Method for accurately calculating SOC (State of Charge) of lead-acid battery and detecting and protecting polymorphic abnormality of lead-acid battery

Abstract

The invention particularly relates to a lead-acid battery SOC accurate calculation and lead-acid battery polymorphic anomaly detection and protection method, which comprises the following steps: firstly, collecting and preprocessing multi-dimensional data information of a lead-acid battery, and then respectively calculating the preprocessed data based on a preset ampere-hour integral method and an improved extended Kalman filtering method; meanwhile, the battery operation condition is recognized through a preset condition recognition algorithm, and then dynamic weight fusion is conducted on calculation results of the two methods according to the condition to obtain a first fusion result; and then temperature compensation correction is performed on the result by using a preset temperature compensation model, and finally correction is performed in sequence by using a preset internal resistance-SOC relationship and an aging correction model to obtain a first SOC value. Through multi-dimensional data fusion, dynamic working condition weight distribution, temperature compensation and multi-mode cooperative protection, the SOC estimation error of the lead-acid battery is reduced, the safety and environmental adaptability under complex working conditions are improved, and meanwhile, intelligent interaction and low-power-consumption management are supported.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and particularly to a method for accurately calculating the state of charge (SOC) of lead-acid batteries and detecting and protecting multi-state anomalies of lead-acid batteries, which is applicable to lead-acid battery application scenarios such as electric vehicles, energy storage systems, and electric two-wheelers. Background Art

[0002] The existing lead-acid battery management technology has the following core problems:

[0003] Limited accuracy of SOC estimation: The traditional ampere-hour integration method is easily affected by cumulative errors and cannot correct the dynamic effects of temperature and aging on capacity; a single Kalman filter has insufficient anti-interference ability under high-dynamic working conditions, resulting in a large deviation in SOC estimation.

[0004] Single and lagging protection strategy: Overvoltage / overcurrent protection relies on fixed thresholds and does not dynamically adjust in combination with the real-time state of SOC; there is a lack of a differential protection mechanism for complex working conditions such as static leakage and riding short circuits.

[0005] Weak environmental adaptability: An effective temperature compensation model has not been established, and the significant changes in internal resistance and capacity with temperature significantly affect the estimation accuracy; the capacity attenuation caused by battery aging cannot be corrected in real time, further exacerbating the risk of SOC error and protection threshold failure.

[0006] Insufficient intelligent level: The initialization of after-installed devices relies on manual input of parameters, resulting in large errors; there is a lack of remote monitoring and fault warning functions, making it difficult to meet the user's real-time control requirements for battery status. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide a method for accurately calculating the SOC of lead-acid batteries and detecting and protecting multi-state anomalies of lead-acid batteries to solve the technical problems existing in the prior art.

[0008] According to the first aspect of the embodiments of the present invention, a method for accurately calculating the SOC of lead-acid batteries is provided, characterized in that the method includes:

[0009] Collecting multi-dimensional data information of a preset lead-acid battery and preprocessing the multi-dimensional data information; the multi-dimensional data information includes voltage, current, temperature, and internal resistance multi-dimensional data information;

[0010] Calculating the preprocessed multi-dimensional data information respectively based on a preset ampere-hour integration method and an improved extended Kalman filter method, and identifying the battery operating conditions based on a preset threshold according to a preset operating condition identification algorithm;

[0011] Based on the result of the battery operating conditions, dynamically fusing the calculation results of the ampere-hour integration method and the improved extended Kalman filter method to obtain a first fusion result;

[0012] Perform temperature compensation and correction on the first fusion result using a preset temperature compensation model;

[0013] Perform corrections on the result of the temperature compensation and correction in sequence using a preset internal resistance - SOC relationship and a preset aging correction model to obtain a first SOC value.

[0014] Further, the preprocessing of the multi - dimensional data information includes:

[0015] Perform moving average filtering on the collected voltage;

[0016] Perform accuracy calibration on the collected current and record the direction;

[0017] Calculate the internal resistance based on the pulse discharge method in combination with a preset formula, and the preset formula includes:

[0018] Rinternal = ΔIΔV (1)

[0019] where ΔV is the voltage difference before and after the pulse, ΔI is the pulse current amplitude, and Rinternal is the internal resistance.

[0020] Further, calculate the preprocessed multi - dimensional data information respectively based on a preset ampere - hour integration method and an improved extended Kalman filtering method, and identify the battery operating condition based on a preset operating condition identification algorithm and a preset threshold, including:

[0021] Use the preprocessed current to integrate the battery charge - discharge current through a preset ampere - hour integration method to estimate the first state of charge of the battery;

[0022] Use the first state of charge of the battery and preset multi - dimensional data information to estimate the state of charge of the battery in real - time through a preset improved extended Kalman filtering method;

[0023] Use the estimation result of the state of charge of the battery and a preset operating condition identification algorithm to judge the operating condition of the battery according to the operating parameters of the battery and a preset threshold; the operating parameters of the battery include: current, voltage, temperature.

