lead to further deterioration of SOC estimation accuracy.

[0079] The error between the SOC standard trigger value and the SOC real-time trigger value is less than the preset SOC accuracy error.

[0080] Specifically, the preset SOC accuracy error is a quantitative standard for judging the consistency between the real-time SOC trigger value and the standard SOC trigger value. It is used to measure the acceptable range of deviation between the dynamically calculated real-time SOC trigger value and the high-precision reference standard SOC trigger value. The value of the preset SOC accuracy error needs to be determined by combining the accuracy upper limit of the standard SOC trigger value and the engineering error range of the real-time SOC trigger value. It is usually set to 1%, which can ensure that the deviation between the real-time SOC trigger value and the high-precision benchmark is within the acceptable engineering range and does not affect the user's range prediction and charge / discharge control. It can also verify that the current SOC estimate has no significant cumulative error, and subsequent error judgments based on the real-time SOC value have reference value. When the error between the standard SOC trigger value and the real-time SOC trigger value is less than the preset SOC accuracy error, it indicates that the real-time SOC value has no significant cumulative error and can be used as a benchmark for judging the deviation of subsequent SOC estimates. That is, if the deviation between the subsequent SOC estimate and the real-time SOC value is too large, it can be determined that it is caused by the deviation of the OCV-SOC curve in the Kalman filter algorithm. Conversely, it indicates that there is a large deviation in the real-time SOC value at this time, and the real-time SOC trigger value needs to be calibrated first to avoid misjudging the error of the real-time SOC value as the deviation caused by the original OCV-SOC curve, which would lead to the wrong correction direction.

[0081] The error between the estimated SOC value and the corresponding real-time SOC value is greater than the preset correction accuracy error.

[0082] Specifically, the preset correction accuracy error is a quantitative standard for judging the consistency between the SOC estimate and the real-time SOC value. It is used to measure the acceptable range of deviation between the SOC estimate and the real-time SOC value. The value of the preset correction accuracy error needs to be determined in combination with the actual needs of SOC estimation and the impact threshold of OCV deviation. The accuracy requirement of electric vehicle BMS for SOC estimation is usually ±3%. When the estimation deviation exceeds 3%, it will lead to a large deviation in range prediction, affecting user experience, or causing overcharging and over-discharging risks. Moreover, when the deviation of OCV-SOC curve causes the error between the SOC estimate and the real-time SOC value to be >3%, it means that the initial OCV-SOC curve can no longer compensate for the deviation through filtering algorithms. That is, Kalman filtering can only correct small-range noise and cannot offset the systematic deviation of the curve itself. It is necessary to start curve self-learning optimization. Therefore, the preset correction accuracy error is usually set to 3%. When the error between the estimated SOC and the real-time SOC is greater than the preset correction accuracy error, combined with the aforementioned SOH accuracy determination and SOC accuracy determination results, it can be determined that the deviation is caused by the mismatch between the original OCV-SOC curve and the actual characteristics of the battery. Self-learning needs to be started to record the real-time OCV value and the corresponding real-time SOC value at the vehicle end, and the original OCV-SOC curve is updated after fitting the corrected OCV-SOC curve. Conversely, it indicates that the current original OCV-SOC curve and filtering parameters can meet the SOC estimation accuracy requirements, and there is no need to start cloud curve fitting and distribution, thus reducing unnecessary resource consumption.

[0083] Optionally, the preset standard state trigger time includes a resting time and a fully charged time;

[0084] The step of retrieving the SOC standard value of the battery at the preset standard state trigger time based on the standard OCV-SOC curve to obtain the SOC standard trigger value includes:

[0085] After the battery is detected to meet the resting conditions, the OCV resting value corresponding to the resting time is obtained, and the SOC value corresponding to the OCV resting value in the standard OCV-SOC curve is retrieved to obtain the SOC resting standard value.

