Low-voltage battery control method and system

Abstract

The application provides a low-voltage battery control method and system, and relates to the technical field of intelligent control. The method comprises the following steps: acquiring state data of a low-voltage battery, and diagnosing based on the state data to determine the health state of the low-voltage battery; estimating the running state of the low-voltage battery based on the health state and the state data; dynamically executing the charging, discharging or power-off control strategy of the low-voltage battery according to the running state and the health state; and controlling the low-voltage battery management subsystem of the vehicle to switch between different power management modes based on the charging, discharging or power-off control strategy of the low-voltage battery. The application improves the accuracy and robustness of diagnosis and avoids misjudgment caused by single signal failure.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to a low-voltage battery control method. Background Technology

[0002] Currently, traditional low-voltage battery management solutions primarily focus on monitoring basic parameters and simple charge / discharge control. At the data acquisition level, fixed-period, single-parameter sampling methods are typically used, resulting in insufficient real-time and coordinated acquisition of key parameters such as voltage, current, and temperature, making it difficult to accurately capture the battery's dynamic characteristics and early signs of faults. Regarding fault diagnosis, existing solutions are mostly limited to judging basic thresholds such as overvoltage, undervoltage, and overcurrent, lacking hierarchical and correlated diagnostic logic. This leads to high false alarm rates or slow response to serious faults, failing to provide early warnings or address performance degradation in the early stages of a fault.

[0003] In battery state estimation, common methods often estimate the state of charge (SOC) or state of health (SOH) independently, lacking coordination and coupling between the various state parameters. For example, SOH updates typically rely on offline calibration, failing to reflect the impact of battery degradation on usable capacity in real time, leading to an accumulation of SOC estimation errors over time. Simultaneously, estimations of state of power (SOP) and state of energy (SOE) are rather coarse, usually employing static lookup table methods that do not adequately consider the influence of dynamic factors such as temperature, aging, and operating conditions. This results in inaccurate predictions of charge / discharge power limits and remaining energy, impacting driving safety and user experience.

[0004] In terms of charging and discharging control strategies, existing strategies often have fixed patterns and lack the ability to dynamically adjust based on real-time status and fault levels. For example, the intelligent charging logic is simple and may cause unnecessary frequent wake-ups and energy loss; the discharging power-locking strategy lacks a buffer mechanism and is prone to false triggering due to instantaneous load or estimation errors, affecting vehicle functions. In addition, in abnormal situations such as system communication failures, there is often a lack of local, robust backup control strategies.

[0005] In terms of system-level power management, common power mode switching logic is relatively simple, mainly relying on network signals or ignition status. The conflict handling mechanism for multiple wake-up sources (such as network management, hardwired, and low battery events) is imperfect, and when the battery is deeply discharged and the system completely crashes, there is a lack of effective emergency wake-up or recovery methods, which may result in the vehicle being unable to start normally. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a low-voltage battery control method that improves the accuracy and robustness of diagnosis and avoids misjudgment caused by a single signal failure.

[0007] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a low-voltage battery control method is applied to a vehicle low-voltage battery management subsystem, the method comprising: Step 1: Obtain the status data of the low-voltage battery and perform diagnosis based on the status data to determine the health status of the low-voltage battery; Step 2: Based on the health status and the status data, estimate the operating status of the low-voltage battery; Step 3: Based on the operating status and health status, dynamically execute the charging, discharging, or power-locking control strategy for the low-voltage battery; Step 4: Based on the charging, discharging, or power-locking control strategy of the low-voltage battery, control the vehicle's low-voltage battery management subsystem to switch between different power management modes.

[0008] Secondly, the low-voltage battery control system includes: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0009] The above-described solution of the present invention has at least the following beneficial effects: By employing a multi-cycle, multi-parameter parallel data acquisition strategy, and setting differentiated sampling periods (e.g., 10ms for voltage, 100ms for temperature) to account for the differences in the characteristics of parameters such as voltage, current, and temperature (e.g., the high inertia of temperature), higher quality basic data was obtained. Based on this, the constructed multi-level fault diagnosis mechanism (e.g., level one early warning, level two performance limitation, level three safety protection) can classify and categorize hardware anomalies and battery faults. Through fault correlation analysis (e.g., combining voltage change rate for redundancy judgment in current sampling faults), the accuracy and robustness of diagnosis are significantly improved, avoiding misjudgments caused by a single signal failure.

[0010] By establishing a collaborative estimation model for SOC (State of Charge), SOH (State of Health), SOP (State of Power), and SOE (State of Energy), this approach overcomes the limitations of traditional methods that rely on isolated estimations of individual state parameters. Specifically, full-charge calibration is used to update SOH online, and the updated SOH is fed back into the ampere-hour integration method of SOC in real time to correct the real-time available capacity. This achieves closed-loop coupling between SOC and SOH, effectively suppressing long-term cumulative errors in SOC. Simultaneously, the dynamic calculation of SOP not only relies on table lookups but also incorporates multi-dimensional factors such as battery temperature, SOC, SOH, and system temperature in real time. The algorithm dynamically corrects the maximum charge and discharge current, making the power boundary prediction more closely aligned with the actual battery capacity and operating conditions. Furthermore, SOE calculation based on voltage and available capacity provides a more accurate energy depletion warning than a single SOC calculation. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of sampling management in low-voltage battery control methods.

