A high-precision soc estimation method for a battery management system

Abstract

The application relates to the technical field of a battery management system, in particular to a high-precision SOC estimation method of a battery management system, which comprises the following steps: collecting and filtering and calibrating the voltage, current and temperature parameters of a battery in real time; extracting effective data from the pretreated data to construct a multi-parameter dynamic fusion model; adopting an ampere-hour integral method and voltage correction weighted fusion to calculate a state of charge value; performing full-temperature-range correction through a temperature compensation module; performing dynamic aging compensation based on an aging correction module to output a final high-precision SOC estimation value. Through four-dimensional parameter dynamic fusion of voltage, current, temperature and pressure and self-adaptive weight adjustment, combined with full-temperature-range compensation and a double-path aging correction mechanism, the application realizes an estimation accuracy of 3% or less in a full-temperature range, effectively solves the industry problems of SOC overestimation at low temperature and SOC underestimation at high temperature, and has excellent expansibility and universality, and can be applied to electric vehicles and various new energy vehicles.

Description

Technical Field

[0001] This invention belongs to the field of battery management technology, specifically relating to a high-precision SOC estimation method for battery management systems. Background Technology

[0002] The Battery Management System (BMS), as the core control unit of the power battery in new energy vehicles, undertakes key functions such as battery state monitoring, safety protection, equalization management, and range estimation. Among these, accurate estimation of the battery's State of Charge (SOC) is one of the core technologies of battery management, directly affecting the accuracy of the electric vehicle's range display, battery lifespan, and overall vehicle safety performance. Accurate SOC estimation can effectively prevent overcharging and over-discharging, optimize energy utilization efficiency, and provide users with reliable travel assurance. However, due to the highly nonlinear nature of battery electrochemical reactions, the complexity of the charging and discharging process, and the variability of operating conditions, high-precision SOC estimation has always been a core challenge in the field of battery management system technology.

[0003] Traditional SOC estimation methods mainly include the ampere-hour integration method, the open-circuit voltage method, and estimation methods based on battery equivalent models. The ampere-hour integration method calculates the SOC value by integrating the battery's charge and discharge current. While simple in principle, it suffers from cumulative errors and is highly dependent on the initial SOC value, leading to continuous error accumulation over long-term operation. The open-circuit voltage method estimates SOC using the correlation between the battery's open-circuit voltage and SOC, but this method requires the battery to rest for a considerable period to reach equilibrium, making it unsuitable for real-time estimation in electric vehicles. Estimation methods based on equivalent circuit models estimate SOC by establishing an equivalent circuit model of the battery and utilizing the relationship between model parameters and SOC. However, model parameters are significantly affected by factors such as temperature, aging, and charge / discharge rates, resulting in limited parameter identification accuracy. Traditional SOC estimation methods mainly include the following steps: 1. Parameter Acquisition: Acquiring real-time operating parameters of the battery, such as voltage, current, and temperature. 2. Model Construction: Establishing an equivalent circuit model or equivalent electrochemical model of the battery to describe its external characteristics. 3. Parameter Identification: Identifying model parameters online based on the acquired real-time data. IV. SOC Calculation: The SOC value is calculated using the identified model parameters and corresponding algorithms. However, traditional methods often struggle to guarantee accuracy and adaptability when dealing with the nonlinear characteristics and complex operating conditions of batteries.

[0004] Traditional SOC estimation methods exhibit significant limitations in practical applications. First, they fail to accurately describe the dynamic characteristics of batteries, especially under complex operating conditions such as frequent start-stop cycles, acceleration, and deceleration in electric vehicles, where current fluctuations are drastic, leading to substantial cumulative errors in the ampere-hour integration method. Second, they insufficiently consider battery aging factors. As battery usage time increases, capacity decay and changes in internal resistance cause model parameter drift, further reducing estimation accuracy. Third, traditional methods are highly sensitive to changes in ambient temperature. Low temperatures significantly alter the electrochemical reaction kinetics of batteries, leading to a marked increase in estimation errors. Therefore, existing traditional SOC estimation methods are insufficient to meet the urgent needs of new energy vehicles for high-precision, high-reliability SOC estimation. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides a high-precision SOC estimation method for a battery management system, comprising the following steps:

[0006] The battery's voltage, current, and temperature parameters are collected in real time, and the collected data is filtered and calibrated to obtain the effective values ​​of the processed parameters.

