Battery cell storage SOC calculation method and system and readable storage medium

Abstract

The invention provides a battery cell storage SOC calculation method and system and a readable storage medium, and relates to the technical field of battery management. The method is executed by a battery management system and comprises the following steps: when a battery cell is statically stored, the battery management system is periodically awakened; after each wake-up, collecting the current voltage and current temperature of the battery cell; determining a current self-discharge rate based on the current temperature, a stored SOC value and a pre-stored self-discharge rate mapping relation; calculating SOC loss caused by self-discharge according to the current self-discharge rate and the self-storage period duration; the stored SOC value is corrected according to the SOC loss amount, and a current SOC estimated value is obtained; the current SOC estimation value is calibrated by using the current voltage; and storing the calibrated SOC value, and enabling the battery management system to enter dormancy.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a cell storage SOC calculation method, system, and readable storage medium. Background Technology

[0002] Accurate estimation of the state of charge (SOC) is a core function of the battery management system (BMS) and directly impacts battery safety and energy efficiency optimization. Currently, SOC estimation for lithium iron phosphate batteries primarily relies on the ampere-hour integration method. This method uses a current sensor to collect the battery's charging and discharging current in real time, combined with a software coulomb counter to accumulate changes in charge, enabling SOC tracking in dynamic scenarios. This method demonstrates stability during battery charging and discharging and can meet the accuracy requirements of typical usage scenarios.

[0003] However, the limitations of traditional methods become apparent when batteries enter the static storage phase. To reduce power consumption during dormancy, battery management systems typically shut down high-energy-consuming modules such as current sensors, making it impossible to monitor the minute currents generated by battery self-discharge in real time. Since self-discharge is an inherent characteristic of batteries, especially during long-term storage, the self-discharge effect accumulates over time, and the resulting State of Charge (SOC) loss cannot be captured and corrected using the traditional ampere-hour integration method. Over time, the estimated SOC value during storage gradually deviates from the actual remaining capacity, resulting in significant estimation errors. These errors can easily cause serious problems in practical use. For example, if users judge the battery's available energy based on distorted SOC displays, they may misjudge the battery's range, leading to unexpected device shutdowns. Furthermore, if the system formulates a charging strategy based on an incorrect SOC value, it may lead to over-discharge or over-charge of the battery, accelerating capacity degradation and even posing safety risks. Therefore, how to achieve accurate SOC correction during the static storage phase without relying on real-time current sensor acquisition has become an urgent technical problem to be solved. Summary of the Invention

[0004] The purpose of this invention is to provide a cell storage SOC calculation method, system, and readable storage medium to solve the technical problem mentioned in the background art: how to achieve accurate correction of SOC during the static storage stage without relying on real-time acquisition by current sensors.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for calculating the state of charge (SOC) of a battery cell is provided, the method being executed by a battery management system, the method comprising:

[0006] When the battery cells are in static storage, the battery management system is periodically woken up;

[0007] After each wake-up, the current voltage and temperature of the battery cell are collected;

[0008] The current self-discharge rate is determined based on the current temperature, the stored SOC value, and the pre-stored self-discharge rate mapping relationship.

[0009] Calculate the SOC loss caused by self-discharge based on the current self-discharge rate and self-storage cycle duration.

[0010] The stored SOC value is corrected based on the SOC loss to obtain the current SOC estimate.

[0011] The current SOC estimate is calibrated using the current voltage;

[0012] Store the calibrated SOC value and put the battery management system into sleep mode.

[0013] In one possible implementation, the pre-stored self-discharge rate mapping is constructed in the following way:

[0014] Acquire self-discharge rate test data of battery cells at different temperatures and different SOC points;

[0015] Based on the test data, a database or function model reflecting the relationship between temperature, SOC, and self-discharge rate is generated.

[0016] In one possible implementation, the step of determining the current self-discharge rate based on the current temperature, the stored SOC value, and the pre-stored self-discharge rate mapping relationship specifically includes:

[0017] When the current temperature does not match the pre-stored standard temperature point, the self-discharge rate at the current temperature is calculated using linear interpolation.

[0018] In one possible implementation, calibrating the current SOC estimate using the current voltage includes:

[0019] Based on the current voltage, the OCV-SOC calibration curve is consulted to obtain the SOC reference value corresponding to the voltage;

[0020] The current SOC estimate is fused with the SOC reference value, and the fusion result is used as the final current SOC estimate.

[0021] In one possible implementation, the acquisition of the current voltage and current temperature of the battery cell includes:

[0022] The voltage and temperature signals are sampled multiple times consecutively.

[0023] The results of multiple samplings are filtered, and the processed results are used as the current voltage and current temperature.

[0024] In one possible implementation, if acquiring the current voltage and current temperature fails, the default self-discharge rate is used to calculate the SOC loss.

[0025] According to another aspect of the present disclosure, a battery management system is provided, the battery management system comprising:

[0026] A wake-up module is used to periodically wake up the battery management system when the battery cell is in static storage.