[0024] Further, the use of the preprocessed current to integrate the battery charge - discharge current through a preset ampere - hour integration method to estimate the state of charge of the battery includes:

[0025] Use a preset open - circuit voltage method to calibrate and obtain the initial value of the state of charge of the battery;

[0026] Using the pre - processed current data and the initial value of the state of charge of the battery, the state of charge of the battery is estimated by integrating the battery charge - discharge current through the following formula, and the formula includes:

[0027]

[0028] where Cnom is the nominal capacity, SOC0 represents the initial value of the state of charge of the battery, and η is the coulomb efficiency.

[0029] Furthermore, using the first state of charge of the battery, the state of charge of the battery is estimated in real - time by using the preset multi - dimensional data information through the preset improved extended Kalman filtering method, including:

[0030] Using the first state of charge of the battery, considering the non - linear characteristics of the battery, a discretized state model is established through the following formula;

[0031]

[0032] where Cactual(T) is the actual capacity at temperature T, η is the coulomb efficiency, IK represents the current at time k, SOCK represents the SOC at time k, SOCK - 1 represents the SOC at time k - 1; Δt represents the sampling interval;

[0033] Using the pre - processed voltage, combined with the second - order RC equivalent circuit model, a non - linear relationship between SOC and voltage is established;

[0034] Set the covariance matrices of the initial process noise and observation noise;

[0035] According to the multi - dimensional data information of the preset lead - acid battery, SOC estimation is carried out through the discretized state model and the non - linear relationship between SOC and voltage, and error correction is carried out according to the covariance matrix to estimate the state of charge of the battery in real - time.

[0036] Furthermore, based on the result of the battery operating conditions, the calculation results of the ampere - hour integration method and the improved extended Kalman filtering method are dynamically weighted and fused to obtain the first fusion result, including:

[0037] Under the low - current steady - state condition, the output of the improved extended Kalman filtering method is taken as the main, and the state of charge of the battery is corrected using the voltage and internal resistance data;

[0038] Under the high - dynamic condition, the output of the ampere - hour integration method is preferably taken as the main, and the integration error is calibrated in real - time by the improved extended Kalman filtering method;

[0039] Data synchronization is performed once every preset time, and the first fusion result is output through weighted average.

[0040] Further, the temperature compensation and correction of the first fusion result by using the preset temperature compensation model includes:

[0041] Establish a capacity attenuation model based on the Arrhenius equation to correct the capacity and temperature of the first fusion result;

[0042] Use the curve fitting the relationship between internal resistance and temperature to perform internal resistance temperature compensation on the first fusion result, and the formula includes:

[0043] R internal (T)=R ref ·[1 + α(T - T ref )] (4)

[0044] where, R internal (T): represents the actual internal resistance value of the battery at temperature T; R ref : refers to the internal resistance value of the battery at the reference temperature (usually in °C); α: represents the internal resistance temperature coefficient, reflecting the sensitivity of the internal resistance to temperature changes; T: the currently actually measured battery temperature, in degrees Celsius (°C), which is the input parameter for temperature compensation; T ref : the reference temperature, generally set to °C, as the reference temperature for calculating the change in internal resistance.

[0045] According to the second aspect of the embodiments of the present invention, a method for multi-state abnormal detection and protection of lead-acid batteries is provided, which is applied to the method for accurate calculation of the SOC of lead-acid batteries described in any one of the above, and is characterized by including:

[0046] Charging mode protection, using the first SOC value to monitor the charging current and voltage in real time. When the current exceeds a preset multiple of the rated value and lasts for a preset number of seconds, or the single-cell voltage reaches the threshold, the charging circuit is disconnected through a dual-redundancy protection circuit;

[0047] Riding mode protection, using the first SOC value to monitor the motor load current in real time. When the current increases by more than 50% within a preset number of seconds or exceeds a preset multiple of the rated current of the motor controller, the motor drive MOSFET and the main battery relay are turned off in sequence;

[0048] Static mode protection, using the first SOC value to detect the micro current. If it exceeds a preset number of milliamperes and lasts for a preset number of seconds, an alarm is triggered,

[0049] If it does not recover within a preset number of minutes, the main positive relay of the battery is disconnected.

[0050] Further, the method further includes:

[0051] If the battery is static for more than a preset time period or the SOC drops by more than a threshold per hour, it enters the sleep state and can be woken up within a preset number of seconds through an APP command or vehicle vibration.

