SOH dynamic threshold-based battery data hierarchical storage method and device, and storage medium

Abstract

The invention discloses a battery data hierarchical storage method and device based on an SOH dynamic threshold, and a storage medium. The battery data hierarchical storage method comprises the following steps: collecting battery data, and preprocessing and normalizing the battery data; calculating the current SOH value of the battery, and determining the health state of the current battery according to the current SOH value; wherein different data sampling frequencies and storage data types are preset for different battery health states; and according to the current battery health state, sampling the normalized battery data according to a preset data sampling frequency, and hierarchically storing the sampled battery data in different storage spaces with dynamically changed space sizes. According to different battery health state stages, the data storage frequency and the data volume are dynamically adjusted, so that effective storage and efficient utilization of the data are realized.

Description

Technical Field

[0001] This invention relates to the field of battery pack data processing technology, and in particular to a method, device, and storage medium for hierarchical storage of battery data based on a dynamic threshold of state of harm (SOH). Background Technology

[0002] With the rapid development of electric vehicles and large-scale energy storage systems, state of health (SOH) management of lithium-ion batteries has become one of the core functions of battery management systems (BMS). During operation, the BMS generates a large amount of real-time data, including key parameters such as voltage, current, and temperature. This data is crucial for battery status assessment, fault diagnosis, lifespan prediction, and accident tracing.

[0003] However, battery management systems (BMS) typically employ embedded storage solutions (such as eMMC and NOR Flash), which have limited storage capacity and read / write lifespan. Traditional data storage strategies mostly use fixed-frequency sampling patterns, which cannot adapt to the differences in data value at different stages of the battery's life cycle, leading to a series of technical pain points: a large amount of low-value redundant data is stored during the battery's healthy phase, wasting limited storage resources; while during the period of accelerated battery performance degradation, critical high-frequency characteristic data is lost due to insufficient sampling rate.

[0004] In summary, existing battery management system (BMS) data storage methods mainly suffer from the following technical shortcomings:

[0005] 1. Low storage resource utilization: Using a fixed frequency storage mode (such as 10Hz throughout) generates a large amount of redundant data when the state of battery health (SOH) is high (SOH>90%), resulting in an excessively high rate of invalid storage space occupation and premature wear and tear on the storage medium.

[0006] 2. Missing data during critical degradation period: When the battery enters the performance degradation period (SOH≤80%), fixed low-frequency sampling (e.g., 1Hz) cannot capture high-frequency characteristic signals (e.g., capacity jump, internal resistance surge) during the charging and discharging process, resulting in an excessively high rate of missed sampling of failure characteristic data.

[0007] 3. Lack of protection for inflection point data: The use of static storage management strategies such as "first-in, first-out" fails to identify and prioritize the preservation of data during critical periods before and after the SOH decay inflection point (decay rate ≥ 0.5% / day), resulting in the most valuable data during the accelerated decay period being overwritten and lost.

[0008] 4. Insufficient intelligence in the triggering mechanism: The existing solution lacks triggering conditions that are linked to the actual working state of the battery (such as the current rate), and cannot activate high-frequency recording when it is really needed (such as high-current charging and discharging). Summary of the Invention

[0009] The purpose of the present invention is to overcome the deficiencies and defects of the prior art, and to provide a battery data hierarchical storage method, device and storage medium based on the SOH dynamic threshold, aiming to dynamically adjust the data storage frequency and data volume according to different battery health state stages, so as to achieve effective data storage and efficient utilization.

[0010] The first object of the present invention is to provide a battery data hierarchical storage method based on the SOH dynamic threshold, including the steps of:

[0011] Collect battery data and preprocess and normalize the battery data;

[0012] Calculate the current SOH value of the battery, and determine the current health state of the battery according to the current SOH value; wherein, different data sampling frequencies and storage data types are preset for different battery health states;

[0013] According to the current battery health state, sample the normalized battery data at the preset data sampling frequency, and hierarchically store the sampled battery data in different storage spaces with dynamically changing space sizes.

[0014] Preferably, the battery health state is divided into a healthy period, a transition period and a decay period according to the battery SOH value;

[0015] Preferably, when SOH>90%, it is the healthy period; when 80%<SOH≤90%, it is the transition period; when SOH≤80%, it is the decay period;

[0016] Preferably, the data sampling frequencies of the healthy period, the transition period and the decay period are configured in a ladder, increasing in turn; preferably, the data sampling frequencies are 1Hz, 5Hz, 10Hz in turn.