[0007] Effective data is extracted from the processed parameter values ​​and used as input parameters for SOC estimation. Based on the extracted effective data, a multi-parameter dynamic fusion model is constructed, and a weighting matrix for four-dimensional parameters of voltage, current, temperature, and pressure is established.

[0008] The weighting coefficients of each parameter are dynamically adjusted according to the current operating conditions. The comprehensive feature value after fusion is calculated by weighted fusion algorithm. Based on the multi-parameter dynamic fusion model, the current state of charge (SOC) value of the battery is calculated. The SOC change is calculated by ampere-hour integration method. The ampere-hour integration result is weighted and fused with the voltage correction result to obtain the preliminary SOC value.

[0009] Based on the temperature compensation module, the state of charge value is corrected over the entire temperature range, and the temperature compensation amount is calculated; the temperature compensation amount is superimposed on the initial SOC value to obtain the temperature-corrected SOC value.

[0010] Based on the aging correction module, dynamic aging compensation is performed on the corrected SOC value, the current cycle number and cumulative charge and discharge capacity of the battery are obtained, the state of health (SOH) value of the battery is calculated based on the cycle number and capacity decay rate, and the aging compensation amount is calculated.

[0011] The aging compensation is added to the temperature-corrected SOC value to obtain the final high-precision SOC estimate.

[0012] Optionally, the voltage, current, and temperature parameters of the battery are acquired in real time, specifically including the following steps: acquiring the battery terminal voltage through a voltage detection circuit to obtain a voltage sampling sequence; acquiring the battery charging and discharging current through a current detection circuit to obtain a current sampling sequence; acquiring the temperature at key locations of the battery pack through a temperature detection circuit to obtain a temperature sampling sequence; wherein the sampling frequency for voltage and current is 100 Hz, and the sampling frequency for temperature is 1 Hz; the processed effective values ​​of the parameters include , , ;in This represents the processed effective voltage value. This represents the processed effective value of the current. This indicates the effective temperature value after processing. The transfer function represents the filtering algorithm. This represents the calibration compensation coefficient.

[0013] Optionally, the change in SOC can be calculated using the ampere-hour integral method. , ,in Indicates the rated capacity. This represents the charge / discharge efficiency coefficient. Indicates real-time current. The integration time is represented; the ampere-hour integration result is weighted and fused with the voltage correction result to obtain the preliminary SOC value. ,in Indicates voltage correction weight. Indicates the integral weight of ampere-hours. This represents the initial SOC value after fusion.

[0014] Optionally, the construction of the multi-parameter dynamic fusion model further includes the following steps: establishing a nonlinear mapping relationship between SOC and each parameter; determining the current battery operating state through a real-time operating condition identification algorithm; and adaptively adjusting the coefficients of the mapping relationship according to the operating state to achieve adaptive calibration of the multi-parameter dynamic fusion.

[0015] Optionally, based on the temperature compensation module, the state of charge value is corrected across the entire temperature range, and the temperature compensation amount is calculated. ,in Indicates the temperature compensation coefficient. Indicates the current temperature. Indicates reference temperature. Indicates the capacity temperature coefficient;

[0016] The temperature compensation is added to the initial SOC value to obtain the temperature-corrected SOC value. ;

[0017] Aging compensation ,in Indicates the aging compensation coefficient. This indicates the current SOH value. Indicates the initial SOH value. Indicates the baseline capacity.

[0018] Optionally, the charge / discharge efficiency coefficient is dynamically adjusted according to temperature, specifically including the following steps: when the temperature is below zero degrees Celsius, the charge / discharge efficiency coefficient is set to 0.98; when the temperature is in the range of zero degrees Celsius to fifty-five degrees Celsius, the charge / discharge efficiency coefficient increases linearly from 0.98 to 1.02 as the temperature rises; when the temperature is above fifty-five degrees Celsius, the charge / discharge efficiency coefficient remains at 1.02.