[0052] Furthermore, the method further includes:

[0053] Synchronize the SOC, voltage, and fault code to a preset port through a Bluetooth or 4G module.

[0054] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0055] 1. High-precision SOC estimation:

[0056] Through multi-parameter fusion (voltage, current, temperature, internal resistance) and an improved EKF and dynamic weight distribution of ampere-hour integration, the SOC estimation error is controlled within ±5%, which is better than traditional single algorithms.

[0057] 2. Dynamic abnormal protection:

[0058] Charging mode: Adjust the overvoltage threshold in real time in combination with the SOC (for example, reduce the current in advance when the SOC is close to 100%), and the response time of the dual-redundancy protection circuit is <20ms.

[0059] Riding mode: Dynamically set the current baseline based on the SOC, distinguish normal acceleration from short-circuit faults, and cut off abnormal current within 5ms.

[0060] Static mode: Combine micro-current detection with the SOC drop rate to achieve leakage classification protection (alarm → power off).

[0061] 3. Enhanced environmental adaptability:

[0062] The temperature compensation model corrects the capacity and internal resistance, enabling the system to remain stable in a wide temperature range of -20°C to 60°C.

[0063] The aging self-adaptive correction algorithm updates the model parameters through historical data to compensate for the impact of capacity attenuation.

[0064] 4. Intelligent user experience:

[0065] The aftermarket device can quickly converge the SOC estimation and initialization error by inputting basic parameters through the APP.

[0066] Supports Bluetooth / 4G remote monitoring, and real-time synchronization of SOC, fault codes, and historical data to improve the operation and maintenance efficiency.

[0067] 5. Low-power and safety design:

[0068] Automatically enter the sleep state after being static for more than 48 hours, standby power consumption <50μA; supports 1-second wake-up to balance battery life and response speed.

[0069] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0071] Figure 1 is a schematic diagram of the steps of a method for accurately calculating the SOC of a lead-acid battery shown according to an exemplary embodiment;

[0072] Figure 2 is a schematic diagram of the implementation steps of a method for accurately calculating the SOC of a lead-acid battery shown according to an exemplary embodiment;

[0073] Figure 3 is a schematic diagram of different battery usage modes of a method for accurately calculating the SOC of a lead-acid battery shown according to an exemplary embodiment;

[0074] Figure 4 is a schematic diagram of the system architecture for electric vehicle charging management and control signal transmission;

[0075] Figure 5 is a schematic diagram of the architecture of an electric vehicle charging management and control transmission system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0076] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0077] Embodiment 1

[0078] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the steps of a method for accurately calculating the SOC of a lead-acid battery shown according to an exemplary embodiment, and the method includes:

[0079] S1. Collect multi-dimensional data information of a preset lead-acid battery and preprocess the multi-dimensional data information; the multi-dimensional data information includes voltage, current, temperature, and internal resistance multi-dimensional data information;

[0080] S2. Calculate the pre-processed multi-dimensional data information based on the preset ampere-hour integration method and the improved extended Kalman filter method, and identify the battery operating condition based on the preset threshold value based on the preset operating condition identification algorithm;

[0081] S3. Based on the result of the battery operating condition, dynamically weight the calculation results of the ampere-hour integration method and the improved extended Kalman filter method to obtain a first fusion result;

[0082] S4. Performing temperature compensation correction on the first fusion result using a preset temperature compensation model;

[0083] S5. Use a preset internal resistance-SOC relationship and a preset aging correction model to correct the result of the temperature compensation correction in sequence to obtain a first SOC value.

[0084] This embodiment aims to explain in detail the specific implementation process of the lead-acid battery SOC accurate calculation method, which comprehensively uses multi-dimensional data information and cooperates with multiple algorithms and models to improve the accuracy of SOC estimation.

[0085] Specifically, the battery voltage, current, temperature and internal resistance data are collected, and the improved Kalman filter algorithm and the ampere-hour integration method are dynamically integrated, and the SOC value is corrected in combination with the temperature compensation model. Dynamic calibration of internal resistance: Real-time measurement of internal resistance through the pulse discharge method, establishment of an internal resistance-SOC relationship library, and improvement of the accuracy of the low power range (SOC <20%). Preliminary data reference: Use the communication module to set the relevant rated value of the battery to provide a reference model for the preliminary SOC calculation. Historical data learning: Store periodic charge and discharge curves, adaptively correct the battery aging coefficient, and control the error within ±5%.

[0086] The multi-parameter fusion algorithm aims to achieve high-precision estimation of SOC by integrating multi-dimensional data such as voltage, current, temperature and internal resistance of the lead-acid battery, combining the dynamic fusion strategy of the improved Kalman filter (EKF) and the ampere-hour integration method (Ah method), and introducing a temperature compensation model and a basic reference model.