[0086] Obtain the OCV full charge value corresponding to at least one full charge time before the resting time, and retrieve the SOC value corresponding to the OCV full charge value in the standard OCV-SOC curve to obtain the SOC full charge standard value.

[0087] The process of retrieving the real-time SOC value at the preset standard state trigger time to obtain the real-time SOC trigger value includes:

[0088] The SOC real-time value at rest is obtained by retrieving the corresponding SOC real-time value at the resting time, and the SOC real-time value at full charge is obtained by retrieving the corresponding SOC real-time value at the full charge time.

[0089] Specifically, the battery charging and discharging current is monitored in real time by the BMS. When the absolute value of the current continuously reaches a preset static current threshold and the duration reaches a preset resting time, such as 30 minutes, the battery is deemed to meet the resting conditions, and this current moment is recorded as the resting time. During the resting time, the battery terminal voltage value is collected by a voltage sensor as the OCV resting value. The standard OCV-SOC curve is retrieved, and the SOC value in the standard OCV-SOC curve is the SOC standard value. The SOC standard value corresponding to the OCV resting value is found in the standard OCV-SOC curve, which is the SOC resting standard value. The method for obtaining the SOC full charge standard value is the same as the method for obtaining the SOC resting standard value, and will not be described again here.

[0090] After obtaining the SOC static standard value and the SOC full charge standard value, it is necessary to obtain the corresponding real-time SOC value. The vehicle-side BMS obtains the real-time SOC value based on the ampere-hour integration method. After determining the static time, the real-time SOC value corresponding to the static time is retrieved by timestamp matching and recorded as the SOC static real-time value. The method for obtaining the SOC full charge real-time value is the same as the method for obtaining the SOC static real-time value, and will not be described in detail here.

[0091] When performing accuracy judgment, the error between the static standard value of SOC and the real-time static value of SOC, as well as the error between the full charge standard value of SOC and the real-time full charge value of SOC, must be less than the preset SOC accuracy error to meet the preset update conditions.

[0092] In this embodiment, the static state and at least one fully charged state before the static state are used as preset standard states. The SOC standard value and SOC real-time value are determined with dual precision in the static state and the SOC standard value and SOC real-time value in the fully charged state, thereby improving the reliability of the SOC real-time value.

[0093] Optionally, the process of obtaining the corrected OCV-SOC curve based on the actual operating data and the real-time SOC values ​​at different times includes:

[0094] The actual operating data is identified and processed using an online parameter identification algorithm to obtain the real-time OCV value;

[0095] The real-time values ​​of OCV and SOC at multiple different times are fitted to generate the modified OCV-SOC curve.

[0096] Specifically, after obtaining the real-time OCV value, the real-time OCV value and the real-time SOC value are associated based on the timestamp corresponding to the time information. Based on the time information and the time order, a piecewise polynomial fitting algorithm is used to fit the discrete data pairs to obtain the corrected OCV-SOC curve.

[0097] In this embodiment, the actual operating data is processed by an online parameter identification algorithm to accurately extract the real-time OCV value that reflects the true state of the battery. Then, based on the timestamp of the time information, the real-time OCV value and the real-time SOC value are accurately correlated and matched to generate a modified OCV-SOC curve. The online identification ensures the dynamic accuracy of the real-time OCV value, and the correlation of time information eliminates data misalignment errors. Finally, the modified OCV-SOC curve generated by the fitting can fit the actual changes such as battery aging and operating condition differences in real time, providing a high-precision mapping basis for subsequent SOC estimation, and effectively improving the reliability and adaptability of battery state estimation.

[0098] Optionally, after updating the original OCV-SOC curve based on the modified OCV-SOC curve, the method further includes:

[0099] After running the modified OCV-SOC curve for a preset stabilization time, return to the steps of determining the first health state, the SOC standard value at the preset standard state trigger time, and the real-time SOC trigger value at the preset standard state trigger time based on actual operating data and cell testing standards.