[0017] Preferably, the stored data in the healthy period and the transition period include RTC time, voltage, temperature, current, internal resistance, and the stored data in the decay period include RTC time, voltage, temperature, current, internal resistance, cell voltage difference, and temperature rise rate.

[0018] Preferably, the total data storage space of the healthy period and the transition period is less than the data storage space of the decay period, and the data storage space of the healthy period is greater than the data storage space of the transition period.

[0019] Preferably, the storage space is divided into a healthy area and a decay area. The healthy area uses a circular buffer to buffer and manage data; the decay period uses the LRU management strategy to buffer and manage data;

[0020] Preferably, when it is monitored that the battery SOH>80% and lasts for a preset time, it is set as the healthy area;

[0021] When the current battery SOH is detected to be ≤80% for at least two consecutive times, it is judged to be in the degradation zone;

[0022] If the battery SOH decay rate is detected to be greater than or equal to the daily decay rate threshold for a preset number of consecutive days, the current SOH value is marked as the inflection point SOH value. Data within the preset number of days before the inflection point SOH value is copied / migrated from the health zone and decay zone to the protection zone. The data in the protection zone is read-only data. Preferably, the daily decay rate threshold is 0.5% / day.

[0023] Preferably, the health zone, decay zone, and protection zone are selected from at least one of eMMC, NOR Flash, and FRAM as storage media.

[0024] Preferably, the calculation of the current SOH value of the battery is performed according to the following formula:

[0025] SOH=( Q current / Q initial )×100%;

[0026] Among them, Q current The current maximum available capacity, Q initial This indicates the initial nominal capacity; preferably, the current maximum available capacity is measured by constant current discharge at 0.3C to the cutoff voltage.

[0027] Preferably, when calculating the current SOH value of the battery, the method further includes:

[0028] When determining the complete charge-discharge cycle / interval required to reach the SOH value calibration, the SOH value is calibrated using the open-circuit voltage method according to the preset error compensation threshold to achieve ±1% error compensation control. Then, the calibrated SOH value is used as the current SOH value of the battery.

[0029] Preferably, the open-circuit voltage method is used to calibrate the SOH value, including:

[0030] Allow the battery to stand completely for a period of time, and measure the open-circuit voltage (OCV) of all cells and the current ambient temperature.

[0031] The average open-circuit voltage (OCV) and the current ambient temperature are compared with a pre-stored three-dimensional lookup table of OCV-SOC-temperature value to obtain the current accurate SOC reference value. OCV ;

[0032] Obtain the current SOC calculated using the ampere-hour integration method. Ah Calculate the capacity difference ΔQ = |SOC OCV- SOC Ah ×Q current If the deviation ΔQ exceeds the initial capacity Q initial The preset percentage threshold is then used in SOC. OCVThe value is the standard, and the SOC is corrected. Ah The revised SOC Ah As the new Q current It will be used in subsequent SOH calculations.

[0033] Preferably, when the battery is detected to have entered the degradation period and |current|≥ a preset current rate threshold, a high-time-resolution charge-discharge curve with a battery SOC range of 0%-100% is recorded, and the recording of the single-cell voltage difference and temperature rise rate is initiated; preferably, the preset current rate threshold is 0.2C; preferably, the time resolution is greater than or equal to 100ms / point and less than 200ms / point; preferably, the charge-discharge curve is stored in a timestamp and binary stream format.

[0034] A second objective of this invention is to provide a computer device comprising a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the battery data tiered storage method based on the SOH dynamic threshold.

[0035] A third objective of the present invention is to provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the battery data hierarchical storage method based on the dynamic threshold of SOH.

[0036] This invention presents a battery data tiered storage method based on a dynamic SOH threshold. It dynamically adjusts the sampling frequency by adjusting the SOH threshold, reducing the sampling rate to save space during the healthy period and increasing the sampling rate to capture feature data during the degradation period. It introduces an inflection point detection and dynamic space allocation mechanism to prioritize the complete storage of data for the 30 days before and after the accelerated SOH degradation period. It establishes intelligent triggering conditions linked to the current rate to ensure that high-value data such as charge / discharge curves are recorded only under critical operating conditions. Ultimately, it achieves synergistic optimization of storage resource utilization and critical data integrity. Compared to existing technologies, this invention has the following significant advantages and technical effects:

[0037] Improved storage efficiency: Through the SOH tiered storage strategy (1Hz during healthy period, 10Hz during decay period) and dynamic space allocation (healthy area ≤15%, decayed area ≥60%, protected area ≥25%), the overall storage space occupancy is reduced, resulting in an extended lifespan of the storage chip.