[0012] Figure 2 This is a schematic diagram of fault management in low-voltage battery control methods.

[0013] Figure 3 This is a schematic diagram of SOX state management in low-voltage battery control methods.

[0014] Figure 4 This is a schematic diagram of intelligent power replenishment in low-voltage battery control methods.

[0015] Figure 5 This is a schematic diagram of power-locking management in low-voltage battery control methods. Detailed Implementation

[0016] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0017] like Figures 1 to 5As shown, this embodiment discloses a low-voltage battery control method applied to a vehicle low-voltage battery management subsystem. The vehicle low-voltage battery is a 12V lead-acid battery with a rated capacity of 60Ah, adapted to the low-voltage power supply system of passenger vehicles. The vehicle low-voltage battery management subsystem includes a front-end acquisition chip, a power monitoring chip, a charge / discharge switching device, a DC-DC converter, and a core control unit. The core control unit is communicatively connected to the vehicle controller and the vehicle network bus, and is used to execute all the steps of this method to achieve accurate monitoring, state estimation, dynamic control, and system-level power management of the low-voltage battery. The specific steps are as follows: Step 1: Obtain the status data of the low-voltage battery and perform diagnostics based on the status data to determine the health status of the low-voltage battery. This step comprehensively acquires battery status data and determines its health status through multi-channel parallel acquisition, filtering and verification, hardware fault diagnosis, and battery body fault classification diagnosis. It is specifically divided into the following four sub-steps: Step 11: Collect state data of individual cell voltage, total voltage, current, and temperature parameters of the low-voltage battery in parallel at different preset sampling periods; perform moving average filtering on the state data to obtain filtered data. Step 110: Initiate multi-channel data acquisition tasks in parallel, configuring independent data acquisition channels for the individual cell voltages (6 cells in total), total voltage, current, and temperature parameters of the low-voltage battery, thus constructing a multi-channel parallel acquisition architecture. Based on the operating characteristics of the vehicle's low-voltage system, a first sampling period and a second sampling period are preset. The first preset period is 10ms (high-frequency period, adapting to the rapid dynamic changes in voltage and current parameters), and the second preset period is 100ms (low-frequency period, longer than the first preset period due to the slower rate of temperature parameter change). After starting the parallel acquisition task, control the voltage and current acquisition channels to continuously trigger sampling actions according to the first preset period of 10ms, and simultaneously control the temperature acquisition channel to trigger sampling actions according to the second preset period of 100ms. At each sampling trigger, record the original sampled value of the corresponding parameter and associate it with the acquisition timestamp. Finally, summarize to form a multi-channel raw sampling dataset of 6 individual cell voltage raw data, 1 total voltage raw data, 1 current raw data, and 1 temperature raw data. The temperature acquisition point is set in the middle of the battery casing to ensure that the acquired data reflects the actual operating temperature of the battery.

[0018] Step 111: For the raw sampling data acquired by each acquisition task, perform a moving average calculation using multiple consecutive sampled values ​​within a preset time window to generate preliminary filtered data. For each raw sampling data acquired in Step 110, preset moving average time windows adapted to the characteristics of each parameter are defined: the moving average time window for voltage parameters (individual voltage, total voltage) is set to 50ms (corresponding to 5 consecutive sampled values), the moving average time window for current parameters is set to 50ms (corresponding to 5 consecutive sampled values), and the moving average time window for temperature parameters is set to 500ms (corresponding to 5 consecutive sampled values). For each raw sampling data, extract all consecutive raw sampling values ​​within the current time and preset time window according to the order of acquisition timestamps; perform an arithmetic average calculation on this set of extracted consecutive raw sampling values ​​to obtain the single set of average data for the corresponding time. As the sampling process continues, for each new original sample value, the earliest original sample value within the sliding time window is removed, and the above averaging calculation process is repeated to continuously generate continuous single-set average data. These continuous single-set average data are summarized to form the preliminary filtered data corresponding to each parameter. At the same time, the timestamp relationship between each preliminary filtered data and the original sample data is retained to achieve traceability of the original data and the filtered data.

[0019] Step 112: Perform validity verification on each preliminary filtered data stream to obtain verified data; use the verified data as the filtered data. For each preliminary filtered data stream generated in Step 111, construct multi-dimensional validity verification rules: First, perform range verification. Based on the model of the 12V lead-acid battery, preset reasonable ranges for each parameter: reasonable range for single cell voltage is 2.0V~2.4V, reasonable range for total voltage is 10.8V~14.8V, normal operating range for current is -30A~30A (negative values ​​indicate discharge, positive values ​​indicate charging), and allowable operating temperature is -30℃~60℃. Compare the current preliminary filtered data with the corresponding range to determine whether the data exceeds the range; Second, perform rate of change verification, preset... The maximum allowable rate of change for each parameter is as follows: Individual voltage: 0.1V / 10ms; Total voltage: 0.5V / 10ms; Current: 5A / 10ms; Temperature: 1℃ / 100ms. The difference between the current preliminary filtered data and the previous preliminary filtered data is calculated and compared to the preset maximum allowable rate of change to determine if the rate of change is abnormal. Finally, a continuity check is performed by statistically analyzing the preliminary filtered data over three consecutive time points. If three consecutive data points simultaneously exceed the range or exhibit abnormal rates of change, they are considered continuously invalid. Preliminary filtered data that passes the check is directly used as the final filtered data. If the check fails, the data is marked as invalid, and the valid filtered data from the previous time point is used as a temporary substitute. The occurrence time and frequency of invalid data are recorded. Finally, the complete filtered dataset obtained after verification and filtering is output to ensure the accuracy of subsequent diagnosis and estimation.