[0100] Specifically, the preset stabilization time refers to a fixed period of time set by the BMS after completing the update from the original OCV-SOC curve to the modified OCV-SOC curve, allowing the modified curve to run fully, verify, and collect stable data. This avoids interference from short-term special operating conditions and ensures that the subsequent re-evaluation process is based on stable and reliable operating data, rather than accidental instantaneous states. It can be set to one week.

[0101] In one embodiment, after completing the curve update, the BMS records the timestamp of the update completion moment and starts the timing module. During this period, the BMS strictly performs functions such as SOC estimation and charge / discharge control based on the corrected OCV-SOC curve. The timing module monitors the running time in real time, and triggers a process jump signal when the accumulated running time reaches the preset stabilization time. After the preset stabilization time ends, the BMS automatically calls the data acquisition module to re-acquire the battery's actual operating data. Based on the newly acquired operating data, it recalculates the first SOH at the current time, obtains new SOC standard trigger values ​​and real-time SOC trigger values ​​by monitoring the resting and fully charged states, and resets the relevant status flags, bringing the entire process back to the initial evaluation stage, forming a closed-loop iterative mechanism.

[0102] By way of example, the OCV-SOC curve self-learning method will now be further described with reference to a specific embodiment. The OCV-SOC curve self-learning method includes the following steps:

[0103] (1) The initial healthy life of the battery is determined based on the standard cycle life and calendar life, and the first healthy state is obtained by correcting the initial healthy life using the capacity range method.

[0104] (2) After the battery is found to meet the resting conditions, the OCV resting value corresponding to the resting time is obtained, and the SOC value corresponding to the OCV resting value in the standard OCV-SOC curve is retrieved to obtain the SOC resting standard value; the OCV full charge value corresponding to at least one full charge time before the resting time is obtained, and the SOC value corresponding to the OCV full charge value in the standard OCV-SOC curve is retrieved to obtain the SOC full charge standard value.

[0105] (3) The SOC real-time value at rest is obtained by retrieving the corresponding SOC real-time value at rest time, and the SOC real-time value at full charge is obtained by retrieving the corresponding SOC real-time value at full charge time.

[0106] (4) An online parameter identification algorithm is used to identify and process the actual operating data to obtain the real-time OCV value. Based on the real-time OCV value and the standard OCV-SOC curve, the SOC estimate is obtained using the Kalman filter algorithm.

[0107] (5) Determine whether the preset update conditions are met. The preset update conditions include:

[0108] a. |First Health State - Second Health State| < Preset Health Error;

[0109] b. |SOC static standard value - SOC static real-time value| < preset SOC accuracy error; |SOC full charge standard value - SOC full charge real-time value| < preset SOC accuracy error;

[0110] c. |SOC estimated value - SOC real-time value| > preset correction accuracy error;

[0111] (6) The online parameter identification algorithm is used to identify and process the actual running data to obtain the real-time OCV value. According to the time information, the real-time OCV value and real-time SOC value at different times are fitted to generate the corrected OCV-SOC curve.

[0112] (7) If so, update the original OCV-SOC curve based on the modified OCV-SOC curve, and run the preset stabilization time according to the modified OCV-SOC curve, then return to step (1).

[0113] If not, proceed according to the original OCV-SOC curve and return to step (1).

[0114] Instance-wise, such as Figure 2As shown, this embodiment can also be applied to vehicle-to-cloud interaction systems. A specific embodiment will be used to further illustrate the OCV-SOC curve self-learning method, which includes the following steps:

[0115] (1) The vehicle determines the initial healthy life of the battery based on the standard cycle life and calendar life, and corrects the initial healthy life using the capacity range method to obtain the first healthy state and sends it to the cloud.

[0116] (2) After the vehicle detects that the battery meets the resting conditions, it obtains the OCV resting value corresponding to the resting time, retrieves the SOC value corresponding to the OCV resting value in the standard OCV-SOC curve to obtain the SOC resting standard value; it obtains the OCV full charge value corresponding to at least one full charge time before the resting time, retrieves the SOC value corresponding to the OCV full charge value in the standard OCV-SOC curve to obtain the SOC full charge standard value, and sends it to the cloud.