[0038] Breakthrough in data integrity: 10Hz high-frequency sampling and recording of charge-discharge curves are initiated for the decay period when SOH≤80%, thereby improving the data integrity of the critical decay period.

[0039] Inflection point data reliable protection: By defining an inflection point (attenuation rate ≥ 0.5% / day for 3 consecutive days) and locking the data storage space for the 30 days before and after, the inflection point data capture rate is greatly improved, providing a solid data foundation for battery safety early warning and life prediction.

[0040] Precise and intelligent triggering mechanism: The charge and discharge curve recording function is triggered only when SOH≤80% and the absolute value of the current≥0.3C, avoiding invalid records and ensuring the high value and analyzability of the stored data.

[0041] This invention utilizes a real-time hierarchical control data storage strategy based on battery health status (SOH) to scientifically and rationally utilize storage system resources and perform differentiated data analysis for battery status at different stages. It is applicable to battery management systems (BMS) in scenarios such as electric vehicles and energy storage power stations, and in particular, it solves the contradiction between capturing key degradation data and optimizing storage resources throughout the battery's entire life cycle. Attached Figure Description

[0042] Figure 1 This is a flowchart of the battery data hierarchical storage method based on SOH dynamic threshold of the present invention. Detailed Implementation

[0043] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0044] The exemplary embodiment of this application, a battery data hierarchical storage method, collects battery state parameters, processes, filters, and stores the data, performs differentiated data analysis for battery states at different stages, and implements different storage frequencies for parameters of different states, enabling scientific and rational use of storage system resources; when the battery life is about to be exhausted, the data storage frequency is increased to ensure comprehensive collection of the current battery state data, so as to comprehensively collect the required battery data.

[0045] See Figure 1 As shown in the exemplary embodiment of this application, the battery data hierarchical storage method based on SOH dynamic threshold includes the following steps:

[0046] S1. Collect battery data and perform preprocessing and normalization on the battery data;

[0047] S2. Calculate the current SOH value of the battery and determine the current health status of the battery based on the current SOH value; different data sampling frequencies and data types are preset for different battery health statuses;

[0048] S3. Based on the current battery health status, sample normalized battery data at a preset data sampling frequency, and store the sampled battery data in different storage spaces with dynamically changing sizes.

[0049] In the embodiments of the present application, in step 1, sensors such as voltage, temperature, current, and internal resistance are used to collect data of the lithium-ion battery pack or battery system in real time. For example, a high-precision voltage sensor is used for voltage, an NTC temperature sensor is used for temperature, a current sensor is used for current, and an internal resistance collector is used for internal resistance. The system time and the collected data are obtained through RTC, and charge-discharge curve data can be formed. Among them, when preprocessing the collected data, different algorithms are used for data preprocessing according to the type of collected signal. For example, the ADC oversampling and decimation combined method is used for data preprocessing of voltage data; the moving window average filtering is used for data preprocessing of current data; the recursive average filtering is used for data preprocessing of temperature data. The preprocessed data is normalized and normalized to the interval [0,1] for subsequent SOH calculation and data storage.

[0050] In the exemplary embodiments of the present application, the battery health state is divided into a healthy period, a transition period, and a decay period according to the battery SOH value; in an optional embodiment, taking 80% and 90% of SOH as the division criteria, SOH>90% is divided into the healthy period, 80%<SOH≤90% is divided into the transition period, and SOH≤80% is divided into the decay period. Of course, it is not limited to this, and other division criteria can also be set, such as SOH>95% is divided into the healthy period, and less than 70% is the decay period. Among them, the battery health state is not limited to the three states of the healthy period, the transition period, and the decay period, and can also be divided into more health states to achieve a more refined division or fewer health states.