[0027] The data acquisition module is used to collect the current voltage and temperature of the battery cell after it is woken up;

[0028] The storage module is used to store the self-discharge rate mapping relationship and the SOC value;

[0029] The calculation module is used for:

[0030] The current self-discharge rate is determined based on the current temperature, the SOC value stored in the storage module, and the self-discharge rate mapping relationship.

[0031] Calculate the SOC loss caused by self-discharge based on the current self-discharge rate and self-storage cycle duration.

[0032] The stored SOC value is corrected based on the SOC loss to obtain the current SOC estimate.

[0033] The current SOC estimate is calibrated using the current voltage;

[0034] The storage module is also used to store the calibrated SOC value, and the wake-up module is also used to enable the battery management system to enter a sleep state after storage is completed.

[0035] In one possible implementation, the calculation module is configured to calculate the self-discharge rate at the current temperature by linear interpolation when determining the self-discharge rate, provided that the collected temperature does not match a pre-stored standard temperature point.

[0036] According to another aspect of the present disclosure, a battery system is provided, the battery system comprising:

[0037] One or more battery cells;

[0038] The battery management system is connected to the battery cell, as described in any of the possible implementations above.

[0039] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the cell storage SOC calculation method as described in any of the above possible implementations.

[0040] The above-described one or more technical solutions in the embodiments of this application have at least one or more of the following technical effects:

[0041] The battery cell storage SOC calculation method provided in this embodiment of the invention periodically wakes up the battery management system during its dormant period and dynamically determines the self-discharge rate based on the current temperature and a pre-stored self-discharge rate mapping relationship, thereby accurately calculating the SOC loss caused by self-discharge and achieving real-time correction of the stored SOC value. Furthermore, the estimated value is calibrated using the collected current voltage, forming a complete closed-loop correction system including prediction and feedback. This technical solution enables continuous and accurate tracking and compensation of the natural SOC decay caused by self-discharge during static storage without relying on high-energy-consuming current sensors, fundamentally solving the problem of cumulative drift in SOC estimation caused by long-term storage. Its beneficial effects are directly reflected in: significantly improving the accuracy of SOC estimation during static storage and even subsequent use, avoiding risks such as unexpected device shutdown and misjudgment of driving range caused by SOC display distortion; at the same time, the system formulates charging and discharging strategies based on more accurate SOC values, which can effectively prevent overcharging or over-discharging of the battery, not only ensuring the safety of battery use, but also helping to delay battery capacity decay and extend battery life. It is especially suitable for fields such as electric vehicles where there are long-term static scenarios, and has important practical value and safety improvement.

[0042] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0043] Figure 1 This is a schematic flowchart of a cell storage SOC calculation method according to an exemplary embodiment;

[0044] Figure 2 This is a schematic diagram of the composition structure of a battery management system module according to an exemplary embodiment.

[0045] Explanation of reference numerals in the attached diagram: 100, wake-up module; 200, data acquisition module; 300, storage module; 400, calculation module. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0047] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of systems and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0048] Figure 1 This is a flowchart of a cell storage SOC calculation method according to an exemplary embodiment, such as... Figure 1 As shown, this method is executed by the battery management system and includes:

[0049] In step S100, when the battery cell is in static storage, the battery management system is periodically woken up; this step is the starting and triggering condition for the SOC correction in the static storage stage of the present invention. Its core is to enable the battery management system (BMS) to periodically resume its working state in a low-power manner during the static storage period when the battery cell is in a non-working state, so as to perform subsequent SOC estimation and correction operations.

[0050] Specifically, the determination of the battery cell's static storage state can be automatically triggered by the BMS based on the system status it monitors. In a typical implementation, when the BMS detects that the battery cell has no charging or discharging current, for example, when the charging or discharging circuit is cut off, or when the current value remains zero for a period of time, it can determine that the battery cell has entered the static storage state.

[0051] After determining that it has entered the static storage state, the BMS is configured to enter a low-power operating mode. In this mode, in order to minimize system power consumption, the BMS can control its internal circuitry to at least shut down the high-power-consuming current sensor module. At the same time, the BMS's main control unit can be set to a sleep state.

[0052] The periodic wake-up is achieved through a timer module inside the BMS. This timer module can be a low-power real-time clock inside the main control unit or an independent timing circuit. The timer module is preset with a wake-up period.

[0053] When the timer module reaches the preset wake-up period, a wake-up signal is generated. This wake-up signal is configured to wake up the main control unit of the BMS from the sleep state and restore control over necessary functional modules, such as voltage sampling circuit, temperature sampling circuit and memory, so that the BMS can execute the SOC calculation and correction process defined in subsequent steps S200 to S700.

[0054] The default value for the wake-up period is preferably set to 6 hours; the wake-up period can be dynamically configured according to environmental conditions. In a preferred embodiment, the BMS is configured as follows:

[0055] In high-temperature environments (e.g., temperatures above 40°C), due to the high self-discharge rate, the wake-up cycle is automatically shortened to 4 hours to track and correct the SOC more frequently.