[0117] (3) The vehicle retrieves the corresponding SOC real-time value based on the stationary time to obtain the SOC stationary real-time value, and retrieves the corresponding SOC real-time value based on the full charging time to obtain the SOC full charging real-time value, and sends it to the cloud.

[0118] (4) The vehicle end adopts an online parameter identification algorithm to identify and process the actual operation data to obtain the real-time OCV value. Based on the real-time OCV value and the standard OCV-SOC curve, the SOC estimate is obtained by using the Kalman filter algorithm and sent to the cloud.

[0119] (5) The cloud determines whether the preset update conditions are met. The preset update conditions include:

[0120] a. |First Health State - Second Health State| < Preset Health Error;

[0121] b. |SOC static standard value - SOC static real-time value| < preset SOC accuracy error; |SOC full charge standard value - SOC full charge real-time value| < preset SOC accuracy error;

[0122] c. |SOC estimated value - SOC real-time value| > preset correction accuracy error;

[0123] (6) The cloud uses an online parameter identification algorithm to identify and process the actual running data to obtain the real-time OCV value. According to the time information, the real-time OCV value and real-time SOC value at different times are fitted to generate a corrected OCV-SOC curve and fed back to the Enter terminal.

[0124] (7) If so, the vehicle updates the original OCV-SOC curve based on the modified OCV-SOC curve, and after running for a preset stabilization time according to the modified OCV-SOC curve, returns to step (1).

[0125] If not, the vehicle will run according to the original OCV-SOC curve and return to step (1).

[0126] like Figure 3 As shown, an OCV-SOC curve self-learning device 300 provided in this embodiment of the invention includes:

[0127] The acquisition module 310 is used to determine the first health state of the battery, the standard SOC trigger value, and the real-time SOC trigger value based on actual operating data and cell test standard data. The standard SOC trigger value is the standard SOC value at the preset standard state trigger time, and the real-time SOC trigger value is the real-time SOC value at the preset standard state trigger time.

[0128] The judgment module 320 is used to obtain a corrected OCV-SOC curve based on the actual operating data and the real-time SOC values ​​corresponding to different times when the error between the first health state and the second health state of the battery and the error between the SOC standard trigger value and the real-time SOC trigger value meet the preset update conditions.

[0129] The update module 330 is used to update the original OCV-SOC curve based on the modified OCV-SOC curve.

[0130] Optionally, the acquisition module 310 is also used for:

[0131] The initial healthy life of the battery is determined based on the standard cycle life and the calendar life, and the initial healthy life is corrected by the capacity range method to obtain the first healthy state.

[0132] The SOC standard value of the battery at the preset standard state trigger time is retrieved according to the standard OCV-SOC curve to obtain the SOC standard trigger value;

[0133] Based on the first health state, the SOC real-time value is obtained using the ampere-hour integration method, and the SOC real-time trigger value is obtained by retrieving the SOC real-time value at the preset standard state trigger time.

[0134] Optionally, the acquisition module 310 is also used for:

[0135] After the battery is detected to meet the resting conditions, the OCV resting value corresponding to the resting time is obtained, and the SOC value corresponding to the OCV resting value in the standard OCV-SOC curve is retrieved to obtain the SOC resting standard value.

[0136] Obtain the OCV full charge value corresponding to at least one full charge time before the resting time, and retrieve the SOC value corresponding to the OCV full charge value in the standard OCV-SOC curve to obtain the standard SOC full charge value.

[0137] The SOC real-time value at rest is obtained by retrieving the corresponding SOC real-time value at the resting time, and the SOC real-time value at full charge is obtained by retrieving the corresponding SOC real-time value at the full charge time.