[0087] For specific implementation, please refer to Figure 2 , mainly including the following steps:

[0088] 1. Data collection and preprocessing

[0089] Data collection: Collect multi-dimensional data information of lead-acid batteries, including voltage, current, temperature and internal resistance. These data are the basis for subsequent calculations and are obtained in real time through high-precision sensors.

[0090] More specifically, voltage acquisition: a high-precision differential ADC (such as a 24-bit Σ-Δ type) is used to measure the battery pack terminal voltage and single cell voltage in real time, with a sampling frequency of 10 Hz, and instantaneous fluctuations are eliminated through sliding average filtering.

[0091] Current monitoring: Based on Hall sensor (such as ACS712) or shunt resistor (with INA226 chip), the charge and discharge current is collected with an accuracy of ±0.5%, and the current direction is recorded simultaneously (charging is positive and discharging is negative).

[0092] Temperature detection: DS18B20 digital temperature sensors are deployed on the battery surface and key connection points to monitor the ambient and battery body temperature with a resolution of 0.5°C.

[0093] Dynamic measurement of internal resistance: The transient voltage change is obtained by pulse discharge method (such as applying 1C current pulse for 10ms), and the internal resistance is calculated by combining Ohm's law. The formula is:

[0094] Rinternal=ΔV / ΔI (1)

[0095] Where ΔV is the voltage difference before and after the pulse, and ΔI is the pulse current amplitude.

[0096] 2. Calculation and working condition identification based on different methods

[0097] Calculation by ampere-hour integration method:

[0098] Get the initial value: Use the preset open circuit voltage method to calibrate and get the initial value of the battery state of charge SOC0. After the battery is left to stand for a period of time, measure its open circuit voltage, and determine the initial SOC value of the battery through the pre-established open circuit voltage-SOC relationship curve.

[0099] Integral calculation: Using the preprocessed current data and SOC0, the battery charge and discharge current is integrated using Formula 2 to estimate the battery state of charge.

[0100] In specific implementation, the ampere-hour integration method is based on: the initial SOC value is calibrated by the open circuit voltage (OCV) method, and then calculated by current integration:

[0101]

[0102] Where Cnom is the nominal capacity and η is the Coulomb efficiency (charge η=0.95, discharge η=1.0).

[0103] Improved extended Kalman filter method calculation:

[0104] Establish state model: Use the first battery state of charge, consider the nonlinear characteristics of the battery, and

[0105] Formula 3 establishes the discretized state model.

[0106]

[0107] Where Cactual(T) is the actual capacity at temperature T, η is the coulombic efficiency, IK represents the current at time k, SOCK represents the SOC at time k, and SOCK-1 represents the SOC at time k-1; Δt represents the sampling interval. Establish a non-linear relationship: Using the preprocessed voltage and combining with the second-order RC equivalent circuit model, establish the non-linear relationship between SOC and voltage.

[0108] By measuring the battery voltage at different SOCs, fit the non-linear curve between the two.

[0109] Specifically, taking the terminal voltage as the observed value and combining with the second-order RC equivalent circuit model, establish the non-linear relationship between SOC and voltage as shown in the following formula:

[0110] V k =OCV(SOC k )+I k ·R internal (SOC k ,I) (4)

[0111] Set the covariance matrix: Set the initial covariance matrices of the process noise and the observation noise for subsequent error correction.

[0112] Real-time estimation: According to the multi-dimensional data information of the preset lead-acid battery, estimate the SOC through the discretized state model and the non-linear relationship between SOC and voltage, and perform error correction according to the covariance matrix to estimate the state of charge of the battery in real time.

[0113] Operating condition identification: Use the preset operating condition identification algorithm to judge the operating condition of the battery according to the operating parameters (current, voltage, temperature) of the battery and the preset threshold. For example, when the current change rate is less than a certain threshold and the voltage and temperature are relatively stable, it is judged as a low-current steady-state condition; when the current change rate is large, it is judged as a high-dynamic condition.

[0114] 3. Dynamic weight fusion

[0115] Low-current steady-state condition: In the low-current steady-state condition, the output of the improved extended Kalman filter method is mainly used, and the state of charge of the battery is corrected by using the voltage and internal resistance data. Since the state of the battery is relatively stable in this condition, the improved extended Kalman filter method can estimate the SOC more accurately, and combining the voltage and internal resistance data can further improve the estimation accuracy.

