A lithium battery SOC and residual time estimation method combining dynamic voltage drop compensation and current adaptive calibration

Abstract

The application discloses a kind of lithium battery SOC and residual time estimation method combined with dynamic pressure drop compensation and current adaptive calibration, method includes: step 1, offline experiment is carried out, and basic database is constructed, and basic database includes standard OCV-SOC relationship curve calibration, multi-current V-SOC dynamic curve family calibration and ohmic internal resistance Rs-SOC relationship calibration;Step 2, real-time SOC estimation is carried out on line, mode determination and static OCV calibration, dynamic ampere-hour integral main, and double-stage compensation calibration;Step 3, calculate residual time.This application approaches the dynamic accuracy of model filtering algorithm, while maintaining the low computational complexity of table lookup method, with very high practicability and popularization.

Description

Technical Field

[0001] This invention belongs to the field of energy storage technology, and in particular relates to a method for estimating the SOC and remaining time of a lithium battery by combining dynamic voltage drop compensation and current adaptive calibration. Background Technology

[0002] With the rapid development of electric vehicles, distributed energy storage, and high-end portable devices, ternary lithium batteries have become the mainstream electrochemical energy storage solution due to their high energy density and long cycle life. The Battery Management System (BMS), as the "brain" of the battery pack, has one of its core functions: real-time and accurate estimation of the battery's State of Charge (SOC) and the remaining usable time (or driving range) based on the current state. Accurate SOC estimation is fundamental to ensuring the battery operates within a safe range, optimizing system energy dispatch, improving user experience, and alleviating "range anxiety."

[0003] Currently, industry research on SOC estimation methods mainly focuses on improving accuracy and robustness. Under ideal laboratory conditions, solutions based on high-precision sensors and complex algorithms can achieve high accuracy, but these methods generally suffer from problems such as high hardware computing power requirements, reliance on numerous calibration experiments, and complex parameter identification.

[0004] Therefore, in large-scale industrial applications that are cost-sensitive, have limited hardware resources, and are constrained by experimental conditions, there is an urgent need for a technical solution that achieves an excellent balance between accuracy, real-time performance, complexity, and feasibility. Summary of the Invention

[0005] The purpose of this invention is to solve the problems mentioned in the background art and to propose a method for estimating the SOC and remaining time of lithium batteries by combining dynamic voltage drop compensation and current adaptive calibration.

[0006] To achieve the objectives of this invention, this invention provides a method for estimating the SOC and remaining time of a lithium battery by combining dynamic voltage drop compensation and current adaptive calibration. The method includes:

[0007] Step 1: Conduct offline experiments and build a basic database. The basic database includes standard OCV-SOC relationship curve calibration, multi-current V-SOC dynamic curve family calibration, and ohmic internal resistance Rs-SOC relationship calibration.

[0008] Step 1 includes:

[0009] Step 11, standard OCV-SOC relationship curve calibration;

[0010] Step 12, Calibration of the family of dynamic V-SOC curves for multiple currents;

[0011] Step 13, calibration of the ohmic internal resistance Rs-SOC relationship;

[0012] Step 2: Perform online real-time SOC estimation, mode determination and static OCV calibration, dynamic ampere-hour integration main, and two-stage compensation calibration;

[0013] Step 2 includes:

[0014] Step 21: Perform mode determination and static OCV calibration;

[0015] Step 22: Perform two-stage compensation calibration using dynamic ampere-hour integration;

[0016] Step 3: Calculate the remaining time.

[0017] The significant advancement of this invention compared to existing technologies lies in:

[0018] (1) Achieving the optimal solution between accuracy and complexity: Through the two-level mechanism of "dynamic voltage drop compensation" and "current adaptive lookup table", the two core factors affecting SOC estimation (ohmic voltage drop and polarization voltage) are accurately compensated without introducing complex filtering algorithms, achieving dynamic accuracy close to that of the model filtering algorithm, while maintaining the low computational complexity of the lookup table method.