[0138] Optionally, the acquisition module 310 is also used for:

[0139] An online parameter identification algorithm is used to identify and process the actual operating data to obtain the real-time OCV value;

[0140] Based on the real-time OCV value and the standard OCV-SOC curve, the SOC estimate is obtained using the Kalman filter algorithm.

[0141] like Figure 4 As shown, an electronic device 400 provided in this embodiment of the invention includes a memory 410 and a processor 420; the memory 410 is used to store a computer program; the processor 420 is used to implement the OCV-SOC curve self-learning method as described above when the computer program is executed.

[0142] Alternatively, an electronic device 400 includes a memory 410 and a processor 420 coupled to the memory 410; the memory 410 is configured to store a computer program; and the processor 420 is configured to perform the following operations when the computer program is executed:

[0143] Based on actual operating data and cell testing standard data, the first health state of the battery, the standard SOC trigger value, and the real-time SOC trigger value are determined. The standard SOC trigger value is the standard SOC value at the preset standard state trigger time, and the real-time SOC trigger value is the real-time SOC value at the preset standard state trigger time.

[0144] In response to the error between the first health state and the acquired second health state of the battery and the error between the SOC standard trigger value and the SOC real-time trigger value satisfying the preset update conditions, the OCV-SOC curve is corrected based on the actual operating data and the SOC real-time value corresponding to different times.

[0145] The original OCV-SOC curve is updated based on the modified OCV-SOC curve.

[0146] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the OCV-SOC curve self-learning method as described above.

[0147] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations:

[0148] Based on actual operating data and cell testing standard data, the first health state of the battery, the standard SOC trigger value, and the real-time SOC trigger value are determined. The standard SOC trigger value is the standard SOC value at the preset standard state trigger time, and the real-time SOC trigger value is the real-time SOC value at the preset standard state trigger time.

[0149] In response to the error between the first health state and the acquired second health state of the battery and the error between the SOC standard trigger value and the SOC real-time trigger value satisfying the preset update conditions, the OCV-SOC curve is corrected based on the actual operating data and the SOC real-time value corresponding to different times.

[0150] The original OCV-SOC curve is updated based on the modified OCV-SOC curve.

[0151] The present invention will now be described an electronic device 400 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. Electronic device 400 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 400 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, 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.

[0152] Electronic device 400 includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or a computer program loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0153] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0154] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. An OCV-SOC curve self-learning method, characterized in that, The method comprises: determining a first health state, a SOC standard trigger value and a SOC real-time trigger value of the battery based on actual operation data and battery cell test standard data, wherein the actual operation data comprises calendar life, the battery cell test standard data comprises standard cycle life and standard OCV-SOC curve, the first health state is obtained according to the standard cycle life and the calendar life, the SOC standard trigger value is a SOC standard value at a preset standard state trigger moment in the standard OCV-SOC curve, and the SOC real-time trigger value is a SOC real-time value at the preset standard state trigger moment, and the SOC real-time value is obtained according to the first health state; in response to errors between the first health state and a second health state of the battery obtained and between the SOC standard trigger value and the SOC real-time trigger value satisfying a preset update condition, obtaining a corrected OCV-SOC curve based on the actual operation data and SOC real-time values corresponding to different moments, and the second health state is used to represent the real health state of the battery; updating an original OCV-SOC curve based on the corrected OCV-SOC curve.

2. The OCV-SOC curve self-learning method of claim 1, wherein, The method of determining the first health state, the SOC standard trigger value and the SOC real-time trigger value of the battery based on the actual operation data and the battery cell test standard data comprises: determining an initial health life of the battery based on the standard cycle life and the calendar life, and correcting the initial health life by using a capacity interval method to obtain the first health state; obtaining the SOC standard trigger value by calling the SOC standard value of the battery at the preset standard state trigger moment according to the standard OCV-SOC curve; obtaining the SOC real-time value by using an ampere-hour integral method based on the first health state, and obtaining the SOC real-time trigger value by calling the SOC real-time value at the preset standard state trigger moment.