[0020] Step 12: Based on the filtered data, identify the status of the sampling circuit, switching devices, and communication link, and generate a comprehensive hardware fault code. Step 120: Based on the individual cell voltages and total voltage in the filtered data, diagnose whether there are open circuits or short circuits to ground / power supply in each voltage sampling channel by comparing the voltage sampling values ​​with the preset reasonable range and change trends, and generate a first-type hardware fault code. Retrieve the preset reasonable voltage range of the 12V lead-acid battery (individual cell voltage 2.0V~2.4V, total voltage 10.8V~14.8V), and set the voltage change trend judgment threshold to 0.2V / 100ms. From the filtered data obtained in Step 112, extract 6 individual cell voltage filtered data and 1 total voltage filtered data, and compare them with the preset reasonable range respectively: if a certain individual cell voltage filtered data exceeds the upper limit (2.4V) or lower limit (2.0V) of the reasonable range for 500ms continuously, or if the total voltage filtered data deviates from the reasonable range for a duration of 500ms, it is marked as a voltage anomaly. Further analysis of voltage change trends reveals the following: if the individual unit voltage or total voltage suddenly drops to 0V±0.1V and remains stable, an open-circuit fault is identified in the corresponding voltage sampling channel; if the individual unit voltage or total voltage abnormally approaches 12V (power supply voltage), a short-circuit fault to the power supply is identified; if the individual unit voltage or total voltage abnormally approaches 0V (ground potential), a short-circuit fault to ground is identified. For each type of fault, a first-type hardware fault code is generated according to a preset coding rule. The fault code uses a 1-byte encoding, where the high 4 bits represent the fault type (01 for open circuit, 10 for short circuit to ground, and 11 for short circuit to power supply), and the low 4 bits represent the corresponding fault sampling channel number (01~06 correspond to 6 individual unit voltage channels, and 07 corresponds to the total voltage channel). The fault code also includes information on the fault duration.

[0021] Step 121: Based on the current value and switching device temperature in the filtered data, combined with the control command status of the charging / discharging switch, diagnose whether the charging / discharging switch device has a sticking or unexpected disconnection fault, and generate a second type of hardware fault code. The normal operating temperature range (-20℃~80℃) and current response characteristics of the charging / discharging switch device are obtained in advance: after receiving a closing control command, the current should reach ≥0.5A within 100ms (charging or discharging state); after receiving a disconnection control command, the current should drop to ≤0.1A within 100ms. Current filtering data and switching device temperature filtering data are extracted from the filtered data, and the real-time control command status (closing / disconnecting) issued to the charging / discharging switch by the vehicle's low-voltage battery management subsystem is retrieved. The current data, temperature data, and control command status are verified together: If the control command is closed, but the current filter data remains around 0±0.1A for a long period (500ms) (no normal charging / discharging current), and the temperature of the switching device does not rise significantly (below -20℃ or remains at room temperature without change), then the charging / discharging switching device is determined to have an unexpected disconnection fault; if the control command is open, but the current filter data is not 0 (≥0.2A) for a long period (500ms), or the temperature of the switching device rises abnormally (above 80℃), then the charging / discharging switching device is determined to have an adhesion fault. Based on the fault type (adhesion / unexpected disconnection) and the corresponding switching device number, a second type of hardware fault code is generated, using 1-byte encoding. The high 4 bits represent the fault type (01 for unexpected disconnection, 10 for adhesion), and the low 4 bits represent the switching device number (01 for charging switch, 02 for discharging switch). The fault code is associated with the severity of the fault. The adhesion fault code is marked as high risk, and the unexpected disconnection fault is marked as medium risk.

[0022] Step 122: Based on the communication verification results with the front-end acquisition chip and the voltage status output by the power monitoring chip, diagnose whether the communication link and core power supply are abnormal, and generate a third type of hardware fault code. Pre-set communication verification rules: communication timeout threshold is 100ms, data checksum deviation is allowed within ±1, frame loss rate is capped at 1%; the normal range of core power supply voltage is 3.3V±0.3V (determined based on the operating voltage requirements of the front-end acquisition chip and power monitoring chip). First, communicate with the front-end acquisition chip to verify the communication process in real time: if the communication response time exceeds 100ms, the deviation between the received data checksum and the calculated value exceeds ±1, or the data frame loss rate exceeds 1% within 1 minute, the communication link is determined to be abnormal. Simultaneously, read the core power supply voltage status data output by the power monitoring chip and compare it with the preset normal range of core power supply voltage (3.0V~3.6V). If the voltage value exceeds the range (undervoltage <3.0V or overvoltage >3.6V), the core power supply is determined to be abnormal. For communication link anomalies (timeout / checksum error / frame loss) and core power supply anomalies (undervoltage / overvoltage), a third type of hardware fault code is generated according to a preset encoding rule. The code is encoded in 1 byte. The high 4 bits represent the anomaly type (01 for communication timeout, 02 for checksum error, 03 for frame loss, 04 for core power supply undervoltage, and 05 for core power supply overvoltage) and the low 4 bits represent the anomaly severity (01 for minor, 02 for moderate, and 03 for severe). The fault code contains the anomaly type and key parameters at the time of the fault occurrence (such as timeout time and voltage deviation value).