[0019] (2) Strong engineering applicability and low threshold: All offline experiments are standard industrial tests, requiring no complex parameter identification software. The online algorithm has very low requirements for MCU clock frequency and memory, and can be directly deployed on existing low-cost BMS hardware, greatly reducing the cost of technology upgrades.

[0020] (3) Excellent robustness and maintainability: The solution is not sensitive to current sampling noise and initial SOC error. When the battery drifts with aging characteristics, the accuracy can be restored simply by recalibrating the experiment and updating the curve library. The maintenance process is clear and simple, and the total life cycle cost is low.

[0021] (4) Fully functional and excellent user experience: It not only provides high-precision SOC, but also derives high-reliability remaining time prediction based on it. Through intelligent mode switching and data fusion, it ensures the continuity and stability of readings under various working conditions, effectively improving user trust.

[0022] (5) Wide applicability: The method takes ternary lithium battery as an example, but its core ideas (establishing a dynamic curve family, ohmic compensation, and table lookup calibration) can be widely applied to the SOC estimation of other battery systems such as lithium iron phosphate and lithium cobalt oxide, and have high promotion value.

[0023] To more clearly illustrate the functional characteristics and structural parameters of the present invention, further explanation is provided below in conjunction with the accompanying drawings and specific embodiments. Attached Figure Description

[0024] Figure 1 This is a flowchart of the method provided in the embodiments of this application;

[0025] Figure 2 This is a schematic diagram illustrating the construction of an offline high-precision basic database provided in an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the online real-time high-precision SOC estimation system provided in the embodiments of this application;

[0027] Figure 4 This is a flowchart of the overall solution provided in the embodiments of this application. Detailed Implementation

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

[0029] Existing mainstream SOC estimation methods each have significant drawbacks and are difficult to meet the aforementioned application requirements:

[0030] (1) Ampere-hour integration method: This method estimates the SOC by integrating the change of current over time. It is simple in principle and easy to implement. However, its fatal flaw is that it cannot correct the initial error, and any small deviation in current measurement will accumulate over time, causing the SOC estimation result to gradually deviate from the true value, resulting in poor long-term reliability.

[0031] (2) Open-circuit voltage method: This method utilizes the characteristic that the open-circuit voltage (OCV) and state of charge (SOC) of a battery have a definite monotonic correspondence after the battery has been left to rest for a sufficient period of time. Although the static accuracy is high, the battery is in a charged state for most of the working period, which cannot meet the requirements of real-time online estimation. In addition, the OCV-SOC relationship of the battery is significantly affected by temperature and aging.

[0032] (3) Algorithms based on equivalent circuit models and filtering (such as Kalman filtering and its variants): These methods establish a second-order or higher-order RC equivalent circuit model of the battery and combine it with algorithms such as Kalman filtering to perform state-optimal estimation. Theoretically, they can achieve high-precision estimation under dynamic operating conditions, but their shortcomings are extremely prominent:

[0033] 1) Strong model dependence: The accuracy is highly dependent on the accuracy of the equivalent circuit model.

[0034] 2) Complex parameter identification: The parameters such as ohmic internal resistance, polarization resistance, and polarization capacitance in the model change drastically with SOC, temperature, and state of health (SOH), requiring a lot of manpower and equipment resources for full-coverage testing and calibration, and the experimental conditions are demanding.

[0035] 3) High computational complexity: The iterative calculation of the filtering algorithm places high demands on the computing power of the microcontroller (MCU), which increases the system cost and power consumption.

[0036] 4) Robustness challenge: If the model parameters cannot be updated online as the battery ages, the estimation accuracy will continue to decline.

[0037] In summary, existing technologies either fail to meet long-term accuracy requirements due to accumulated errors, cannot be applied online due to poor real-time performance, or are too complex to be implemented under limited conditions.

[0038] The method provided in this solution mainly includes the following steps:

[0039] Step 1: Conduct offline experiments and build a basic database. The basic database includes standard OCV-SOC relationship curve calibration, multi-current V-SOC dynamic curve family calibration, and ohmic internal resistance Rs-SOC relationship calibration.