[0019] Optionally, full-temperature-range correction can be performed based on the temperature compensation module, specifically including the following steps: establishing a dynamic mapping model between temperature and battery capacity; setting the capacity temperature coefficient to a negative 0.1% per degree Celsius when the temperature is below zero degrees Celsius; setting the capacity temperature coefficient to a positive 0.05% per degree Celsius when the temperature is between zero and fifty-five degrees Celsius; obtaining the capacity compensation coefficient at different temperature points through experimental calibration and constructing a Lookup table; and achieving accurate capacity compensation across the entire temperature range.

[0020] Optionally, the current cycle count and cumulative charge / discharge capacity of the battery can be obtained, specifically including the following steps: querying the cumulative number of completed charge / discharge cycles through the battery usage record; calculating the cumulative ampere-hour throughput of the battery to obtain the cumulative charge / discharge capacity; setting the SOH parameter to be updated once every 100 cycles; and establishing a correspondence table between the number of cycles and the capacity decay rate.

[0021] Optionally, the State of Health (SOH) value of the battery is calculated based on the number of cycles and the capacity decay rate, specifically including the following steps: extracting battery aging characteristic parameters, including parameters of the number of cycles and parameters of the capacity decay rate; assigning fusion weights to the parameters of the two dimensions, setting the weight of the time dimension to 60% and the weight of the operating condition dimension to 40%; and calculating the composite SOH value based on the following formula: ,in This represents the SOH value over the time dimension. Represents the SOH value under operating conditions; implements a dual-path aging correction mechanism; provides high-precision SOC estimation. Output the final high-precision SOC estimate.

[0022] Optionally, a periodic voltage calibration step may also be included, which specifically includes the following steps: determining whether the battery meets the resting condition, i.e., the current is less than 0.5 amperes and the duration exceeds 30 minutes; when the resting condition is met, the open-circuit voltage (OCV) of the battery is collected; the pre-established SOC-OCV correspondence table is consulted to obtain the calibration reference SOC value; the calibration reference SOC value is weighted and fused with the current estimated SOC value to complete the voltage calibration.

[0023] Compared with the prior art, the present invention has at least the following technical effects:

[0024] This invention overcomes the limitations of single-parameter dependence by using a multi-parameter dynamic fusion algorithm. It integrates four-dimensional parameters—voltage, current, temperature, and pressure—to establish a dynamic weight adjustment model, achieving an estimation accuracy of less than 3% across the entire temperature range, which is significantly better than the 8% error accuracy of traditional methods.

[0025] This invention constructs a dynamic mapping model between temperature and battery capacity through fully adaptive compensation technology, realizing full-temperature range compensation from -20 degrees Celsius to 55 degrees Celsius. It effectively solves the industry problem of SOC overestimation under low temperature conditions and SOC underestimation under high temperature conditions, while supporting dynamic switching of charge and discharge rates in the range of 0.5C to 2C.

[0026] This invention achieves a comprehensive consideration of battery aging factors by combining the time dimension of cycle count and the operating condition dimension of capacity decay rate through a dual-path aging correction mechanism. It breaks through the limitation of traditional SOH estimation relying solely on the number of cycles, improves the accuracy of aging correction by 40%, and effectively suppresses the accumulation of errors during long-term use.

[0027] The algorithm architecture of this invention can be extended to different battery types and application scenarios, including new energy transportation vehicles such as electric vehicles, mining vehicles, and mobile charging vehicles. At the same time, the algorithm has good compatibility and is applicable to various types of battery chemistry systems, providing a technical foundation for diverse industrial applications. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the overall process of the SOC estimation method based on power batteries;

[0030] Figure 2 This is a block diagram of the multi-parameter collaborative estimation module in a battery management system. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only possible technical implementations of the present invention and not all possible implementations. Those skilled in the art can obtain other embodiments in conjunction with the embodiments of the present invention without creative effort, and these embodiments are also within the protection scope of the present invention.

[0032] Example 1

[0033] Reference Figure 1 This embodiment provides a high-precision battery management system. The estimation method first involves real-time acquisition of battery voltage, current, and temperature parameters, followed by filtering and calibration of the acquired data. Then, valid data is extracted from the preprocessed data as... The system estimates the input parameters; then, based on the extracted effective data, it constructs a multi-parameter dynamic fusion model, calculates the current state of charge (SOC) value of the battery based on the multi-parameter dynamic fusion model; then, it performs full-temperature range correction on the SOC value based on a temperature compensation module, and then performs dynamic aging compensation on the corrected SOC value based on an aging correction module; finally, it outputs the final high-precision value. Estimated value.