[0023] Step 123: Based on the first, second, and third types of hardware fault codes, generate a comprehensive hardware fault code representing the overall status of the sampling circuit, switching devices, and communication link. A hardware fault code aggregation rule is pre-established, using a 4-byte comprehensive fault code. The first byte represents the first type of fault (voltage sampling channel fault), the second byte represents the second type of fault (charge / discharge switch fault), the third byte represents the third type of fault (communication and core power supply fault), and the fourth byte indicates the fault aggregation level (01 for low risk, 02 for medium risk, and 03 for high risk). Collect the first type of hardware fault codes generated in Step 120, the second type of hardware fault codes generated in Step 121, and the third type of hardware fault codes generated in Step 122, and integrate and encode the three types of fault codes according to the aggregation rule: if only a single type of fault exists, the corresponding byte is filled with that type of fault code; other fault types are filled with 00 (no fault identifier); if multiple types of faults exist, the corresponding bytes of the fault codes are filled sequentially. Simultaneously, based on the severity of the three types of faults (high risk for switch device adhesion and low risk for minor frame loss in the communication link), the comprehensive fault level is marked in the 4th byte: 03 for the presence of a high-risk fault, 02 for the presence of only a medium-risk fault, 01 for the presence of only a low-risk fault, and 00 for the absence of any fault. Finally, a comprehensive hardware fault code containing all hardware fault information (fault type, corresponding component, severity level) is generated, and the fault code generation timestamp and associated original verification data (such as communication timeout records and power supply voltage data) are stored for subsequent fault tracing and diagnosis.

[0024] Step 13: Based on the hardware fault codes and filtered data, perform battery body fault diagnosis to generate the battery fault status. Step 130: Based on the filtered data, perform first-level fault diagnosis to determine whether the individual cell voltage, total voltage, current, and temperature of the low-voltage battery exceed preset real-time warning thresholds. If they exceed these thresholds, a warning-level fault state is generated. Based on the model, aging characteristics, and vehicle operating conditions of the 12V lead-acid battery, preset real-time warning thresholds for each parameter are as follows: individual cell voltage warning upper limit is 2.3V, lower limit is 2.1V; total voltage warning upper limit is 14.5V, lower limit is 11.0V; current overload warning upper limit is 25A, lower limit is -25A; temperature warning upper limit is 55℃, lower limit is -25℃. The duration for determining threshold exceedance is set to 300ms (to avoid misdiagnosis due to instantaneous fluctuations). From the filtered data obtained in Step 112, extract the filtered data for individual cell voltage, total voltage, current, and temperature, and compare them one by one with the corresponding real-time warning thresholds. If the filtered data of a certain parameter exceeds the warning threshold, and the duration of the exceeding state reaches 300ms, a warning fault for the corresponding parameter is triggered. For different parameter warning faults, a warning-level fault state is generated according to preset rules, clearly indicating the fault type (e.g., single-unit voltage overvoltage warning, total voltage undervoltage warning, current overload warning, temperature overheating warning), the specific value of the corresponding fault parameter, and the duration of exceeding the limit, while marking the fault level as warning level. If all parameters do not exceed the warning threshold, or the exceeding state does not meet the duration requirement, a state record without a warning-level fault is generated.

[0025] Step 131: Based on the filtered data and the early warning level fault status, perform a second-level fault diagnosis to obtain the diagnostic results. Retrieve the early warning level fault status (including fault type, parameter value, and duration) generated in Step 130, and simultaneously extract the filtered data from Step 112 to construct a multi-parameter collaborative diagnostic model. First, conduct a deeper analysis of the early warning level fault status: If a single parameter early warning exists, combine the filtered data of other parameters to determine whether there is a correlation anomaly. For example, when a single cell voltage undervoltage early warning is detected, simultaneously check whether there is abnormal discharge in the current data (current ≤ -20A) and whether the temperature data is too low (≤ -20℃) to investigate whether the undervoltage is caused by excessive discharge at low temperatures. If multiple parameters are simultaneously warned, analyze the temporal relationship of the parameter anomalies to determine the primary and secondary logic of the fault triggering. For example, if a high temperature early warning (≥55℃) occurs first, followed by a current overload early warning (≥25A), then the core of the fault is determined to be the current anomaly caused by the change in battery internal resistance due to high temperature. Based on the analysis results, a diagnostic result is generated, which clarifies the core cause of the fault (such as low temperature causing undervoltage of a single cell, excessive discharge leading to current overload accompanied by temperature rise), the fault development trend (such as the fault continuing to expand and the fault tending to stabilize), and the initial scope of impact (such as only affecting the performance of a single cell, but possibly affecting the entire battery pack). If step 130 is a fault without warning level, the diagnostic result is that the battery body parameters are normal and there is no related fault.