[0051] In the exemplary embodiments, taking the three battery health states of the healthy period, the transition period, and the decay period as an example, the data sampling frequencies of the healthy period, the transition period, and the decay period are configured in a stepped manner and increase in sequence. That is, when the battery health state is good, a low sampling frequency is used. Correspondingly, as the battery health state deteriorates, the sampling frequency increases to facilitate more sampled data to better monitor the battery state; in the exemplary embodiments, preferably, the data sampling frequencies are 1Hz, 5Hz, and 10Hz in sequence. This is only an example, and different stepped sampling frequencies can be set according to needs.

[0052] In an exemplary embodiment, taking three battery health states—healthy period, transition period, and degradation period—as examples, the stored data for the healthy period and transition period includes RTC time, voltage, temperature, current, and internal resistance. The stored data for the degradation period includes RTC time, voltage, temperature, current, internal resistance and single-cell voltage difference, and temperature rise rate (dT / dt). By setting different data types for different health state stages, better and more extensive data storage for the degradation period can be achieved, facilitating better monitoring of the battery during the degradation stage. This reduces the storage workload and storage space required by the battery management system during the non-degradation healthy period and transition period, alleviates system storage pressure, and balances the different data storage needs for different health states.

[0053] In an exemplary embodiment, since the data during the battery degradation period is more important, more detailed, and more abundant, and the data during the healthy period is relatively important, the total data storage space for the healthy period and the transition period is less than the data storage space for the degradation period, and the data storage space for the healthy period is greater than the data storage space for the transition period. For example, the data storage space for the healthy period accounts for ≤15% of the total storage space, the data storage space for the transition period accounts for 20% of the total storage space, the proportion of the healthy area corresponding to the storage space of the two is set to 35%, and the data storage space for the degradation period accounts for ≥65% of the total storage space. For example, the proportion is set to 65%. Of course, this ratio can be adjusted according to the size of the storage data space and the amount of data, and is not limited to this.

[0054] In an exemplary embodiment, the storage space is divided into a healthy region and a decaying region. The healthy region uses a circular buffer to manage data, while the decaying region uses an LRU (Least Recently Used) management strategy to manage data. Furthermore, data in the healthy region is compressed using Delta encoding, and data in the transition period is compressed using segmented compression.

[0055] In an exemplary embodiment, if the battery SOH > 80% and remains so for a preset time, such as 3 hours, it is set as a healthy zone. Data during the battery's healthy period is stored in the healthy zone, and a circular buffer is activated to manage the healthy zone data. When the current battery SOH ≤ 80% is detected at least twice consecutively, it is determined to be a degradation zone, and data during the battery's degradation period is stored in the degradation zone. When the battery enters the degradation period, the healthy zone is locked, all new data is written to the degradation zone, and an LRU management strategy is activated to manage the degradation zone data.

[0056] In an exemplary embodiment, when the battery SOH decay rate is detected to be greater than or equal to the daily decay rate threshold, such as ≥0.5% / day, and this decay continues for a preset number of days, such as 30 days, the current SOH value is marked as the inflection point SOH value. Data within the preset number of days before the inflection point SOH value is filtered out from the healthy zone and the decay zone and copied / migrated to the protected zone. The data in the protected zone is read-only data. The data in the protected zone is marked as read-only and will not be automatically overwritten or deleted under any circumstances. Only authorized diagnostic tools are allowed to read and clear it through specific instructions.

[0057] By classifying battery health states and storing data in tiers according to different health states, this approach breaks away from the traditional, single, static storage pool management method. It creates a partitioned architecture that dynamically correlates with battery health states and allocates space based on value. Different value weights are assigned to data at different life stages, and the most suitable management algorithms are matched (such as the efficiency of a circular buffer, the intelligence of LRU, and the safety of protected areas), achieving scientific and quantitative management of storage resources. Based on the setting of this degradation rate threshold, a quantitative diagnosis and data protection mechanism for the SOH degradation inflection point is formed. This allows the battery management system to automatically and reliably identify the most critical turning points in battery life and trigger subsequent data migration and read-only protection mechanisms, ensuring the integrity of extremely valuable data.

[0058] In an exemplary embodiment, the health region, decay region, and protection region are selected from at least one of eMMC, non-volatile flash memory (NOR Flash), and ferroelectric memory (FRAM) as storage media. More preferably, the health region and decay region are FRAM, and the protection region is NOR Flash.