[0034] Specifically, it consists of the following six steps:

[0035] S1 Data Acquisition and Preprocessing In this invention, we first collect the battery's voltage, current, and temperature parameters in real time, specifically including the following steps: acquiring the battery's terminal voltage through a voltage detection circuit to obtain a voltage sampling sequence; acquiring the battery's charging and discharging current through a current detection circuit to obtain a current sampling sequence; and acquiring the temperature at key locations in the battery pack through a temperature detection circuit to obtain a temperature sampling sequence; wherein the sampling frequency of voltage and current is 100 Hz, and the sampling frequency of temperature is 1 Hz.

[0036] The collected data was filtered and calibrated to obtain:

[0037]

[0038]

[0039]

[0040] in This represents the processed effective voltage value. This represents the processed effective value of the current. This indicates the effective temperature value after processing. The transfer function represents the filtering algorithm. This represents the calibration compensation coefficient.

[0041] Based on the collected preprocessed data, valid data is extracted as... The input parameters for estimation.

[0042] S2 constructs a multi-parameter dynamic fusion model, establishing a weight allocation matrix for four-dimensional parameters: voltage, current, temperature, and pressure. The weight coefficients of each parameter are dynamically adjusted according to the current operating conditions, and the fused comprehensive characteristic value is calculated using a weighted fusion algorithm.

[0043]

[0044] in Indicates the first The weighting coefficients of each parameter, Indicates the first The collected values ​​of each parameter, This represents the combined eigenvalues ​​after fusion.

[0045] Establish The nonlinear mapping relationship between the parameters is used to determine the current working state of the battery through a real-time working condition identification algorithm. The coefficients of the mapping relationship are adaptively adjusted according to the working state to achieve adaptive calibration of multiple parameters through dynamic fusion.

[0046] S3 calculates the current state of charge using the ampere-hour integral method. The change is calculated based on the following formula:

[0047]

[0048]

[0049] in Indicates the rated capacity. This represents the charge / discharge efficiency coefficient. Indicates real-time current. Indicates the integration time.

[0050] The ampere-hour integration result and the voltage correction result are weighted and fused to obtain a preliminary result. value:

[0051]

[0052] in Indicates voltage correction weight. Indicates the integral weight of ampere-hours. Indicates the initial stage after fusion value.

[0053] The charge / discharge efficiency coefficient is dynamically adjusted according to temperature, specifically including the following steps: when the temperature is below zero degrees Celsius, the charge / discharge efficiency coefficient is set to 0.98; when the temperature is in the range of zero to fifty-five degrees Celsius, the charge / discharge efficiency coefficient increases linearly from 0.98 to 1.02 as the temperature rises; when the temperature is above fifty-five degrees Celsius, the charge / discharge efficiency coefficient remains at 1.02.

[0054] S4 Temperature Compensation Correction

[0055] Based on the temperature compensation module, the state of charge value is corrected across the entire temperature range. A dynamic mapping model between temperature and battery capacity is established. The corresponding capacity compensation coefficient is queried based on the current temperature, and the temperature compensation amount is calculated using the following formula:

[0056]

[0057] in Indicates the temperature compensation coefficient. Indicates the current temperature. Indicates reference temperature. This indicates the capacity temperature coefficient.

[0058] The temperature compensation is added to the initial SOC value to obtain the temperature-corrected SOC value:

[0059]

[0060] A dynamic mapping model between temperature and battery capacity is established, which includes the following steps: when the temperature is below zero degrees Celsius, the capacity temperature coefficient is set to a negative 0.1 percent per degree Celsius; when the temperature is between zero and fifty-five degrees Celsius, the capacity temperature coefficient is set to a positive 0.05 percent per degree Celsius; the capacity compensation coefficient at different temperature points is obtained through experimental calibration, and a Lookup table is constructed; accurate capacity compensation is achieved across the entire temperature range.

[0061] S5 Aging Correction Compensation

[0062] Based on the aging correction module, dynamic aging compensation is performed on the corrected SOC value. The current cycle count and cumulative charge / discharge capacity of the battery are obtained. The battery's State of Health (SOH) value is calculated based on the cycle count and capacity decay rate. The aging compensation amount is then calculated using the following formula:

[0063]

[0064] in Indicates the aging compensation coefficient. This indicates the current SOH value. Indicates the initial SOH value. Indicates the baseline capacity.