[0116] High dynamic conditions: Under high dynamic conditions, the ampere-hour integration method is preferentially used as the main output, and the improved extended Kalman filter method is used to calibrate the integration error in real time. Because under high dynamic conditions, the current changes greatly. The ampere-hour integration method can quickly respond to the current change, but it is prone to cumulative errors. At this time, calibration by the improved extended Kalman filter method can effectively reduce errors.

[0117] Data synchronization: Data synchronization is performed once every preset time (for example, 1 minute), and the first fusion result is output through weighted average. Different weights are assigned according to the reliability of the two methods under different working conditions, and then weighted average calculation is performed.

[0118] In specific implementation, please refer to the following steps:

[0119] Under low current steady-state conditions (|I| < 0.1C), the EKF output is the main one, and the voltage and internal resistance data are used to correct the SOC;

[0120] Under high dynamic conditions (such as acceleration / braking), the ampere-hour integration method is preferentially used, and the integration error is calibrated in real time through EKF;

[0121] Data synchronization is performed every 5 seconds, and the final SOC value is output through weighted average (weights are dynamically allocated according to the working conditions).

[0122] 4. Temperature compensation and correction

[0123] Capacity correction: Based on the Arrhenius equation, a capacity attenuation model is established as shown in the following formula, and the first fusion result is corrected for capacity and temperature. The Arrhenius equation can describe the relationship between battery capacity and temperature. Through this model, the battery capacity can be corrected according to the current temperature, thereby improving the accuracy of SOC estimation.

[0124]

[0125] Where T_ref = 25°C, and k is the temperature coefficient (typical value for lead-acid batteries k = 0.008 / °C).

[0126] Internal resistance-temperature compensation: The relationship curve between internal resistance and temperature is fitted through experimental data, as shown in the following formula:

[0127] R internal (T) = R ref ·[1 + α(T - T ref )] (6)

[0128] Where α is the internal resistance temperature coefficient (typical value α = 0.004 / °C).

[0129] The internal resistance-temperature compensation of the first fusion result is carried out by using the fitted relationship curve between the internal resistance and temperature and combining with Formula 6. Among them, R internal (T) represents the actual internal resistance value of the battery at temperature T; R ref refers to the internal resistance value of the battery at the reference temperature (usually 25°C); α represents the internal resistance temperature coefficient, reflecting the sensitivity of the internal resistance to temperature changes; T is the currently actually measured battery temperature; T ref is the reference temperature.

[0130] 5. Internal Resistance-SOC Relationship and Aging Correction

[0131] The preset internal resistance-SOC relationship and the preset aging correction model are used to correct the results of the temperature compensation correction in turn to obtain the first SOC value. Through long-term experimental data, the relationship curve between the internal resistance and SOC is established, as well as the relationship model between the aging factor and factors such as the battery usage time and the number of cycles, and the results after temperature compensation are further corrected to improve the accuracy of SOC estimation.

[0132] In specific implementation, under the laboratory environment, a full charge-discharge cycle test is carried out on the battery, and the internal resistance values at different SOC points (at 5% intervals) are recorded to generate a two-dimensional look-up table (LUT).

[0133] Enhancement in the low SOC range: When SOC < 20%, the internal resistance shows an exponential upward trend as the SOC decreases, and the algorithm switches to the high-sensitivity mode:

[0134] The internal resistance sampling frequency is increased to 20 Hz to enhance the pulsed discharge detection;

[0135] The cubic spline interpolation is used to refine the SOC-internal resistance curve, and the accuracy is improved to ±2%.

[0136] 6. Aging Adaptive Correction

[0137] Historical charge-discharge curve storage: Record the voltage-capacity curve of each complete charge-discharge, and extract the characteristic points (such as the constant current to constant voltage point, cut-off voltage).

[0138] Calculation of the capacity attenuation factor: Compare the capacity difference between the current cycle and the initial cycle, and calculate the aging coefficient through the following formula:

[0139]

[0140] Online update of model parameters: Feed back β_aging to C_actual in the EKF state equation and adjust the slope of the OCV-SOC curve to compensate for the aging effect.

[0141] In specific implementation, please refer to Figure 2In order to solve the problem of initial power estimation deviation caused by the lack of battery history data in the after-sales SOC device, this application builds a high-precision initial calculation model through user-defined parameter preset and intelligent fusion algorithm. Users can input key parameters such as battery rated capacity, production date, and number of cycles through the interactive interface. The system combines real-time sensor data with a priori knowledge base to achieve rapid convergence of SOC calculation.

[0142] Dynamic parameter fusion: Based on the rated capacity input by the user, the ampere-hour integral benchmark value is initialized, and the capacity decay coefficient is dynamically corrected through the dual-clock aging model (physical clock quantifies calendar decay, and chemical clock evaluates cycle loss). The production date and number of cycles are involved in the SOH (health state) calculation. For example, lead-acid batteries decay by 0.8% per month, and lithium batteries decay by 0.015% per cycle. Combined with the temperature compensation coefficient (Arrhenius equation), the real-time effectiveness of the capacity is optimized.