[0040] Step 2: Perform online real-time SOC estimation, mode determination and static OCV calibration, dynamic ampere-hour integration main, and two-stage compensation calibration;

[0041] Step 3: Calculate the remaining time.

[0042] Step 1: Conduct offline experiments and build a basic database;

[0043] The goal of Step 1 is to establish three interconnected digital maps that characterize the static and dynamic properties of the battery, providing a solid foundation for online algorithms.

[0044] Step 11, Standard OCV-SOC Relationship Curve Calibration: This curve is the "absolute benchmark" for SOC estimation. To ensure its accuracy, the following rigorous steps are followed:

[0045] (1) Environmental control:

[0046] The experiment was conducted in a temperature-controlled chamber at a standard temperature of 25±0.5°C.

[0047] (2) Charge and discharge preparation:

[0048] Using high-precision charging and discharging equipment (accuracy better than 0.1%FS), the battery pack is charged at a constant current of 0.05C (0.75A for a 15Ah battery pack) to the full charge cutoff voltage (16.8V), and then switched to constant voltage charging until the current drops below 0.01C to ensure that the battery reaches 100% SOC.

[0049] (3) Discharge and relaxation:

[0050] The discharge is performed at a constant current of 0.05C; the discharge is stopped after 5% of the rated capacity (i.e., 0.75Ah) has been discharged, and the system enters the resting relaxation stage.

[0051] (4) Determination of relaxation endpoint and data recording:

[0052] During the resting period, continuously monitor the terminal voltage; when the voltage change rate dV / dt is less than 0.1mV / min for 5 consecutive minutes, it is determined that the voltage has stabilized to the true OCV; record the voltage at this time and the corresponding cumulative discharged charge (the SOC point can be accurately calculated, such as 95%, 90%, etc.).

[0053] (5) Curve fitting:

[0054] After obtaining all discrete points (SOC, OCV), a piecewise, high-order polynomial fitting method is used to generate an OCV-SOC lookup table that can be quickly queried in the BMS.

[0055] Step 12, Calibration of the family of dynamic V-SOC curves for multiple currents:

[0056] This family of curves is crucial for achieving online dynamic calibration. For the battery pack (discharge current range 0-7.5A), the following steps are required to establish it:

[0057] (1) Selection of current point:

[0058] Cover typical operating currents while also taking into account curve interpolation requirements; select at least 6 points, for example: 1A (approximately 0.07C), 2A (0.13C), 3A (0.2C), 5A (approximately 0.33C), 6A (approximately 0.4C), 7.5A (0.5C, maximum discharge current).

[0059] (2) Experimental procedure:

[0060] For a fully charged battery, sequentially apply each selected constant current. Discharge to the cutoff voltage (9.6V); before each discharge, the battery must be allowed to stand until the voltage stabilizes to ensure an initial SOC of 100%.

[0061] (3) Synchronous data acquisition:

[0062] During the discharge process, at a frequency of not less than 1Hz, the following data are synchronously and precisely acquired: timestamp t, terminal voltage. Discharge current ;

[0063] (4) Data processing and curve generation:

[0064] Integrate the current to calculate the real-time discharge capacity. ;

[0065] Calculate real-time SOC: ;

[0066] This represents the total capacity of the battery pack.

[0067] Data organization For each discharge current Generate a dynamic terminal voltage-state of charge curve and store it as a lookup table;

[0068] Key observation: Comparing the curves of different currents, at the same SOC, the larger the current, the lower the terminal voltage. The difference includes ohmic voltage drop and polarization voltage drop.

[0069] Step 13, calibration of the ohmic internal resistance Rs-SOC relationship:

[0070] The ohmic internal resistance Rs-SOC is used for online real-time compensation of ohmic voltage drop; the internationally recognized hybrid pulse power characteristic test method is adopted.

[0071] (1) SOC point settings:

[0072] Select 8-10 points evenly within the battery's available SOC range (e.g., 100%, 90%, ..., 10%).