[0065] The aging compensation is added to the temperature-corrected SOC value to obtain the final high-precision SOC estimate:

[0066]

[0067] Obtaining the current cycle count and cumulative charge / discharge capacity of the battery involves the following steps: querying the cumulative number of completed charge / discharge cycles through the battery usage record; calculating the cumulative ampere-hour throughput of the battery to obtain the cumulative charge / discharge capacity; setting the SOH parameter to be updated every 100 cycles; and establishing a correspondence table between the number of cycles and the capacity decay rate.

[0068] The State of Health (SOH) value of the battery is calculated based on the number of cycles and the capacity decay rate, specifically including the following steps: extracting battery aging characteristic parameters, including parameters of the number of cycles and parameters of the capacity decay rate; assigning fusion weights to the parameters of the two dimensions, setting the weight of the time dimension to 60% and the weight of the operating condition dimension to 40%; and calculating the composite SOH value based on the following formula:

[0069]

[0070] in This represents the SOH value over the time dimension. It represents the SOH value in the operating condition dimension; and implements a dual-path aging correction mechanism.

[0071] S6 Data Storage and Communication

[0072] It outputs the final high-precision SOC estimate, saves complete battery status data every five minutes, including SOC, SOH, voltage, current, and temperature; reports SOC data to the vehicle controller (VCU) via the CAN bus at 100-millisecond intervals; and supports data reading from external diagnostic devices via the RS485 interface.

[0073] As another embodiment of the present invention, combined with Figures 1 to 2 As shown, the high-precision SOC estimation method for the battery management system in this embodiment mainly includes the following steps:

[0074] First, the battery's voltage, current, and temperature parameters are acquired in real time, and the acquired data is filtered and calibrated. Specifically, this includes acquiring the battery's terminal voltage through a voltage detection circuit, acquiring the battery's charging and discharging current through a current detection circuit, and acquiring the temperature at key locations in the battery pack through a temperature detection circuit. Then, valid data is extracted from the acquired preprocessed data and used as input parameters for SOC estimation.

[0075] Subsequently, based on the extracted effective data, a multi-parameter dynamic fusion model was constructed. A weighting matrix for four-dimensional parameters—voltage, current, temperature, and pressure—was established. The weight coefficients of each parameter were dynamically adjusted according to the current operating conditions, and the fused comprehensive feature value was calculated using a weighted fusion algorithm. Simultaneously, a nonlinear mapping relationship between SOC and each parameter was established. The current battery operating state was determined using a real-time operating condition identification algorithm, and the coefficients of the mapping relationship were adaptively adjusted based on the operating state.

[0076] Based on the aforementioned multi-parameter dynamic fusion model, the current state of charge (SOC) value of the battery is calculated. The SOC change is calculated using the ampere-hour integral method, and the ampere-hour integral result is weighted and fused with the voltage correction result to obtain a preliminary SOC value. The charge / discharge efficiency coefficient is dynamically adjusted according to temperature: it is set to 0.98 when the temperature is below 0 degrees Celsius, linearly increases from 0.98 to 1.02 when the temperature is between 0 and 55 degrees Celsius, and remains at 1.02 when the temperature is above 55 degrees Celsius.

[0077] Then, the state of charge (SOC) value is corrected across the entire temperature range based on the temperature compensation module. A dynamic mapping model between temperature and battery capacity is established. The corresponding capacity compensation coefficient is queried based on the current temperature, the temperature compensation amount is calculated, and it is added to the initial SOC value. When the temperature is below zero degrees Celsius, the capacity temperature coefficient is negative 0.1 percent per degree Celsius, and when the temperature is between zero and fifty-five degrees Celsius, it is positive 0.05 percent per degree Celsius.

[0078] Then, dynamic aging compensation is performed on the corrected SOC value based on the aging correction module. The current cycle count and cumulative charge / discharge capacity of the battery are obtained. The SOH value of the battery is calculated based on the cycle count and capacity decay rate. The aging compensation amount is calculated and added to the temperature-corrected SOC value. Battery aging characteristic parameters are extracted, including parameters from the cycle count dimension and the capacity decay rate dimension. Fusion weights are assigned to the parameters in both dimensions, with a 60% weight for the time dimension and a 40% weight for the operating condition dimension, thus implementing a dual-path aging correction mechanism.