[0143] Error two-way compensation mechanism: Initialization stage (the first three charge and discharge times): the user preset parameter weight accounts for 70%, and the voltage reverse calculation method accounts for 30%, to avoid deviation from a single data source.

[0144] Learning convergence stage: After each charge and discharge, reverse integral backtracking is triggered to correct the initial SOC curve with the actual charge / discharge power, and the capacity calibration value is updated, and the error transmission rate is reduced to 5% / time.

[0145] Intelligent abnormality processing: When the difference between the user's preset capacity and the ampere-hour integral is greater than 30%, the virtual load verification is started (applying a small disturbance of 0.1C), the authenticity of the capacity is judged by the voltage response characteristics, and a pop-up window is displayed to prompt the user to calibrate.

[0146] Built-in safety fuse logic: Even if incorrect parameters are entered (such as capacity exceeding the limit), the algorithm forces the SOC to be locked in a physically reasonable range (0% to 100%) to avoid extreme misjudgments.

[0147] Measured results: In the aftermarket scenario of electric vehicles, the preset parameters can reduce the initial SOC error from ±18.7% to ±8.5%, and converge to ±4.2% within 3 complete cycles. For the battery replacement market (such as electric vehicles / energy storage systems), users only need to enter the production date and rated capacity to achieve ±6% accuracy in the first charge and discharge cycle, which is 3 times more efficient than the scenario without configuration. This design takes into account both user ease of operation and algorithm robustness, providing aftermarket equipment with initialization accuracy assurance similar to that of a pre-installed BMS.

[0148] 2. Implementation of the polymorphic abnormality inspection and maintenance method for lead-acid batteries

[0149] See also Figure 3, this embodiment will introduce in detail the specific implementation process of the lead-acid battery multi-state anomaly detection and protection method based on the above SOC precise calculation method. This method monitors the battery status in real time according to different battery usage modes, discovers and processes abnormal situations in a timely manner, and ensures the safe operation of the battery.

[0150] 1. Charging mode protection

[0151] Real-time monitoring: Using the first SOC value, the charging current and voltage are monitored in real time. Through high-precision current sensors and voltage sensors, the current and voltage data during the charging process are continuously collected.

[0152] Trigger protection: When the current exceeds 1.5 times the rated value and lasts for 10 seconds, or the single-cell voltage reaches 14.8V (for 12V battery specifications), the charging circuit is disconnected through a dual-redundancy protection circuit. The dual-redundancy protection circuit consists of a high-speed solid-state relay (such as Panasonic AQH2223, operating time < 3ms) and a high-power MOSFET (such as Infineon IPP075N15N5, Rds(on) = 7.5mΩ) in parallel with a TVS diode, ensuring that the charging circuit can be quickly and reliably cut off in case of an abnormality.

[0153] 2. Riding mode protection

[0154] Real-time monitoring: Using the first SOC value, the motor load current is monitored in real time. The motor load current data is collected through a closed-loop Hall current sensor (such as LEM LAH 100-P, accuracy ±0.5%, range ±150A, response time < 1μs).

[0155] Trigger protection: When the current increases by more than 50% within 2 seconds or exceeds 2 times the rated current of the motor controller, the motor drive MOSFET and the main battery relay are turned off in sequence. When an abnormal current change is detected, the motor drive MOSFET is first turned off within 5ms to cut off the power supply to the motor; if the short-circuit current persists, the main battery relay is disconnected after 10ms to completely isolate the fault.

[0156] 3. Standstill mode protection

[0157] Real-time monitoring: Using the first SOC value, the micro-current is detected. A nanoampere-level current detection circuit is constructed through a zero-drift operational amplifier (such as ADI AD8629), with a detection range of 0 - 500mA and a resolution of 0.1mA, to monitor the micro-current situation of the battery in real time.

[0158] Trigger alarm: If the micro-current exceeds 0.1A and lasts for 30 seconds, an alarm is triggered. The alarm methods include waking up the main MCU, sending a text message alarm through the GSM module, and the vehicle's double-flash lights flashing at a frequency of 0.5Hz.

[0159] Cut off the power supply: If it cannot be restored within 30 minutes, disconnect the main positive relay of the battery and only retain the power supply for the emergency communication circuit.

[0160] 4. Intelligent Sleep and Wake-up

[0161] Enter the sleep state: Enter the sleep state if the battery is static for more than 48 hours or the SOC drops by more than 0.5% per hour. The system shuts down non-essential loads such as GPS and Bluetooth, and only retains the core monitoring circuit, with a standby power consumption < 50 μA.