[0056] In low-temperature environments (e.g., below 0°C), due to the low self-discharge rate, the wake-up cycle is automatically extended to 8 hours to further optimize system power consumption. This dynamic adjustment strategy is executed based on the temperature-cycle correspondence pre-existing in the BMS.

[0057] In step S200, after each wake-up, the current voltage and temperature of the battery cell are collected; this step is the data acquisition basis for subsequent SOC prediction calculation and closed-loop calibration; after the battery management system is periodically woken up according to step S100 and completes the necessary initialization, it immediately executes the data acquisition operation of this step.

[0058] Specifically, step S200 is implemented through the data acquisition module built into the BMS. This data acquisition module typically includes a voltage sampling circuit and a temperature sampling circuit, and works in conjunction with the main control unit of the BMS and the analog front-end chip.

[0059] The BMS is configured to control its analog front-end (AFE) circuit to connect the cell voltage sampling channel connected to the positive and negative electrodes of the cell. Through this channel, the AFE measures the current voltage at both ends of the cell. The obtained analog voltage signal is conditioned and then converted into a digital quantity by the analog-to-digital converter inside the AFE, and transmitted to the main control unit for processing and storage.

[0060] Simultaneously or sequentially, the BMS is configured to control its AFE to connect the temperature sampling channel connected to the temperature sensing element, preferably a negative temperature coefficient thermistor (NTC). By measuring the resistance value of the NTC element under the current environment, the AFE can calculate the corresponding current temperature, which is also converted by an analog-to-digital converter and transmitted to the main control unit in digital form.

[0061] To ensure the accuracy and stability of the collected data, in a preferred embodiment, after the main control unit is woken up, the BMS can wait for a preset circuit stabilization time, such as several milliseconds, until the analog sampling circuit reaches a stable working state before performing voltage and temperature acquisition operations.

[0062] To strictly control power consumption, in a preferred embodiment, the BMS keeps or immediately sets non-core functions to a powered-off or low-power state during the wake-up phase. This includes high-power communication interfaces, such as CAN communication modules.

[0063] In step S300, the current self-discharge rate is determined based on the current temperature, the stored SOC value, and the pre-stored self-discharge rate mapping relationship. This step is the core prediction link of the present invention. Its purpose is to dynamically and accurately quantify the SOC decay rate caused by self-discharge in the current storage state through a pre-established model when the self-discharge current cannot be directly measured.

[0064] Specifically, the pre-stored self-discharge rate mapping relationship is a set of data relationships that are experimentally determined and stored in the non-volatile memory of the BMS; the mapping relationship defines the self-discharge rate (k) of the cell per unit time at a specific temperature (T) and a specific state of charge (SOC).

[0065] In step S400, the SOC loss caused by self-discharge is calculated based on the current self-discharge rate and the self-storage cycle duration. This step converts the instantaneous self-discharge rate predicted in step S300 into a specific total SOC loss accumulated over a specific time span, thereby providing an accurate numerical basis for subsequent direct correction of the stored SOC value. Specifically, the main control unit of the BMS is configured to perform the following calculation process:

[0066] The MCU reads two key input variables from memory or registers: the current self-discharge rate and the self-storage cycle duration. The current self-discharge rate, calculated or queried in step S300, is physically represented as the percentage of SOC decay per unit time. The self-storage cycle duration (…) This represents the time interval between the current wake-up time and the previous SOC correction time, i.e., the time when step S700 was completed after the last wake-up, or the time when the system initially entered the static storage state. The duration is measured and provided by the timer module within the BMS, and its unit must be consistent with the current self-discharge rate (…). The units should be consistent.

[0067] The MCU calculates the SOC loss caused by self-discharge through arithmetic operations. The calculation is performed using the following mathematical relation:

[0068] in:

[0069] Indicates the duration of the self-storage period The reduction in SOC due to self-discharge is usually expressed as a percentage. . This indicates the current self-discharge rate. Indicates the duration of the self-storage cycle.

[0070] The theoretical basis for this calculation relationship lies in the time interval. If the current self-discharge rate is considered... If we consider it as a constant or average decay rate, then its product with time is the accumulated SOC loss during that time period.

[0071] Through this step, the present invention achieves effective quantification of the unmeasurable self-discharge accumulation effect. It precisely linearizes a dynamic decay process dependent on environmental conditions (temperature) and battery state of charge (SOC) over discrete time intervals, thus transforming a complex electrochemical process into a simple arithmetic operation that can be directly processed by the battery management system (BMS). This calculation result is the core data input for subsequent direct and quantitative correction of the storage SOC value, enabling the SOC estimate to track the actual capacity decay of the battery during resting periods and effectively preventing cumulative drift of the estimate.

[0072] In step S500, the stored SOC value is corrected according to the SOC loss to obtain the current SOC estimate. This step is the part of the present invention that predictively corrects the SOC during static storage. Its purpose is to apply the theoretical SOC loss due to self-discharge calculated in step S400 to the SOC baseline value of the BMS internal storage, thereby obtaining an updated SOC estimate that reflects the effect of self-discharge.