[0026] Step 132: Based on the filtered data, comprehensive hardware fault codes, and diagnostic results, perform a third-level safety protection diagnosis to generate an emergency fault state. Pre-set safety protection diagnostic rules, clarifying the correlation criteria between comprehensive hardware fault codes and battery-related faults: voltage sampling channel open-circuit faults require simultaneous verification of the voltage warning's authenticity; charge / discharge switch sticking faults require a focus on investigating the risk of current overload. Emergency fault triggering conditions include: battery temperature ≥ 60℃ (thermal runaway risk), total voltage ≥ 15.0V (severe overcharge), total voltage ≤ 10.0V (severe over-discharge), charge / discharge switch sticking and current ≥ 25A (current runaway), and core power supply abnormalities leading to parameter runaway. First, retrieve the comprehensive hardware fault codes generated in step 123 and analyze the sampling circuit, switching device, and communication link fault information contained within; simultaneously, retrieve the diagnostic results from step 131 and the filtered data from step 112 for multi-dimensional cross-verification. If the comprehensive hardware fault code indicates core hardware failure (such as a stuck charge / discharge switch), and the diagnostic results show current overload and excessive temperature, with corresponding parameters in the filtered data consistently exceeding safety thresholds (such as current ≥25A, temperature ≥60℃), an emergency fault is triggered, generating an emergency fault state. The fault level is specified (e.g., Level 1 emergency fault: risk of thermal runaway; Level 2 emergency fault: severe overcharge / over-discharge), the necessary immediate safety protection actions (e.g., cutting off the charge / discharge circuit and activating the cooling system), and fault location information (e.g., current runaway caused by a stuck switch). If the comprehensive hardware fault code indicates a minor fault (such as minor frame loss in the communication link), and the diagnostic results show that the battery itself is fault-controllable and the filtered data parameters do not exceed safety thresholds, then an emergency fault is not triggered, and the emergency fault state is "No Emergency Fault." If the comprehensive hardware fault code shows no fault and the diagnostic results are normal, it is also determined that there is no emergency fault.

[0027] Step 133: Based on the warning-level fault status, diagnostic results, and emergency fault status, form the battery fault status. Establish a hierarchical summary rule for battery fault status, clarifying the priority order of warning-level fault status, diagnostic results, and emergency fault status (emergency fault status has the highest priority, followed by warning-level fault status, with diagnostic results serving as supplementary explanations). First, integrate the emergency fault status from Step 132: If an emergency fault exists, directly use the emergency fault status as the core content of the battery fault status, simultaneously attaching the diagnostic results from Step 131 (explaining the fault causes and development trends) and key information of the comprehensive hardware fault code (associated hardware faults), marking the fault handling priority as the highest, and recording the fault generation timestamp and associated filtered data segments. If no emergency fault exists, use the warning-level fault status from Step 130 as the core content, combined with the diagnostic results from Step 131 (deepening the fault cause analysis). If the comprehensive hardware fault code indicates a minor fault, it should also be added to the battery fault status, marking the fault handling priority as medium. If there is neither an emergency fault nor a warning-level fault, the battery fault status is that the battery itself and associated hardware are normal and fault-free, simultaneously attaching the normal judgment conclusion of the diagnostic results. The final result is a structured battery fault status document, which includes elements such as fault level, core fault information, cause analysis, processing priority, and related data, and is used for subsequent health status assessment and control strategy execution.

[0028] Step 14: Determine the health status of the low-voltage battery based on the hardware fault codes and battery fault status. A pre-established health status grading standard is used, divided into four levels: good, fair, poor, and faulty. A collaborative judgment is made by combining the fault level of the integrated hardware fault code and the fault level of the battery fault status: If the integrated hardware fault code is fault-free (byte 4 is 00) and the battery fault status is fault-free, the health status is determined to be good; if the integrated hardware fault code is low-risk (byte 4 is 01), or the battery fault status is at the warning level with no related serious abnormalities, and the diagnostic results show that the fault can be resolved on its own, the health status is determined to be fair; if the integrated hardware fault code is medium-risk (byte 4 is 02), or the battery fault status is at the warning level with related abnormalities, and the diagnostic results show that the fault has an expanding trend, the health status is determined to be poor; if the integrated hardware fault code is high-risk (byte 4 is 03), or the battery fault status has an emergency fault, the health status is determined to be faulty, and protective control strategies must be implemented immediately. In this embodiment, if, after the above steps, the integrated hardware fault code is fault-free and the battery fault status is fault-free, the health status of the low-voltage battery is determined to be good.