[0073] (2) Pulse test:

[0074] At a predetermined SOC point (discharged to that point by a small current), a continuous pulse duration is applied to the battery. A constant discharge pulse current (typically 10 seconds) (For example, 1C, i.e., 15A, must be within the allowable range of the equipment); sufficient settling time (e.g., 1 hour) is required before and after the pulse to ensure voltage stability;

[0075] (3) Internal resistance calculation:

[0076] Record the voltage jump at the moment the pulse begins Ohmic internal resistance The value is obtained from the ratio of instantaneous voltage difference to pulse current:

[0077] The pure ohmic impedance characterizing a battery includes current collectors, electrode materials, electrolyte, and contact resistance.

[0078] (4) Relationship fitting:

[0079] Obtain at different SOC points After setting the value, the fitting formula is used. , where a, b, c are exponential fitting coefficients, a and b are exponential terms, and c is a constant term; establish an Rs-SOC lookup table or function; typically, Rs increases significantly at low SOC.

[0080] Step 2, perform SOC estimation online in real time:

[0081] The online system relies on a basic database and adopts an architecture that operates in both static and dynamic modes and has two levels of compensation and calibration.

[0082] Step 21: Perform mode determination and static OCV calibration module;

[0083] Trigger condition setting: The system continuously monitors the load current. If the following conditions are met: (For example, 0.05C, i.e., 1.5A) for a duration exceeding (For example, after 30 minutes), the battery is determined to have entered a quasi-static state, triggering the OCV calibration mode; where... It is the current threshold used to determine whether the battery has entered a resting state. The corresponding time threshold is used to determine whether the battery has entered a quasi-static state.

[0084] OCV acquisition: Read the current stable terminal voltage. ;

[0085] Lookup table and assignment: Input the OCV-SOC lookup table to obtain the value through table lookup or interpolation. ;

[0086] Error zeroing: Immediately reset the cumulative SOC value SOC_ah of the ampere-hour integration algorithm to the corresponding value. The value is SOC_ah = SOC_ocv; this operation can eliminate the historical accumulated error of the ampere-hour integration.

[0087] System output SOC = ;

[0088] Step 22: The dynamic ampere-hour integration main module performs a two-stage compensation calibration algorithm; when the battery is in operation, the system runs on this main module. Its core process is as follows:

[0089] Step 221, Calculation of ampere-hour integral:

[0090] With a fixed period Iterate (e.g., every 1 second);

[0091] ;

[0092] in: This is the result of the ampere-hour integration for this period; This is the average sampling current of the k-th cycle (negative during discharge and positive during charging). Sampling period, unit: seconds; η is coulombic efficiency, which is 1 for ternary lithium batteries during discharge and between 0.995 and 0.998 for charging based on experimental data. This represents the total capacity of the battery pack.

[0093] Step 222, First-level compensation: Real-time dynamic ohmic voltage drop stripping;

[0094] It is used to eliminate instantaneous voltage drops or rises caused by sudden changes in load current. The drops are not related to the state of charge (SOC) but only to the current and internal resistance.

[0095] Specifically, this includes: Internal resistance query: based on the current ampere-hour integration result. Query the Rs-SOC relationship table and interpolate to obtain the currently estimated ohmic internal resistance. Voltage drop calculation: Calculate the current ohmic voltage drop: During discharge <0, A negative number indicates a voltage drop; during discharge... >0, A positive number indicates a voltage rise; voltage compensation: for the measured terminal voltage Compensation is performed to obtain a virtual polarization voltage. ,

[0096] This will cause the electric current to be severely affected. Converted to a more gradual change that better reflects the internal chemical potential of the battery. The voltage drop at the battery terminals was compensated based on the magnitude of the load current.

[0097] Step 223, Second-level calibration: Current adaptive dynamic curve lookup table;

[0098] The purpose of this section is to perform refined SOC calibration to address the differences in polarization voltage under different steady-state currents.