[0073] Specifically, the MCU reads the stored SOC value from the non-volatile memory. This value represents the SOC baseline that was confirmed and stored in the previous correction cycle, or when initially entering the storage state. Simultaneously, the MCU retrieves the SOC loss calculated in step S400 from memory. The MCU corrects the stored SOC value through subtraction to calculate the current estimated SOC value. The calculation is performed using the following mathematical relation:

[0074]

[0075] The physical meaning of this equation is that, by subtracting the theoretical capacity loss accumulated due to self-discharge from the previous known accurate SOC reference value up to the current time, a new SOC estimate that takes into account this loss is obtained.

[0076] In step S600, the current voltage is used to calibrate the current SOC estimate. This step is a key closed-loop calibration step for achieving high-precision SOC estimation in this invention. Its purpose is to use the battery state information contained in the current voltage collected in step S200 to verify and correct the current SOC estimate obtained by the prediction model in step S500, thereby eliminating the cumulative error that may exist in the prediction model and finally obtaining a more reliable and accurate SOC value.

[0077] In step S700, the calibrated SOC value is stored and the battery management system is put into sleep mode. This step is the closing step of a single correction cycle of the present invention. Its core function is to solidify the correction results and prepare for the next cycle, thereby ensuring that the SOC estimate has continuity and cumulative correction capability throughout the storage period, while maintaining the low power consumption operation of the system.

[0078] Specifically, the MCU writes the calibrated SOC value, obtained in step S600 and calibrated with voltage, into non-volatile memory. This operation overwrites the previously stored SOC value with the calibrated SOC value. After successfully storing the SOC value, the MCU generates control instructions to sequentially switch the BMS from an active operating state to a low-power sleep mode. This process includes: controlling the BMS to at least shut down the high-power data acquisition module that is activated after wake-up, the module including analog front-end circuitry for acquiring voltage and temperature; causing the MCU itself to enter a preset low-power sleep mode, such as the microcontroller's STOP or SLEEP mode; starting or resetting the low-power timer module to begin a new sleep timer cycle, and waiting for it to generate a wake-up signal again after reaching a preset wake-up cycle. This operation ensures that the BMS is in an extremely low-power state for most of the static storage time, minimizing the system's own energy consumption, solving the battery power loss problem caused by continuous BMS operation, and ensuring the feasibility of this method in terms of power consumption.

[0079] By periodically waking up the battery management system during its dormant period and dynamically determining the self-discharge rate based on the current temperature and a pre-stored self-discharge rate mapping, the system accurately calculates the SOC loss caused by self-discharge, achieving real-time correction of the stored SOC value. Furthermore, the estimated value is calibrated using the collected current voltage, forming a complete closed-loop correction system encompassing prediction and feedback. This technical solution enables continuous and accurate tracking and compensation for natural SOC decay caused by self-discharge during static storage without relying on high-energy-consuming current sensors, fundamentally solving the problem of cumulative drift in SOC estimation caused by long-term storage. Its beneficial effects are directly reflected in: significantly improving the accuracy of SOC estimation during static storage and subsequent use, avoiding risks such as unexpected device shutdowns and misjudgments of driving range caused by SOC display distortion; simultaneously, the system formulates charging and discharging strategies based on a more accurate SOC value, effectively preventing overcharging or over-discharging of the battery, ensuring battery safety, delaying battery capacity decay, and extending battery life. It is particularly suitable for fields such as electric vehicles where long-term static storage is common, possessing significant practical value and safety improvements.

[0080] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0081] In an exemplary embodiment, the pre-stored self-discharge rate mapping relationship is constructed in the following manner:

[0082] Acquire self-discharge rate test data of the battery cells at different temperatures and SOC points. Specifically, multiple battery cells of the same specifications and batch as the target product can be selected as test samples, for example, 3 to 5 lithium iron phosphate single-cell batteries. Each test cell is charged to a different, preset initial SOC point. The initial SOC point should cover the typical operating range of the battery cell. Preferably, the initial SOC point includes, but is not limited to, 0%, 20%, 40%, 60%, 80%, and 100%, and the SOC control accuracy is preferably within ±1%. After completion, the battery cells are left to stand for a predetermined period of time to ensure their open-circuit voltage stabilizes.

[0083] The initialized test cell is placed in a programmable temperature chamber, and the temperature of the chamber is set to traverse a series of typical temperature points covering the expected storage environment. These temperature points preferably include -20℃, 0℃, 10℃, 25℃, 35℃, 45℃, and 60℃, with the control error for each temperature point preferably within ±1℃. Each test cell will be tested at its specified initial SOC and temperature combination.

[0084] Under each defined temperature-SOC combination, the battery cells underwent long-term static storage testing. During the testing period, the open-circuit voltage (OCV) of the battery cells and the ambient temperature were collected at fixed times each day. Based on a known, high-precision calibrated "OCV-SOC relationship curve," the daily collected open-circuit voltage values ​​were used to extrapolate the corresponding daily SOC values. By calculating the difference between the SOC values ​​of two adjacent days, the average daily self-discharge rate within that time interval could be obtained. The calculation formula is:

[0085]

[0086] Where n is the number of test days.