[0079] Finally, it outputs the final high-precision SOC estimate, saves complete battery status data every five minutes, reports SOC data to the vehicle controller (VCU) via the CAN bus at a 100-millisecond cycle, and supports data reading from external diagnostic devices via the RS485 interface.

[0080] This invention also includes a periodic voltage correction step, specifically comprising the following steps: determining whether the battery meets the resting condition, i.e., the current is less than 0.5 amperes and the duration exceeds 30 minutes; when the resting condition is met, acquiring the battery's open-circuit voltage (OCV); querying a pre-established SOC-OCV correspondence table to obtain a correction reference SOC value; and weighting and fusing the correction reference SOC value with the current estimated SOC value to complete the voltage correction.

[0081] This application primarily addresses the problems of low accuracy, poor environmental adaptability, and insufficient aging correction in existing battery management system (BMS) SOC estimation methods. The core idea of ​​this invention is to overcome the limitations of single-parameter dependence through a multi-parameter dynamic fusion algorithm, fusing four-dimensional parameters—voltage, current, temperature, and pressure—to establish a dynamic weight adjustment model, achieving high-precision estimation across the entire temperature range. Simultaneously, a fully adaptive compensation technique is used to construct a dynamic mapping model between temperature and battery capacity, achieving full-temperature range compensation from -20°C to 55°C. A dual-path aging correction mechanism combines the time dimension of cycle count and the operating condition dimension of capacity decay rate to comprehensively consider battery aging factors. This method significantly improves SOC estimation accuracy, effectively solving the industry-wide problems of overestimation of SOC under low-temperature conditions and underestimation under high-temperature conditions, while also suppressing error accumulation during long-term use.

[0082] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A high-precision SOC estimation method for a battery management system, characterized in that, Includes the following steps: The battery's voltage, current, and temperature parameters are collected in real time, and the collected data is filtered and calibrated to obtain the effective values ​​of the processed parameters. Extract valid data from the processed parameter values ​​and use it as input parameters for SOC estimation; Based on the extracted effective data, a multi-parameter dynamic fusion model is constructed, and a weight allocation matrix for four-dimensional parameters of voltage, current, temperature, and pressure is established. The weighting coefficients of each parameter are dynamically adjusted according to the current operating conditions. The comprehensive feature value after fusion is calculated by weighted fusion algorithm. Based on the multi-parameter dynamic fusion model, the current state of charge (SOC) value of the battery is calculated. The SOC change is calculated by ampere-hour integration method. The ampere-hour integration result is weighted and fused with the voltage correction result to obtain the preliminary SOC value. Based on the temperature compensation module, the state of charge value is corrected over the entire temperature range, and the temperature compensation amount is calculated; the temperature compensation amount is superimposed on the initial SOC value to obtain the temperature-corrected SOC value. Based on the aging correction module, dynamic aging compensation is performed on the corrected SOC value, the current cycle number and cumulative charge and discharge capacity of the battery are obtained, the state of health (SOH) value of the battery is calculated based on the cycle number and capacity decay rate, and the aging compensation amount is calculated. The aging compensation is added to the temperature-corrected SOC value to obtain the final high-precision SOC estimate.

2. The high-precision SOC estimation method for battery management systems according to claim 1, characterized in that, Real-time acquisition of battery voltage, current, and temperature parameters includes the following steps: acquiring the battery terminal voltage through a voltage detection circuit to obtain a voltage sampling sequence; acquiring the battery charging and discharging current through a current detection circuit to obtain a current sampling sequence; and acquiring the temperature at key locations in the battery pack through a temperature detection circuit to obtain a temperature sampling sequence. The sampling frequency for voltage and current is 100 Hz, and the sampling frequency for temperature is 1 Hz. The processed effective values ​​of the parameters include... , , ;in This represents the processed effective voltage value. This represents the processed effective value of the current. This indicates the effective temperature value after processing. The transfer function represents the filtering algorithm. This represents the calibration compensation coefficient.