[0059] In an exemplary embodiment, when the battery is detected to have entered the degradation period and |current| ≥ a preset current rate threshold, such as 0.2C, the charge-discharge curve of the battery's SOC range of 0%-100% is recorded with a time resolution greater than or equal to 100ms / point and less than 200ms / point, and stored using a timestamp and binary stream format. Recording of individual cell voltage difference and temperature rise rate is also initiated. Through this technical solution, when the battery's SOH value is detected to be no greater than 80% and the absolute value of the battery current is no less than 0.2C, the charge-discharge curve recording function is activated, recording detailed data including individual cell voltage difference, temperature rise rate, and current at a frequency of no less than 10Hz. Combining SOH state (long-term aging indicator) with current rate (instantaneous operating intensity) as trigger conditions ensures that high-density data is recorded only during critical stages of the battery's life cycle and under high-current conditions with analytical value, solving the core problem of "when to record in detail" and avoiding waste of storage resources.

[0060] In an exemplary embodiment, the calculation of the current SOH value of the battery is performed according to the following formula:

[0061] SOH=( Q current / Q initial )×100%;

[0062] Among them, Q current The current maximum available capacity can be obtained by measuring the discharge current at 0.3C to the cutoff voltage, or by measuring the total amount of charge released by discharging to the cutoff voltage of 2.5V using a discharge power type MAR meter. Q initial This indicates the initial nominal capacity, typically the total amount of electricity released when the battery is discharged at a constant current of 0.3C to a specified cutoff voltage of 2.5V under standard conditions (e.g., 25°C) at the time of manufacture. In an exemplary embodiment, the calculation of the battery's current state of equilibrium (SOH) also includes:

[0063] When determining the required complete charge-discharge cycles (a complete charge-discharge cycle refers to a charge-discharge process with a depth of discharge (DOD) ≥ 80%) / interval period to achieve SOH value calibration, the SOH value is calibrated using the open-circuit voltage method, and then the calibrated SOH value is used as the current SOH value of the battery; preferably, after no more than 20 (more preferably 10) complete charge-discharge cycles / after an interval of 90 days (whichever comes first), a calibration process is automatically initiated, using the OCV (open-circuit voltage) method for calibration, with an error compensation of ±1%.

[0064] Among them, the preset 0.3C constant current discharge rate threshold, the calibration cycle of no more than 20 (more preferably 10) complete charge-discharge cycles, and the accuracy control of ±1% error compensation are the best balance point verified by a large number of experiments. It can take into account the interference of test time on battery use and the calculation capability of BMS while ensuring the accuracy of calculation.

[0065] In an exemplary embodiment, the open-circuit voltage method is used to calibrate the SOH value, including:

[0066] Allow the battery to rest completely for a period of time (e.g., at least 2 hours if the current is 0), and measure the open-circuit voltage (OCV) of all cells and the current ambient temperature.

[0067] The average open-circuit voltage (OCV) and the current ambient temperature are compared with a pre-stored three-dimensional lookup table of OCV-SOC-temperature value to obtain the current accurate SOC reference value. OCV ;

[0068] Obtain the current SOC calculated using the ampere-hour integration method. Ah Calculate the capacity difference ΔQ = |SOC OCV- SOC Ah ×Q current If the deviation ΔQ exceeds the initial capacity Q initial The preset percentage threshold is then used in SOC. OCVThe value is the standard, and the SOC is corrected. Ah The revised SOC Ah As the new Q current It will be used in subsequent SOH calculations.

[0069] An exemplary embodiment of the present invention also provides a computer device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the battery data hierarchical storage method based on SOH dynamic threshold. The computer device may include a battery management system.

[0070] An exemplary embodiment of the present invention also provides a storage medium, which may be the computer device, such as part of a battery management system, wherein a computer program is stored on the storage medium, and the computer program, when executed by a processor, implements the steps of the battery data hierarchical storage method based on the dynamic threshold of SOH.

[0071] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0072] Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the claims be included within the invention.

[0073] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A battery data hierarchical storage method based on SOH dynamic threshold, characterized in that, It includes: Collect battery data, preprocess the battery data, and perform normalization processing; Calculate the current SOH value of the battery, and determine the current health state of the battery according to the current SOH value; wherein, different data sampling frequencies and different types of stored data are preset for different battery health states; According to the current health state of the battery, sample the normalized battery data at the preset data sampling frequency, and store the sampled battery data in different storage spaces with dynamically changing space sizes in a hierarchical manner.