[0087] Based on the test data, a database or function model reflecting the relationship between temperature, SOC, and self-discharge rate is generated. Specifically, for each test temperature point, different initial SOC points are correlated with their corresponding daily average self-discharge rates to form a scatter plot of "SOC-self-discharge rate".

[0088] Mathematical methods, such as linear or quadratic polynomial fitting, are used to fit the scattered data to obtain a continuous functional relationship between the self-discharge rate and the state of charge (SOC) at that temperature. The fitting results can reflect the characteristics of different SOC ranges; for example, the self-discharge rate is slightly higher in the high SOC range (80%~100%) and slightly lower in the low SOC range (0%~20%).

[0089] The fitting results at all temperature points are integrated to construct a structured database or function model. This model defines the corresponding self-discharge rate (η) value or calculation method for any given temperature (T) and SOC combination.

[0090] Finally, the constructed self-discharge rate mapping relationship is burned into the non-volatile memory of the BMS, for example, in the form of a lookup table or a set of fitted coefficients, for later retrieval. An example data storage format is shown in the table below:

[0091] Temperature T (°C) SOC range (%) Daily self-discharge rate η (% / day) Fitting formula (SOC is the variable x) 0 0~20 0.02 η = 0.02 + 0.0001x 0 20~100 0.022 η = 0.022 + 0.0001x 25 0~20 0.05 η = 0.05 + 0.0002x 25 20~100 0.055 η = 0.055 + 0.0002x 45 0~20 0.15 η = 0.15 + 0.0005x 45 20~100 0.16 η = 0.16 + 0.0005x

[0092] The self-discharge rate mapping relationship constructed by the above offline pre-calibration method fully considers the combined influence of temperature and SOC, two key factors, on self-discharge behavior. This enables the BMS to dynamically and accurately obtain the current self-discharge rate based on the real-time collected temperature and stored SOC value when applied online, providing a scientific basis for accurately calculating SOC loss.

[0093] In an exemplary embodiment, the step of determining the current self-discharge rate based on the current temperature, the stored SOC value, and the pre-stored self-discharge rate mapping relationship specifically includes:

[0094] When the current temperature does not match a pre-stored standard temperature point, the self-discharge rate at the current temperature is calculated using linear interpolation. Specifically, the BMS main control unit reads the stored SOC value from the non-volatile memory. This value is then compared with the SOC intervals in the pre-stored self-discharge rate mapping relationship to determine its specific SOC interval. For example, if... If it is 80%, then it belongs to SOC range.

[0095] The BMS determines the current temperature collected. Whether it completely matches the standard temperature point in the pre-stored self-discharge rate mapping relationship.

[0096] If the current temperature is exactly the same as any standard temperature point, then the self-discharge rate corresponding to that temperature point is directly used as the base value.

[0097] If the current temperature is between two standard temperature points, then the basic value of the self-discharge rate at the current temperature is calculated using linear interpolation.

[0098] The specific formula for linear interpolation is as follows:

[0099] in, and These are two standard temperature points adjacent to the current temperature, and . and They are respectively in and At temperature, the baseline value of self-discharge rate corresponding to the current SOC range. This is the baseline value of the self-discharge rate at the current temperature, calculated through linear interpolation.

[0100] An exemplary calculation process is as follows: Given the self-discharge rate at the standard temperature point of 10℃... Self-discharge rate at standard temperature point 25℃ When the current temperature hour:

[0101] To obtain the baseline value of self-discharge rate at the current temperature. Subsequently, the BMS further refines the self-discharge rate based on a pre-stored fitting formula within the current SOC range. The fitting formula characterizes the functional relationship between the self-discharge rate and the SOC value within that SOC range. The fitting formula is:

[0102]

[0103] Where the function This is a pre-stored fitting formula corresponding to the current SOC and temperature ranges. An example calculation process is as follows:

[0104] Following the previous example, currently... The SOC range is 20% to 100%, and the fitting formula for this range at the corresponding temperature is pre-stored. Then it will Substitute into the formula to calculate:

[0105]

[0106] By employing linear interpolation within a temperature range and combining it with precise correction of the SOC value, this invention achieves high-precision dynamic estimation of the self-discharge rate. This method effectively resolves the contradiction between the limited number of pre-stored standard temperature points and the continuous variation of actual ambient temperature, enabling accurate self-discharge rate estimates to be obtained at any ambient temperature, and significantly improving the overall accuracy and adaptability of SOC estimation during static storage.

[0107] In an exemplary embodiment, calibrating the current SOC estimate using the current voltage includes:

[0108] The SOC reference value corresponding to the voltage is obtained by querying the OCV-SOC calibration curve based on the current voltage. Specifically, the battery management system is configured to access the OCV-SOC calibration curve (open circuit voltage-state of charge calibration curve) pre-stored in its non-volatile memory. This curve is pre-determined through experiments and establishes a precise correspondence between the open circuit voltage and SOC of the cell under specific conditions.