[0162] Wake up the system: Wake up within 1 second through APP instructions or vehicle vibration. When receiving the wake-up instruction sent by the APP or detecting vehicle vibration, the system quickly restores power supply and enters the normal working state.

[0163] 5. Data Synchronization

[0164] Synchronize the SOC, voltage, and fault codes to the preset port (such as the mobile APP) through the Bluetooth or 4G module. Users can view the status information of the battery in real time through the mobile APP, and timely understand the health status and abnormal conditions of the battery. At the same time, the system can also receive user instructions through the Bluetooth or 4G module to achieve remote control and parameter setting.

[0165] In the specific implementation, please refer to Figure 4 , Figure 5 The APP function aims to improve the user experience and the accuracy of the system:

[0166] Accurate SOC calculation and parameter setting: Users input the key parameters of the battery, such as rated capacity, production date, cycle times, etc. through the APP, providing a reference for the high-precision calculation model. These parameters help improve the calculation accuracy of the SOC (state of charge) and ensure that the real-time data of the remaining battery power is more accurate.

[0167] In addition, the APP supports customizing relevant function settings such as overcurrent threshold, overvoltage threshold, and sleep mode according to different usage scenarios. Users can adjust the protection parameters according to the different battery usage environments to further enhance the safety of the battery.

[0168] Real-time battery status monitoring: Real-time data display: Users can view important data such as the SOC, voltage, and current of the battery at any time to ensure timely understanding of the working state of the battery. Other functions included in the monitoring also include fault notification and fault analysis. When the battery has problems, the APP not only provides fault notification but also helps users better understand the cause of the problem and possible solutions through fault analysis.

[0169] Intelligent Alarm and Reminder System: Users can adjust the push method and alarm level filtering according to different needs, which enables users to more flexibly control the frequency and importance of receiving notifications. For example, important faults can be set as high-priority reminders, while non-urgent information can be pushed at a low priority.

[0170] Remote Control and Security Assurance: If the battery has not been used for a long time or shows abnormalities, the APP supports the remote power-off function, which can effectively avoid potential risks caused by unattended batteries. Users can remotely control the power switch of the battery through the APP to ensure the safety of the battery.

[0171] Customized Function Design: Provide customized protection and reminder settings, allowing users to flexibly adjust the functions according to their usage needs. This not only improves the accuracy of battery management but also ensures the best performance of the battery in different environments.

[0172] It can be understood that the same or similar parts in the above embodiments can be referred to each other, and the content not detailed in some embodiments can be referred to the same or similar content in other embodiments.

[0173] It should be noted that in the description of the present invention, terms such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "a plurality" refers to at least two.

[0174] Any process or method description in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the technical field to which the embodiments of the present invention belong.

[0175] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following technologies well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0176] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the method of the above embodiments can be completed by instructing relevant hardware through a program. The said program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0177] In addition, in each of the embodiments of the present invention, each functional unit can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0178] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc.

[0179] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0180] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for accurately calculating the SOC of a lead-acid battery, characterized in that, The method includes: Collecting multi-dimensional data information of a preset lead-acid battery and preprocessing the multi-dimensional data information; the multi-dimensional data information includes voltage, current, temperature, and internal resistance multi-dimensional data information; Calculating the preprocessed multi-dimensional data information based on a preset ampere-hour integration method and an improved extended Kalman filtering method respectively, and identifying the battery operating condition based on a preset operating condition identification algorithm and a preset threshold; Performing dynamic weight fusion on the calculation results of the ampere-hour integration method and the improved extended Kalman filtering method based on the result of the battery operating condition to obtain a first fusion result; Performing temperature compensation and correction on the first fusion result by using a preset temperature compensation model; Successively correcting the result of the temperature compensation and correction by using a preset internal resistance-SOC relationship and a preset aging correction model to obtain a first SOC value.

2. The method according to claim 1, wherein The preprocessing of the multi-dimensional data information includes: Performing moving average filtering on the collected voltage; Performing accuracy calibration on the collected current and recording the direction; Calculating the internal resistance based on the pulse discharge method and a preset formula, and the preset formula includes: Rinternal = ΔIΔV (1) Where ΔV is the voltage difference before and after the pulse, ΔI is the pulse current amplitude, and Rinternal is the internal resistance.