2. The battery data hierarchical storage method based on SOH dynamic threshold according to claim 1, characterized in that, The battery health state is divided into a healthy period, a transition period, and a decay period according to the battery SOH value; Preferably, when SOH>90%, it is the healthy period; when 80%<SOH≤90%, it is the transition period; when SOH≤80%, it is the decay period; Preferably, the data sampling frequencies of the healthy period, the transition period, and the decay period are configured in a stepped manner and increase in sequence; preferably, the data sampling frequencies are 1Hz, 5Hz, and 10Hz in sequence.

3. The battery data hierarchical storage method based on SOH dynamic threshold according to claim 2, characterized in that, The stored data in the healthy period and the transition period include RTC time, voltage, temperature, current, and internal resistance. The stored data in the decay period include RTC time, voltage, temperature, current, internal resistance, cell voltage difference, and temperature rise rate.

4. The battery data hierarchical storage method based on SOH dynamic threshold according to claim 2, characterized in that, The total data storage space of the healthy period and the transition period is less than the data storage space of the decay period, and the data storage space of the healthy period is greater than the data storage space of the transition period.

5. The battery data hierarchical storage method based on SOH dynamic threshold according to claim 2, characterized in that, The storage space is divided into a healthy area and a decay area. The healthy area uses a circular buffer to buffer and manage data; the decay period uses an LRU management strategy to buffer and manage data; Preferably, when it is monitored that the battery SOH>80% and lasts for a preset time, it is set as the healthy area; When it is monitored that the current battery SOH≤80% at least continuously for 2 times, it is judged as the decay area; When it is monitored that the battery SOH decay rate≥the daily decay rate threshold and lasts for a preset number of consecutive days, mark the current SOH value as the inflection point SOH value, and copy / migrate the data within the preset number of days before the inflection point SOH value from the healthy area and the decay area to the protection area. The data in the protection area is read-only data. Preferably, the daily decay rate threshold is 0.5% / day; Preferably, at least one of eMMC, NOR Flash, and FRAM is selected as the storage medium for the healthy area, the decay area, and the protection area.

6. The battery data hierarchical storage method based on SOH dynamic threshold according to claim 1, characterized in that, The calculation of the current SOH value of the battery is carried out according to the following formula: SOH=( Q current / Q initial )×100%; Among them, Q current The current maximum available capacity, Q initial Indicates the initial nominal capacity; Preferably, the current maximum available capacity is measured by constant current discharge at 0.3C to the cut-off voltage.

7. The battery data hierarchical storage method based on SOH dynamic threshold according to claim 6, characterized in that, When calculating the current SOH value of the battery, it further includes: When it is judged that the complete charge and discharge cycle / interval period required for SOH value calibration is reached, calibrate the SOH value by the open circuit voltage method according to the preset error compensation threshold, and then use the calibrated SOH value as the current SOH value of the battery; Preferably, the calibration of the SOH value by the open circuit voltage method includes: Let the battery stand completely still for a period of time, measure the open circuit voltage OCV of all battery cells and the current ambient temperature; The average open-circuit voltage (OCV) and the current ambient temperature are compared with a pre-stored three-dimensional lookup table of OCV-SOC-temperature value to obtain the current accurate SOC reference value. OCV ; Obtain the current SOC calculated using the ampere-hour integration method. Ah Calculate the capacity difference ΔQ = |SOC OCV- SOC Ah ×Q current If the deviation ΔQ exceeds the initial capacity Q initial The preset percentage threshold is then used in SOC. OCV The value is the standard, and the SOC is corrected. Ah The revised SOC Ah As the new Q current It will be used in subsequent SOH calculations.

8. The battery data hierarchical storage method based on SOH dynamic threshold according to claim 2, characterized in that, When the battery is detected to have entered the degradation period and |current|≥ a preset current rate threshold, a high-time-resolution charge-discharge curve with a battery SOC range of 0%-100% is recorded, and the recording of the single-cell voltage difference and temperature rise rate is initiated; preferably, the preset current rate threshold is 0.2C; preferably, the time resolution is greater than or equal to 100ms / point and less than 200ms / point; preferably, the charge-discharge curve is stored in a timestamp and binary stream format.

9. A computer device, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the steps of the battery data hierarchical storage method based on the SOH dynamic threshold as described in any one of claims 1-8.

10. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the battery data hierarchical storage method based on the SOH dynamic threshold as described in any one of claims 1-8.

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