[0109] The BMS will collect the current voltage in step S200 ( The input is used as the query input, and the value is searched in the OCV-SOC calibration curve. Through table lookup and interpolation calculation, a SOC reference value that precisely corresponds to the current voltage value is obtained. ).

[0110] An exemplary process is as follows: If the average total voltage collected is 13.05V, the corresponding SOC reference value is obtained by referring to the OCV-SOC calibration curve. .

[0111] The current SOC estimate is fused with the SOC reference value, and the fused result is used as the final current SOC estimate. The data fusion preferably uses a weighted average method, and the specific calculation formula is as follows:

[0112] That is, the arithmetic mean of the current SOC estimate and the SOC reference value is used as the final calibration result.

[0113] An example calculation process is as follows:

[0114] Assuming wake-up cycle The formula for calculating the SOC consumed by self-discharge is as follows:

[0115]

[0116] The preliminary revised SOC estimate is calculated using the following formula:

[0117]

[0118] That is, the SOC estimate obtained from the initial correction. The SOC reference value obtained from the voltage query Therefore, the final SOC calculation is as follows:

[0119]

[0120] In a preferred embodiment, this fusion calibration method ensures that the overall error of the SOC estimation is no greater than ±0.05%. This calibration step constructs a crucial feedback loop. It utilizes the stable and reproducible correspondence between voltage and SOC under static storage conditions, using the directly measured voltage value as an objective and reliable benchmark to verify and correct the SOC estimate obtained based on the self-discharge model. This fusion strategy effectively suppresses long-term cumulative drift that may be caused by initial model errors or slight mismatches in the self-discharge rate mapping relationship, controlling the uncertainty of open-loop prediction within a limited range, and significantly improving the accuracy and reliability of the entire method in long-term static storage scenarios.

[0121] In an exemplary embodiment, the acquisition of the current voltage and current temperature of the battery cell includes:

[0122] The voltage and temperature signals are sampled multiple times consecutively; specifically, the battery management system is configured to perform analog-to-digital conversion sampling of the voltage and temperature signals multiple times consecutively within a specific time window after each wake-up. The voltage signals include the total voltage of the battery and the voltage of each individual cell, and the temperature signals typically originate from thermistors mounted on the surface of the cells.

[0123] For each measured signal, N consecutive samples are performed, where N is an integer greater than 1, preferably N=3. A preset time interval is provided between two adjacent samples, preferably 100 milliseconds.

[0124] The results of multiple samplings are filtered, and the processed results are used as the current voltage and current temperature. The main control unit of the BMS is configured to perform digital filtering on each group of multiple sampling values ​​obtained in the aforementioned steps to suppress noise and eliminate possible outliers. Finally, the processed results are used as the current voltage and current temperature for subsequent calculations.

[0125] The filtering process includes at least the following steps:

[0126] Calculate the arithmetic mean for each set of sampled data, for example, three total voltage samples. Each sampled value is compared with the calculated arithmetic mean, and a reasonable deviation threshold is set, for example, ±5% of the mean. Any sampled value exceeding this threshold will be identified as an outlier and removed from the data set.

[0127] After removing outliers, the arithmetic mean of the remaining valid sampled values ​​is recalculated, and this final average is used as a reliable current voltage and current temperature output for subsequent SOC calculation steps.

[0128] The exemplary process is as follows: At an ambient temperature of approximately 25°C, the total battery voltage is sampled three times consecutively. After the filtering process described above, a stable average total voltage is obtained. and average temperature .

[0129] Through the aforementioned continuous sampling and filtering mechanism, this invention effectively overcomes the transient interference and noise that may affect the analog sampling circuit in complex battery operating environments. This design significantly improves the accuracy and stability of voltage and temperature measurement data, providing a high-quality data foundation for subsequent voltage-based SOC closed-loop calibration and temperature-based self-discharge rate query, thereby ensuring the final accuracy and reliability of the entire SOC correction algorithm from the source.

[0130] In an exemplary embodiment, when acquiring the current voltage and current temperature fails, a default self-discharge rate is used to calculate the SOC loss. The BMS is configured to abandon the conventional process of querying the self-discharge rate based on the current temperature when valid current temperature data cannot be obtained, and instead use a pre-stored default self-discharge rate for subsequent calculations.

[0131] The default self-discharge rate is preferably the self-discharge rate corresponding to the range of the currently stored SOC value under standard room temperature (e.g., 25°C) conditions. This default value is pre-stored in the non-volatile memory of the BMS.

[0132] Meanwhile, the main control unit of the BMS is configured to record a corresponding fault code in the internal memory or a dedicated fault log area. This fault code is used to indicate the occurrence of a "voltage / temperature acquisition abnormality" event, which facilitates subsequent system diagnosis and maintenance.

[0133] After enabling the default self-discharge rate and recording the fault code, the BMS will continue to execute the subsequent processes of steps S400 to S700. That is, it will calculate the SOC loss using the default self-discharge rate and the storage cycle duration, and correct and store the stored SOC value.