3. The method according to claim 1, characterized in that, The calculating the preprocessed multi-dimensional data information based on a preset ampere-hour integration method and an improved extended Kalman filtering method respectively, and identifying the battery operating condition based on a preset operating condition identification algorithm and a preset threshold includes: Using the preprocessed current to integrate the battery charge and discharge current through a preset ampere-hour integration method to estimate the first state of charge of the battery; Using the first state of charge of the battery and preset multi-dimensional data information to estimate the state of charge of the battery in real time through a preset improved extended Kalman filtering method; Using the estimation result of the state of charge of the battery and a preset operating condition identification algorithm to judge the operating condition of the battery according to the operating parameters of the battery and a preset threshold; the operating parameters of the battery include current, voltage, and temperature.

4. The method according to claim 3, characterized in that, The using the preprocessed current to integrate the battery charge and discharge current through a preset ampere-hour integration method to estimate the state of charge of the battery includes: Calibrating and obtaining the initial value of the state of charge of the battery by using a preset open-circuit voltage method; Using the preprocessed current data and the initial value of the state of charge of the battery to integrate the battery charge and discharge current through the following formula to estimate the state of charge of the battery, and the formula includes: Where Cnom is the nominal capacity, SOC0 represents the initial value of the state of charge of the battery, and η is the coulomb efficiency.

5. The method according to claim 3, wherein The using the first state of charge of the battery and preset multi-dimensional data information to estimate the state of charge of the battery in real time through a preset improved extended Kalman filtering method includes: Using the first state of charge of the battery and considering the non-linear characteristics of the battery to establish a discretized state model through the following formula; Where Cactual(T) is the actual capacity at temperature T, η is the coulombic efficiency, IK represents the current at time k, SOCK represents the SOC at time k, and SOCK-1 represents the SOC at time k-1; Δt represents the sampling interval; Using the preprocessed voltage and combining with the second-order RC equivalent circuit model, establish the SOC-voltage nonlinear relationship; Set the covariance matrices of the initial process noise and observation noise; According to the preset multi-dimensional data information of the lead-acid battery, estimate the SOC through the discretized state model and the SOC-voltage nonlinear relationship, and perform error correction according to the covariance matrix to estimate the state of charge of the battery in real time.

6. The method according to claim 1, characterized in that Based on the result of the battery operating condition, dynamically weight and fuse the calculation results of the ampere-hour integration method and the improved extended Kalman filter method to obtain the first fusion result, including: Under the low-current steady-state condition, the output of the improved extended Kalman filter method is used as the main, and the state of charge of the battery is corrected by using the voltage and internal resistance data; Under the high-dynamic condition, the ampere-hour integration method is preferably used as the main output, and the integral error is calibrated in real time by the improved extended Kalman filter method; Data synchronization is performed once every preset time, and the first fusion result is output through weighted average.

7. The method according to claim 1, wherein The temperature compensation and correction of the first fusion result by using the preset temperature compensation model includes: Based on the Arrhenius equation, establish a capacity attenuation model to perform capacity and temperature correction on the first fusion result; Use the fitted relationship curve between the internal resistance and temperature to perform internal resistance temperature compensation on the first fusion result, and the formula includes: R internal (T) = R ref ·[1 + α(T - T ref )] (4) Among them, R internal (T): represents the actual internal resistance value of the battery at temperature T; R ref : refers to the internal resistance value of the battery at the reference temperature (usually °C); α: represents the internal resistance temperature coefficient, reflecting the sensitivity of the internal resistance to temperature changes; T: the currently actually measured battery temperature, in degrees Celsius (°C), which is the input parameter for temperature compensation; T ref : reference temperature, generally set to °C, as the reference temperature for calculating the change in internal resistance.

8. A method for multi-state abnormal inspection and protection of lead-acid batteries, which is applied to the method for accurate calculation of the SOC of the lead-acid battery described in any one of claims 1-7, characterized in that, Including: Charging mode protection, using the first SOC value, real-time monitoring of the charging current and voltage. When the current exceeds the rated value by a preset multiple and lasts for a preset number of seconds, or the single-cell voltage reaches the threshold, the charging circuit is disconnected through the dual-redundancy protection circuit; Riding mode protection, using the first SOC value, real-time monitoring of the motor load current. When the current increases by more than 50% within a preset number of seconds or exceeds the rated current of the motor controller by a preset multiple, the motor drive MOSFET and the main battery relay are turned off in sequence; Static mode protection, using the first SOC value, detecting the micro-current. If it exceeds the preset milliampere and lasts for a preset number of seconds, an alarm is triggered. If it does not recover within the preset minutes, the total positive relay of the battery is disconnected.

9. The method according to claim 8, wherein The method further includes: If the battery is static for more than a preset time period or the SOC drops by more than the threshold per hour, it enters the sleep state and is awakened within a preset number of seconds through an APP command or vehicle vibration.

10. The method according to claim 8, characterized in that, The method further includes: Synchronize the SOC, voltage, and fault code to the preset port through the Bluetooth or 4G module.

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