[0029] Step 2: Based on the health status and the status data, estimate the operating status of the low-voltage battery. In this step, the operating state includes at least the state of charge (SOC) and the state of power (SOP), while simultaneously estimating the state of health (SOH) and the state of energy (SOE). A collaborative estimation model for these four states is established to achieve coupled estimation of multiple state parameters. The specific process is as follows: First, the health status determined in step 1 (good in this embodiment) and the filtered data (cell voltage, total voltage, current, and temperature) obtained in step 112 are retrieved as the basic input for state estimation. For SOH estimation, a full-charge calibration online update method is adopted: when the low-voltage battery completes a complete charge-discharge cycle (from SOC≤20% to SOC≥95%), the current SOH value is calculated based on the ratio of the total charged capacity to the battery's rated capacity (60Ah) (SOH=actual usable capacity / rated capacity×100%), and the updated SOH is fed back to the ampere-hour integration method of SOC in real time to correct the real-time usable capacity, thus realizing the closed-loop coupling of SOC and SOH.

[0030] For SOC estimation, an ampere-hour integration method combined with open-circuit voltage correction is adopted: based on the current data in the filtered data, the initial SOC value is calculated using the ampere-hour integration formula (SOC = initial SOC + ∫(charging current - discharging current)dt / actual available capacity), where the actual available capacity is corrected in real time by SOH; at the same time, every 10 minutes, based on the total voltage (open-circuit voltage) in the filtered data, a preset open-circuit voltage-SOC mapping table is consulted to correct the SOC estimate, effectively suppressing the long-term cumulative error of SOC and ensuring that the SOC estimation accuracy is ≤±2%.

[0031] For SOP estimation, a dynamic correction method is adopted, which not only relies on the preset SOC-SOP mapping table, but also combines multi-dimensional factors such as battery temperature, SOC, SOH and system temperature in real time: when the temperature is below -20℃, the maximum charge and discharge current is reduced to 50% of the rated value; when SOH≤80%, the maximum charge and discharge current is reduced to 80% of the rated value; when SOC≤20% or SOC≥90%, the maximum charge and discharge current is gradually reduced. The maximum charge and discharge current is dynamically corrected through the algorithm, so that the power boundary prediction is more in line with the actual battery capacity and operating conditions, and ensures that the SOP estimation error is ≤±5%.

[0032] SOE estimation is calculated based on voltage and available capacity: SOE = (Actual available capacity × SOC × Average battery operating voltage) / 1000, where the actual available capacity is corrected by SOH, and the average battery operating voltage is calculated based on the average total voltage in the filtered data (within the last minute), which can provide a more accurate energy depletion warning than a single SOC, informing users of the remaining range energy (unit: Wh).

[0033] In this embodiment, the estimated values ​​are: SOH = 98% (good health status with no significant degradation), SOC = 65%, SOP maximum charging current is 10A, maximum discharging current is 20A, SOE = 65% × 60Ah × 12V = 468Wh. That is, the current operating status of the low-voltage battery is: 65% state of charge, maximum charging and discharging currents in power state are 10A and 20A respectively, and the remaining range energy is 468Wh.

[0034] Step 3: Based on the operating status and health status, dynamically execute the charging, discharging, or power-locking control strategy for the low-voltage battery. This step, based on the health status (good) determined in step 1 and the operating status estimated in step 2, combined with real-time monitoring data, dynamically selects and executes charging, intelligent power replenishment, power-locking discharge, and protective control strategies. Specifically, it consists of the following four sub-steps: Step 30: Based on the state of charge, state of power, and battery fault status during operation, determine whether the preset charging conditions are met. If met, select pre-charge, main charge, or float charge mode according to the current battery voltage and temperature, and send the corresponding charging request to the DC-DC converter to execute the charging process. The preset charging condition judgment rules specify that the charging conditions must be met simultaneously: In the operating state, the State of Charge (SOC) is lower than the preset charging start threshold (set to 70% in this embodiment); the State of Power (SOP) indicates that the battery has rechargeable capability (maximum charging current ≥ 5A); and in the healthy state, the battery fault status has no charging prohibition level fault (in this embodiment, there is no emergency fault, thus meeting the conditions). From the operating state obtained in step 2, SOC = 65% (lower than 70%) and SOP maximum charging current = 10A (≥ 5A) are extracted. From the healthy state determined in step 1, the battery fault status is extracted as fault-free. Each condition is verified to meet the charging conditions, and the process proceeds to the charging mode selection stage.

[0035] The current total battery voltage is 13.2V and the temperature is 25℃ from the filtered data obtained in step 112. Referring to the charging mode selection rules: the battery voltage is within the normal charging range (11.0V~14.5V) and the temperature is within the suitable charging range (-20℃~55℃), therefore, the main charging mode (rated current charging) is selected. The rated charging current is set to 10A (matching the maximum charging current at SOP), and the upper limit of the charging voltage is set to 14.4V (adapting to the full-charge voltage of this 12V lead-acid battery). Subsequently, a charging request carrying the charging current (10A), the upper limit of the charging voltage (14.4V), and the charging mode identifier (main charging) is sent to the vehicle's DC-DC converter. Simultaneously, charging process monitoring is initiated, recording the SOC=65%, voltage=13.2V, and temperature=25℃ at the start of charging, and monitoring parameter changes during the charging process in real time.