3. The high-precision SOC estimation method for battery management systems according to claim 2, characterized in that, The change in SOC was calculated using the ampere-hour integral method. , ,in Indicates the rated capacity. This represents the charge / discharge efficiency coefficient. Indicates real-time current. The integration time is represented; the ampere-hour integration result is weighted and fused with the voltage correction result to obtain the preliminary SOC value. ,in Indicates voltage correction weight. Indicates the integral weight of ampere-hours. This represents the initial SOC value after fusion.

4. The high-precision SOC estimation method for battery management systems according to claim 3, characterized in that, The construction of the multi-parameter dynamic fusion model also includes the following steps: establishing a nonlinear mapping relationship between SOC and each parameter; determining the current working state of the battery through a real-time operating condition identification algorithm; and adaptively adjusting the coefficients of the mapping relationship according to the working state to achieve adaptive calibration of the multi-parameter dynamic fusion.

5. The high-precision SOC estimation method for battery management systems according to claim 4, characterized in that, Based on the temperature compensation module, the state of charge value is corrected across the entire temperature range, and the temperature compensation amount is calculated. ,in Indicates the temperature compensation coefficient. Indicates the current temperature. Indicates reference temperature. Indicates the capacity temperature coefficient; The temperature compensation is added to the initial SOC value to obtain the temperature-corrected SOC value. ; Aging compensation ,in Indicates the aging compensation coefficient. This indicates the current SOH value. Indicates the initial SOH value. Indicates the baseline capacity.

6. The high-precision SOC estimation method for battery management systems according to claim 1, characterized in that, The charge / discharge efficiency coefficient is dynamically adjusted according to temperature, specifically including the following steps: when the temperature is below zero degrees Celsius, the charge / discharge efficiency coefficient is set to 0.98; when the temperature is in the range of zero degrees Celsius to fifty-five degrees Celsius, the charge / discharge efficiency coefficient increases linearly from 0.98 to 1.02 as the temperature increases; when the temperature is above fifty-five degrees Celsius, the charge / discharge efficiency coefficient remains at 1.

02.

7. The high-precision SOC estimation method for battery management systems according to claim 1, characterized in that, The full-temperature range correction based on the temperature compensation module includes the following steps: establishing a dynamic mapping model between temperature and battery capacity; setting the capacity temperature coefficient to a negative 0.1% per degree Celsius when the temperature is below zero degrees Celsius; setting the capacity temperature coefficient to a positive 0.05% per degree Celsius when the temperature is between zero and fifty-five degrees Celsius; obtaining the capacity compensation coefficient at different temperature points through experimental calibration and constructing a Lookup table; and achieving accurate capacity compensation across the entire temperature range.

8. The high-precision SOC estimation method for battery management systems according to claim 1, characterized in that, Obtaining the current cycle count and cumulative charge / discharge capacity of the battery involves the following steps: querying the cumulative number of completed charge / discharge cycles through the battery usage record; calculating the cumulative ampere-hour throughput of the battery to obtain the cumulative charge / discharge capacity; setting the SOH parameter to be updated every 100 cycles; and establishing a correspondence table between the number of cycles and the capacity decay rate.

9. The high-precision SOC estimation method for battery management systems according to claim 8, characterized in that, The State of Health (SOH) value of the battery is calculated based on the number of cycles and the capacity decay rate, specifically including the following steps: extracting battery aging characteristic parameters, including parameters of the number of cycles and parameters of the capacity decay rate; assigning fusion weights to the parameters of the two dimensions, setting the weight of the time dimension to 60% and the weight of the operating condition dimension to 40%; and calculating the composite SOH value based on the following formula: ,in This represents the SOH value over the time dimension. Represents the SOH value under operating conditions; implements a dual-path aging correction mechanism; provides high-precision SOC estimation. Output the final high-precision SOC estimate.

10. The high-precision SOC estimation method for battery management systems according to claim 1, characterized in that, It also includes a periodic voltage calibration step, which specifically includes the following steps: determining whether the battery meets the resting condition, that is, the current is less than 0.5 amperes and the duration is more than 30 minutes; when the resting condition is met, the open circuit voltage OCV of the battery is collected; the pre-established SOC-OCV correspondence table is consulted to obtain the calibration reference SOC value; the calibration reference SOC value is weighted and fused with the current SOC estimate to complete the voltage calibration.

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