[0099] Specific steps: Current selection: To avoid interference from current fluctuations, use the sliding average value of the load current over the past 30 seconds. As a lookup index; Curve selection: In the family of multi-current V-SOC dynamic curves, select the curve that is consistent with... The two nearest current points and The corresponding two V-SOC curves; bilinear interpolation lookup table:

[0100] First of all, On the corresponding curve, using A low SOC value was found. ;

[0101] Secondly, in On the corresponding curve, using A high SOC value was found. ;

[0102] Finally, linear interpolation is performed on the current axis to obtain the final voltage calibration value. :

[0103] ;

[0104] Step 224, Adaptive Data Fusion and Final Output; Strategy: Instead of using fixed weights, dynamically adjust the ampere-hour integral results according to the operating conditions. With voltage calibration value The confidence level.

[0105] Step 2241, Weight Calculation:

[0106] Define weighting factors Weight and Weight ,and ;

[0107] Calculate the current stability factor ;

[0108] The current stability factor measures the degree of recent fluctuation in load current; the more stable the current, the better. The higher the reliability (based on steady-state curve calibration);

[0109] ;

[0110] in, It is the standard deviation of the absolute value of the load current |I| over the past N sampling periods (e.g., N=30, representing the past 30 seconds); it directly quantifies the magnitude of the current fluctuation. This is the attenuation coefficient, used for adjustment. Sensitivity to current fluctuations The value is 2.0;

[0111] Calculate the voltage change rate factor Voltage change rate factor measures the voltage at the compensation back-end. The region on the SOC-OCV curve; in the voltage flat region, the voltage is not sensitive to changes in SOC. Low resolution, low reliability:

[0112] ;

[0113] in, for The absolute value of the instantaneous rate of change (unit: mV / s); The preset optimal center value of voltage change rate (value: = 0.5 mV / %SOC); The parameter used to control the curve width (δ is set to 0.2) determines the tolerance range for the ideal rate of change.

[0114] Calculate the overall reliability of voltage calibration values and final weights:

[0115] Overall credibility: ; The value range is [0, 1]; it comprehensively reflects the current working conditions. The reliability of the information.

[0116] Dynamic weight allocation: , ; It is an adjustable gain coefficient, 0 < ≤ 1, value = 0.8;

[0117] Step 2242, complete fusion output and protection logic:

[0118] Set boundary protection (primary judgment): Before merging, perform a rationality check; if Then determine Unreliable due to voltage fluctuations, abnormal operating conditions, or mismatch in the curve library; trigger protection logic: Output: SOC = ;

[0119] Dynamic weighted fusion: If Then perform dynamic fusion:

[0120] .

[0121] Step 3, calculate the remaining time;

[0122] In achieving high precision Then, calculate the remaining available time. :

[0123] ;

[0124] in, This represents the average discharge current; if it's for charging, calculate the full charge time.

[0125] Real-time forecast: when It measures the current instantaneous discharge current; suitable for relatively stable load conditions, and offers rapid response.

[0126] Trend forecast: When It takes the moving average of the discharge current over a period of time (e.g., 10 minutes); it is suitable for operating conditions with periodic load fluctuations, and the prediction results are smooth and stable.

[0127] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for estimating the SOC and remaining time of a lithium battery by combining dynamic voltage drop compensation and adaptive current calibration, characterized in that: The method includes: Step 1: Conduct offline experiments and build a basic database. The basic database includes standard OCV-SOC relationship curve calibration, multi-current V-SOC dynamic curve family calibration, and ohmic internal resistance Rs-SOC relationship calibration. Step 1 includes: Step 11, standard OCV-SOC relationship curve calibration; Step 12, calibration of the family of dynamic V-SOC curves for multiple currents; Step 13, calibration of the ohmic internal resistance Rs-SOC relationship; Step 2: Perform online real-time SOC estimation, mode determination and static OCV calibration, dynamic ampere-hour integration main, and two-stage compensation calibration; Step 2 includes: Step 21: Perform mode determination and static OCV calibration; Step 22: Perform two-stage compensation calibration using dynamic ampere-hour integration; Step 3: Calculate the remaining time.