[0036] Step 31: During the charging process or routine monitoring, if the state of charge is detected to be lower than the first preset threshold, local wake-up is triggered and intelligent charging is initiated. The DC-DC converter is controlled to charge the low-voltage battery until the state of charge reaches the preset charging target value. The first preset threshold is set to 30% (the basic threshold for triggering charging), and the charging target value is 50% (to ensure that the vehicle's basic power needs are met). Simultaneously, local wake-up triggering logic is configured: a hardware wake-up signal in low-power mode and a software timed wake-up mechanism (waking up to monitor SOC every minute). During the charging process in step 30, SOC data is continuously monitored in real time. If the charging process is not executed (e.g., the vehicle is in normal monitoring mode), the monitoring module is woken up every minute via the software timed wake-up mechanism to collect SOC data.

[0037] In this embodiment, during the charging process, the SOC continuously rises but does not fall below the first preset threshold (30%), therefore intelligent charging is not triggered. If the vehicle is in a dormant state after charging is completed and the SOC is detected to drop to 28% (below 30%), the local wake-up mechanism is immediately triggered to wake up the low-voltage battery management subsystem and the associated DC-DC converter (if in a dormant state). At the same time, the intelligent charging process is started: a charging request is sent to the DC-DC converter, specifying that the charging current limit is 5A (based on the current battery temperature of 25°C and good health status, to avoid damage to the battery from high-current charging). During the charging process, the SOC change is continuously tracked, and the battery voltage, temperature, and fault status are monitored synchronously. If faults such as overheating during charging (temperature ≥55°C) or abnormal voltage rise (≥14.8V) occur, charging is immediately suspended and the fault is recorded. If the SOC continues to rise to the preset charging target value (50%), the charging process is stopped, a charging completion signal is sent, the subsystem returns to the low-power normal monitoring mode, and charging duration, charging amount, and other data are recorded.

[0038] Step 32: During the discharge process or routine monitoring, if the state of charge is detected to be lower than the second preset threshold below the first preset threshold, a discharge power-lock warning is first issued; if the state of charge is not effectively improved within the warning period, a discharge power-lock operation is executed, controlling the disconnection of the discharge switching device to prohibit discharge. The second preset threshold is set to 15% (below the first preset threshold of 30%, which is the critical threshold for discharge power lock-up), and the discharge power lock-up warning period is set to 3 minutes (to reserve time for emergency power replenishment or load adjustment). The warning prompt method is configured to send a warning signal to the vehicle controller and record a warning log. During the discharge process (such as when vehicle lights, central control screen, and other electrical loads are working) or during normal monitoring, the SOC data in the operating status is continuously monitored.

[0039] In this embodiment, the State of Charge (SOC) continuously rises during charging, and the discharge process is not executed, therefore no discharge lock-up warning is triggered. If the vehicle is in a discharge state and the SOC is detected to drop to 14% (below 15%), a discharge lock-up warning is immediately triggered: a warning message is sent to the vehicle controller, indicating that the SOC is too low and discharge lock-up is about to be triggered. At the same time, the SOC monitoring frequency is increased (from 10 seconds / time to 2 seconds / time), and data such as SOC=14%, discharge current=-10A, and load status (lights on, central control screen working) are recorded at the time of warning activation. After entering the warning period, SOC changes are continuously tracked: if the SOC rises to 15% or above within the 3-minute warning period due to charging operations, it is determined that the state of charge has been effectively improved, the discharge lock-up warning is lifted, and normal discharge monitoring is restored; if the SOC is still below 15% and shows no upward trend at the end of the warning period, a discharge lock-up operation is immediately executed: a disconnect command is sent to the discharge switch device to control it to disconnect the discharge circuit and prohibit subsequent discharge behavior. After power is locked, the reason for power locking (low SOC) and the parameters of the power locking time are recorded. At the same time, the status information that the power locking has been triggered is fed back to the vehicle system, and only the emergency power supply channel of the vehicle's core safety system (such as the anti-theft system) is retained.

[0040] Step 33: When performing the above charging or discharging control, if a fault condition requiring performance limitation or safety protection is detected in the health status, a protective control strategy of derating charging, prohibiting charging, or immediately disconnecting the circuit is executed according to the fault level. Establish a mapping rule between fault levels and protective control strategies in advance: minor faults (such as slight voltage warnings, no core hardware faults) correspond to derating charging (reducing the upper limit of charging current / voltage to 80% of the rated value); moderate faults (such as continuous temperature warnings, slight hardware abnormalities) correspond to prohibiting charging (stopping all charging processes and only maintaining normal monitoring); severe faults (such as emergency fault states, charging / discharging switch sticking, thermal runaway risk) correspond to immediate disconnection (simultaneously cutting off the charging / discharging circuit and triggering a vehicle safety warning).

[0041] In this embodiment, during the charging control process in step 30, the battery fault status in the health status is monitored in real time. If no fault status requiring performance limitation or safety protection is detected (health status is good), the normal main charging mode is maintained. If the battery temperature is detected to rise to 56°C during charging (triggering a temperature warning, minor fault), the charging parameters are immediately adjusted to perform derating charging: the charging current is reduced from 10A to 8A, the upper limit of the charging voltage is reduced from 14.4V to 14.0V, and the parameter monitoring frequency is increased (from 10ms / time to 5ms / time). If the temperature drops below 55°C (fault resolved), the normal main charging mode is restored. If the battery temperature is detected to rise to 60°C (triggering an emergency fault, severe fault), a disconnect command is immediately sent to the charging and discharging switch device to cut off the charging and discharging circuit. At the same time, an emergency safety warning signal is sent to the vehicle controller, recording the fault details (excessive temperature, risk of thermal runaway) and the execution status of the protection action. The changes in the fault status are continuously monitored until the temperature drops to a safe range, and then normal control is gradually restored according to the current battery status.