[0134] By introducing this anomaly handling mechanism, even in the event of a temporary or permanent failure in the data acquisition process, the system can still make necessary corrections to the SOC based on a reasonable conservative estimate, namely the default self-discharge rate. This avoids the complete paralysis of the entire SOC correction function due to the failure of a single module. At the same time, by recording fault codes, it provides a clear direction for subsequent technical maintenance and troubleshooting, demonstrating the completeness and engineering practicality of the system design.

[0135] Please see Figure 2 This disclosure also provides a battery management system, including:

[0136] The wake-up module 100 is used to periodically wake up the battery management system when the battery cell is in static storage; specifically, the wake-up module 100 is configured to periodically switch the battery management system from a low-power sleep state to a normal working state when the battery cell is in static storage.

[0137] In one embodiment, the wake-up module 100 includes a low-power timer (such as an RTC) with a configurable wake-up period, which is set to 6 hours by default. The wake-up module 100 has low power consumption characteristics; its operating current during the wake-up process is no more than 5mA, and its overall static power consumption is no more than 1mA after the control system enters a sleep state.

[0138] The data acquisition module 200 is used to acquire the current voltage and current temperature of the battery cell after the battery management system is woken up. Specifically, the data acquisition module 200 works in conjunction with the wake-up module 100 and is configured to acquire the current voltage and current temperature of the battery cell after the battery management system is woken up.

[0139] The data acquisition module 200 includes a voltage acquisition unit and a temperature acquisition unit. The voltage acquisition unit is configured to acquire the total voltage of the battery and the voltage of each individual cell, with a preferred acquisition accuracy of ±1mV and a preferred sampling rate of 1Hz after wake-up. The temperature acquisition unit preferably includes an NTC thermistor mounted on the surface of the cell, configured to measure the cell temperature, with a measurement range covering -40℃ to 85℃, a preferred accuracy of ±0.5℃, and a preferred sampling rate of 1Hz after wake-up.

[0140] Storage module 300 is used to store the self-discharge rate mapping relationship and the SOC value; specifically, this module uses non-volatile memory, such as EEPROM, to store various data and parameters required for system operation. Storage module 300 is configured to store a predetermined self-discharge rate mapping relationship, voltage and temperature data collected after each wake-up, and the latest calculated SOC value.

[0141] Calculation module 400 is used for:

[0142] The current self-discharge rate is determined based on the current temperature, the SOC value stored in the storage module 300, and the self-discharge rate mapping relationship.

[0143] Calculate the SOC loss caused by self-discharge based on the current self-discharge rate and self-storage cycle duration.

[0144] The stored SOC value is corrected based on the SOC loss to obtain the current SOC estimate.

[0145] The current SOC estimate is calibrated using the current voltage;

[0146] The storage module 300 is also used to store the calibrated SOC value, and the wake-up module 100 is also used to enable the battery management system to enter a sleep state after the storage is completed.

[0147] The computing module 400 is connected to the data acquisition module 200 and the storage module 300, and is configured to execute the core SOC computing and correction algorithm. The computing module 400 is preferably implemented by a low-power microcontroller (MCU, such as an STM32L4 series chip), which supports floating-point operations to meet the computational accuracy requirements.

[0148] After the wake-up module 100 periodically wakes up the system, it triggers the data acquisition module 200 to collect the current voltage and temperature of the battery cell. The collected data is transmitted to the calculation module 400. The calculation module 400 reads the stored SOC value and self-discharge rate mapping relationship from the storage module 300, performs a series of calculations, and finally obtains the calibrated value. Subsequently, the calculation module 400 writes the calibrated SOC value back to the storage module 300 for updating. Finally, the wake-up module 100 controls the entire battery management system to enter a sleep state again, waiting for the next wake-up.

[0149] The battery management system provided in this embodiment forms a complete, low-power closed-loop control system through the hardware integration and software collaboration of the above modules. It can automatically and accurately correct the SOC estimation error caused by self-discharge during long-term static storage of the battery without relying on current sensors. This system is particularly suitable for fields such as electric vehicles that have long-term static storage requirements, effectively improving the reliability, safety and lifespan of battery management.

[0150] In an exemplary embodiment, the calculation module 400, when determining the self-discharge rate, is configured to calculate the self-discharge rate at the current temperature using linear interpolation when the collected temperature does not match a pre-stored standard temperature point. This configuration enables the system to accurately estimate the self-discharge rate at any actual ambient temperature based on limited standard temperature point data, significantly improving the system's adaptability in practical applications and the accuracy of SOC estimation.

[0151] In an exemplary embodiment, the present invention also provides a battery system comprising:

[0152] One or more battery cells; specifically, one or more lithium iron phosphate cells connected in series to form a battery system; in one specific embodiment, the cell group uses four individual cells connected in series to form a battery module with a rated voltage of 12.8V, and its operating voltage range covers 12.8V to 13.2V. The rated capacity of the cell group... It has a capacity of 12Ah and is designed to have a storage temperature range of -30°C to 60°C to meet the application requirements in a wide temperature environment.

[0153] As described in the above embodiments, the battery management system is connected to the battery cell; specifically, the voltage acquisition module of the battery management system is connected to the positive and negative terminals of the battery cell group and the connection point of each individual battery cell, and is used to monitor the total voltage and the voltage of each individual cell.