2. The method according to claim 1, characterized in that, Step 11, standard OCV-SOC relationship curve calibration, including: (1) Environmental control: The experiment was conducted in a temperature-controlled chamber at a standard temperature of 25±0.5°C. (2) Charge and discharge preparation: Using high-precision charging and discharging equipment, the battery pack is charged at a constant current of 0.05C to the full charge cutoff voltage, and then switched to constant voltage charging until the current drops below 0.01C, ensuring that the battery reaches 100% SOC. (3) Discharge and relaxation: The discharge is performed at a constant current of 0.05C; after discharging 5% of the rated capacity, the discharge is stopped and the system enters the resting relaxation stage. (4) Determination of relaxation endpoint and data recording: During the resting period, continuously monitor the terminal voltage; when the voltage change rate dV / dt is less than 0.1mV / min for 5 consecutive minutes, it is determined that the voltage has stabilized to the true OCV; record the voltage at this time and the corresponding cumulative discharged charge; (5) Curve fitting: After obtaining all discrete points, piecewise, high-order polynomial fitting is used to generate an OCV-SOC lookup table that can be quickly queried in the BMS.

3. The method according to claim 2, characterized in that, Step 12, calibration of the multi-current V-SOC dynamic curve family, including: (1) Selection of current point: Cover typical operating currents while also considering curve interpolation requirements; select at least 6 points; (2) Experimental procedure: For a fully charged battery, sequentially apply each selected constant current. Discharge to the cutoff voltage; before each discharge, the battery must be allowed to stand until the voltage stabilizes to ensure an initial SOC of 100%; (3) Synchronous data acquisition: During the discharge process, at a frequency of not less than 1Hz, the following data are synchronously and precisely acquired: timestamp t, terminal voltage. Discharge current ; (4) Data processing and curve generation: Integrate the current to calculate the real-time discharge capacity. ; Calculate real-time SOC: ; This represents the total capacity of the battery pack. Data organization For each discharge current Generate a dynamic terminal voltage-state of charge curve and store it as a lookup table; Key observation: Comparing the curves of different currents, at the same SOC, the larger the current, the lower the terminal voltage. The difference includes ohmic voltage drop and polarization voltage drop.

4. The method according to claim 3, characterized in that, Step 13, calibration of the ohmic internal resistance Rs-SOC relationship, including: The ohmic internal resistance Rs-SOC is used for online real-time compensation of ohmic voltage drop; the internationally recognized hybrid pulse power characteristic test method is adopted. (1) SOC point settings: Select 8-10 points evenly within the battery's usable SOC range; (2) Pulse test: At a predetermined SOC point, a continuous pulse duration is applied to the battery. constant discharge pulse current Sufficient settling time is required before and after the pulse to ensure voltage stability. (3) Internal resistance calculation: Record the voltage jump at the moment the pulse begins Ohmic internal resistance The value is obtained from the ratio of instantaneous voltage difference to pulse current: The pure ohmic impedance characterizing a battery includes current collectors, electrode materials, electrolyte, and contact resistance. (4) Relationship fitting: Obtain at different SOC points After setting the value, the fitting formula is used. , where a, b, c are exponential fitting coefficients, a and b are exponential terms, and c is a constant term; establish an Rs-SOC lookup table or function.

5. The method according to claim 4, characterized in that, Step 21, perform mode determination and static OCV calibration; including: Trigger condition setting: The system continuously monitors the load current. If the following conditions are met: The duration exceeded If the battery enters a semi-static state, the OCV calibration mode is triggered; It is the current threshold used to determine whether the battery has entered a resting state. The corresponding time threshold is used to determine whether the battery has entered a quasi-static state. OCV acquisition: Read the current stable terminal voltage. ; Lookup table and assignment: Input the OCV-SOC lookup table to obtain the value through table lookup or interpolation. ; Error zeroing: Immediately reset the cumulative SOC value SOC_ah of the ampere-hour integration algorithm to the corresponding value. The value is SOC_ah = SOC_ocv; System output SOC = .