3. The OCV-SOC curve self-learning method of claim 2, wherein, After the method of determining the first health state, the SOC standard trigger value and the SOC real-time trigger value of the battery based on the actual operation data and the battery cell test standard data, the method further comprises: obtaining an OCV real-time value by using an online parameter identification algorithm to identify and process the actual operation data; obtaining a SOC estimation value by using a Kalman filtering algorithm based on the OCV real-time value and the standard OCV-SOC curve.

4. The OCV-SOC curve self-learning method of claim 3, wherein, The preset update condition comprises: an error between the first health state and the second health state is less than a preset health error; an error between the SOC standard trigger value and the SOC real-time trigger value is less than a preset SOC precision error; an error between the SOC estimation value and the corresponding SOC real-time value is greater than a preset correction precision error.

5. The OCV-SOC curve self-learning method of claim 2, wherein, The preset standard state trigger moment comprises a static moment and a full charge moment. The method of obtaining the SOC standard trigger value by calling the SOC standard value of the battery at the preset standard state trigger moment according to the standard OCV-SOC curve comprises: After monitoring that the battery meets the static condition, an OCV static value corresponding to a static time is obtained, and a SOC value corresponding to the OCV static value in the standard OCV-SOC curve is called to obtain a SOC static standard value; An OCV full charge value corresponding to at least one full charge time before the static time is obtained, and a SOC value corresponding to the OCV full charge value in the standard OCV-SOC curve is called to obtain a SOC full charge standard value; The calling of the SOC real-time value of the preset standard state trigger time to obtain the SOC real-time trigger value comprises: The SOC real-time value corresponding to the static time is called to obtain a SOC static real-time value, and the SOC real-time value corresponding to the full charge time is called to obtain a SOC full charge real-time value.

6. The OCV-SOC curve self-learning method of claim 2, wherein, The modified OCV-SOC curve based on the actual operation data and the SOC real-time value corresponding to different times comprises: An online parameter identification algorithm is used to identify and process the actual operation data to obtain an OCV real-time value; The OCV real-time value and the SOC real-time value of multiple different times are fitted to generate the modified OCV-SOC curve.

7. The OCV-SOC curve self-learning method of claim 1, wherein, After the original OCV-SOC curve is updated based on the modified OCV-SOC curve, it further comprises: After running for a preset stable time according to the modified OCV-SOC curve, the steps of determining the first health state of the battery, the SOC standard trigger value, and the SOC real-time trigger value of the preset standard state trigger time based on the actual operation data and the battery test standard are returned.

8. An OCV-SOC curve self-learning device, characterized by, Comprise: An acquisition module is used to determine the first health state of the battery, the SOC standard trigger value, and the SOC real-time trigger value based on actual operation data and battery test standard data, wherein the actual operation data includes calendar life, and the battery test standard data includes standard cycle life and standard OCV-SOC curve, the first health state is obtained according to the standard cycle life and the calendar life, the SOC standard trigger value is the SOC standard value of the preset standard state trigger time in the standard OCV-SOC curve, the SOC real-time trigger value is the SOC real-time value of the preset standard state trigger time, and the SOC real-time value is obtained according to the first health state; A judgment module is used to respond to the error between the first health state and the second health state of the battery obtained and the error between the SOC standard trigger value and the SOC real-time trigger value, and to obtain a modified OCV-SOC curve based on the actual operation data and the SOC real-time value corresponding to different times; An update module is used to update the original OCV-SOC curve based on the modified OCV-SOC curve.

9. An electronic device, comprising: Comprise a memory and a processor; The memory is used to store a computer program; The processor is used to implement the OCV-SOC curve self-learning method according to any one of claims 1 to 7 when the computer program is executed.

10. A computer-readable storage medium, characterized in that, The storage medium has stored thereon a computer program which, when executed by a processor, implements the OCV-SOC curve self-learning method according to any one of claims 1 to 7.

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