[0154] The temperature acquisition module of the battery management system obtains the real-time temperature of the battery cell by connecting to a thermistor installed on the surface of the battery cell.

[0155] In this battery system, the battery management system (BMS) is configured to monitor and manage the status of the cell pack. Specifically, when the cell pack is in static storage, i.e., a non-charging / discharging resting state, the BMS performs a series of operations, including the aforementioned periodic wake-up, data acquisition, self-discharge rate determination, SOC loss calculation, SOC correction, and voltage calibration, thereby achieving accurate, low-power tracking and maintenance of the SOC value during storage.

[0156] The battery system provided in this embodiment effectively solves the problem of inaccurate SOC estimation caused by self-discharge during long-term static storage of lithium iron phosphate batteries by combining specific cell packs with an advanced battery management system. This system can automatically compensate for capacity decay, ensuring that users obtain accurate and reliable remaining capacity information when activating the battery. Simultaneously, by maintaining the SOC within a reasonable range, it prevents the risk of battery damage due to over-discharge, significantly improving the reliability, safety, and lifespan of the battery pack in applications such as electric vehicles and energy storage devices where long-term static storage is required.

[0157] In an exemplary embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the cell storage SOC calculation method as described in any of the exemplary embodiments above. Optionally, the storage medium is a non-transitory computer-readable storage medium, such as a ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, and optical data storage device, etc.

[0158] In an exemplary embodiment, a computer program product is also provided, which includes computer program code stored in a computer-readable storage medium. A processor of a computer device reads the computer program code from the computer-readable storage medium and executes the computer program code, causing the computer device to perform the operations performed in the above-described cell storage SOC calculation method.

[0159] Any aspects of this invention not described in detail are well-known to those skilled in the art.

[0160] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for calculating the State of Charge (SOC) of a battery cell, characterized in that, The method is executed by a battery management system, and the method comprises: The battery management system is periodically woken up when the battery cell is in static storage; After each wake-up, the current voltage and current temperature of the battery cell are collected; Based on the current temperature, the stored SOC value, and a pre-stored self-discharge rate mapping relationship, a current self-discharge rate is determined; According to the current self-discharge rate and the self-storage period length, a SOC loss amount caused by self-discharge is calculated; The stored SOC value is corrected according to the SOC loss amount, and a current SOC estimation value is obtained; The current SOC estimation value is calibrated using the current voltage; The calibrated SOC value is stored, and the battery management system is put into hibernation.

2. The electric chip storage SOC calculating method according to claim 1, characterized by, The pre-stored self-discharge rate mapping relationship is constructed by: Obtaining self-discharge rate test data of the battery cell at different temperatures and different SOC points; Based on the test data, a database or a function model reflecting the corresponding relationship between temperature, SOC and self-discharge rate is fitted and generated.

3. The method of claim 1, wherein, The step of determining the current self-discharge rate based on the current temperature, the stored SOC value, and the pre-stored self-discharge rate mapping relationship specifically comprises: When the current temperature does not match the pre-stored standard temperature point, the self-discharge rate at the current temperature is calculated by linear interpolation.

4. The method of claim 1, wherein, The calibration of the current SOC estimation value using the current voltage comprises: According to the current voltage, an OCV-SOC calibration curve is queried to obtain a voltage corresponding SOC reference value; The current SOC estimation value and the SOC reference value are fused, and the fusion result is taken as the final current SOC estimation value.

5. The method of claim 1, wherein, The collection of the current voltage and the current temperature comprises: Continuous multiple sampling of voltage and temperature signals; The results of multiple sampling are filtered to obtain the current voltage and the current temperature after processing.

6. The method of claim 1, wherein, When the current voltage and the current temperature cannot be collected, a default self-discharge rate is used to calculate the SOC loss amount.

7. A battery management system, characterized by, Comprise: The wake-up module is configured to periodically wake up the battery management system when the battery cell is in static storage; The data collection module is configured to collect the current voltage and the current temperature of the battery cell after wake-up; The storage module is configured to store the self-discharge rate mapping relationship and the SOC value; The calculation module is configured to: Determine the current self-discharge rate based on the current temperature, the stored SOC value in the storage module, and the self-discharge rate mapping relationship; According to the current self-discharge rate and the self-storage period length, a SOC loss amount caused by self-discharge is calculated; According to the SOC loss amount, the stored SOC value is corrected to obtain a current SOC estimation value; The current SOC estimation value is calibrated using the current voltage; The storage module is further configured to store the calibrated SOC value, and the wake-up module is further configured to put the battery management system into hibernation after storage is completed.

8. The battery management system of claim 7, wherein, When the collected temperature does not match the pre-stored standard temperature point, the calculation module is configured to calculate the self-discharge rate at the current temperature by linear interpolation.

9. A battery system characterized by, Comprise: One or more battery cells; The battery management system of claim 7 or 8, connected with the battery cell.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the battery cell storage SOC calculation method of any one of claims 1 to 6.

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