6. The method according to claim 5, characterized in that, Step 22, Dynamic Ah Integration, Performing Two-Stage Compensation Calibration; including: Step 221, Ah Integration Calculation: With a fixed period Perform iterations; ； in: This is the result of the ampere-hour integration for this period; The average sampling current of the k-th period, Sampling period, unit: seconds; η is coulombic efficiency, which is 1 for ternary lithium batteries during discharge and between 0.995 and 0.998 for charging based on experimental data. This represents the total capacity of the battery pack. Step 222, First-level compensation: Real-time dynamic ohmic voltage drop stripping; It is used to eliminate instantaneous voltage drops or rises caused by sudden changes in load current. The drops are not related to the state of charge (SOC) but only to the current and internal resistance. Internal resistance query: Based on the current ampere-hour integration result Query the Rs-SOC relationship table and interpolate to obtain the currently estimated ohmic internal resistance. Voltage drop calculation: Calculate the current ohmic voltage drop: During discharge <0, A negative number indicates a voltage drop; during discharge... >0, A positive number indicates a voltage rise; voltage compensation: for the measured terminal voltage Compensation is performed to obtain a virtual polarization voltage. , This will cause the electric current to be severely affected. Converted to a more gradual change that better reflects the internal chemical potential of the battery. The voltage drop at the battery terminals was compensated based on the magnitude of the load current. Step 223, Second-level calibration: Current adaptive dynamic curve lookup table; Fine-grained SOC calibration is performed to address the differences in polarization voltage under different steady-state currents. Current selection: To avoid interference from current fluctuations, the moving average of the load current over the past 30 seconds is used. As a lookup index; Curve selection: In the family of multi-current V-SOC dynamic curves, select the curve that is consistent with... The two nearest current points and The corresponding two V-SOC curves; bilinear interpolation lookup table: First of all, On the corresponding curve, using A low SOC value was found. ; Secondly, in On the corresponding curve, using A high SOC value was found. ; Finally, linear interpolation is performed on the current axis to obtain the final voltage calibration value. : ； Step 224, Adaptive data fusion and final output; dynamically adjust the ampere-hour integral result according to the operating conditions. With voltage calibration value Confidence level; Step 2241, Weight Calculation: Define weighting factors Weight and Weight ,and ; Calculate the current stability factor ; The current stability factor measures the degree of recent fluctuation in load current; the more stable the current, the better. The higher the credibility; ； in, It represents the standard deviation of the absolute value of the load current |I| over the past N sampling periods; it directly quantifies the magnitude of current fluctuation. This is the attenuation coefficient, used for adjustment. Sensitivity to current fluctuations The value is 2.0; Calculate the voltage change rate factor Voltage change rate factor measures the voltage at the compensation back-end. The region on the SOC-OCV curve; in the voltage flat region, the voltage is not sensitive to changes in SOC. Low resolution, low reliability: ； in, for The absolute value of the instantaneous rate of change; The preset optimal center value of voltage change rate; The parameters used to control the curve width determine the tolerance range for the ideal rate of change; Calculate the overall reliability of voltage calibration values and final weights: Overall credibility: ; The value range of is [0, 1]; Dynamic weight allocation: , ; It is an adjustable gain coefficient, 0 < ≤ 1, value = 0.8; Step 2242, complete fusion output and protection logic: Set boundary protection: Before merging, perform a rationality check; if Then determine Unreliable due to voltage fluctuations, abnormal operating conditions, or mismatch in the curve library; triggers protection logic: Output: SOC = ; Dynamic weighted fusion: If Then perform dynamic fusion: 。 7. The method according to claim 6, characterized in that, Step 3, calculate the remaining time; include: In achieving high precision Then, calculate the remaining available time. : ； in, This represents the average discharge current; if it's for charging, calculate the full charge time. Real-time forecast: when It measures the current instantaneous discharge current; suitable for relatively stable load conditions, and offers rapid response. Trend forecast: When It takes the moving average of the discharge current over a period of time; it is suitable for operating conditions with periodic load fluctuations, and the prediction results are smooth and stable.

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