[0042] Step 4: Based on the charging, discharging, or power-locking control strategy of the low-voltage battery, control the vehicle's low-voltage battery management subsystem to switch between different power management modes. This step, based on the charging, discharging, or power-locking control strategy in step 3, and combined with the vehicle's network status, state of charge, and fault status, controls the vehicle's low-voltage battery management subsystem to precisely switch between normal operating mode, first-level sleep mode, second-level sleep mode, and periodic wake-up mode, achieving refined power consumption control and reliable sleep / wake-up management. Specifically, it consists of the following four sub-steps: Step 40: While the charging process is underway or the vehicle network is active, the vehicle's low-voltage battery management subsystem remains in normal operating mode; upon completion of charging, it determines whether to enter Level 1 sleep mode based on the network status and state of charge. The rules for maintaining the normal working mode and determining the first-level sleep condition are as follows: The normal working mode is maintained when the charging process in step 30 is being executed or the vehicle network is in an active state (such as the vehicle controller sending a network activation signal or a network wake-up triggered by user operation); The first-level sleep condition is that charging has been completed and the vehicle network is in a sleep state and the state of charge (SOC) is higher than the preset sleep threshold (set to 30% in this embodiment).

[0043] First, the current system status is monitored in real time: Since the charging process is in progress in step 30, the vehicle's low-voltage battery management subsystem continues to operate normally. At this time, all acquisition, diagnostic, and control modules operate at full power to ensure real-time charging control. When a charging completion signal is detected (SOC reaches 95%, and the DC-DC converter reports charging completion), the first-level sleep determination process is immediately initiated: First, the vehicle network status is queried to confirm that a vehicle network sleep command has been received (the vehicle controller sends the network sleep command after the user turns off the vehicle), and the network has had no data interaction for at least 5 minutes (the preset sleep determination time). Then, the SOC in the operating status is extracted to be 95% (higher than the preset sleep threshold of 30%), thus determining that the first-level sleep conditions are met, and preparations are made to enter the first-level sleep mode. If the network is still active at this time (e.g., the user has not turned off the vehicle), or the SOC is below 30%, the normal working mode will continue until the conditions change and a new judgment is made. When the SOC continues to decrease, the power-locking process will be entered (e.g., the power-locking is triggered when the SOC drops to 15%), and the relay will automatically disconnect. Since the condition for secondary hibernation is that the relay is disconnected (e.g., the relay is disconnected by command, or the relay is disconnected due to various fault protections), when the hibernation judgment time (2 minutes) is reached, the vehicle's low-voltage battery management subsystem will enter the secondary hibernation mode (ultra-low power standby).

[0044] Step 41: When the Level 1 hibernation conditions are met, the vehicle's low-voltage battery management subsystem enters Level 1 hibernation mode; if a network wake-up signal is detected during Level 1 hibernation, it exits to normal operating mode. Once step 40 determines that the conditions for Level 1 hibernation are met, the vehicle's low-voltage battery management subsystem immediately executes the Level 1 hibernation mode entry process: First, it shuts down non-essential working modules (such as the high-frequency data acquisition module and non-core communication modules), leaving only the low-power wake-up monitoring module and the core power management module running to reduce system energy consumption (hibernation power consumption ≤ 10mA); at the same time, it feeds back the status information of having entered Level 1 hibernation mode to the vehicle controller, recording data such as SOC=95%, battery temperature=28℃, and network status=hibernation at the time of hibernation entry.

[0045] After entering Level 1 sleep mode, the low-power wake-up monitoring module continuously listens for wake-up signals from the vehicle network bus (such as user remote unlock commands and vehicle controller wake-up commands), maintaining minimum power consumption during the listening process. If a valid network wake-up signal is detected (signal format and protocol conform to preset wake-up rules, such as user remote unlock commands), the sleep exit procedure is immediately triggered: the shut-down working modules are quickly started, and module initialization is completed (such as data acquisition channel calibration and communication link establishment); after all modules resume normal operation, the Level 1 sleep mode is exited, and the normal operation mode is returned; at the same time, the source of the wake-up signal (user remote control) and the wake-up time are recorded, the system operating status is updated to normal operation, and feedback is sent to the vehicle controller. If no wake-up signal is detected, the Level 1 sleep mode is maintained until the periodic wake-up conditions in step 42 are met or other wake-up trigger signals are received.

[0046] Step 42: If no wake-up signal is received continuously in Level 1 hibernation mode, and the preset periodic wake-up time has been reached since the last hibernation, the vehicle low-voltage battery management subsystem will perform a periodic wake-up. The pre-set periodic wake-up time is 2 hours (determined based on the self-discharge characteristics and sleep power consumption of the 12V lead-acid battery to ensure accurate monitoring of battery status during periodic wake-up), and the core monitoring tasks for periodic wake-up are configured as follows: SOC verification, battery fault status review, and core power supply voltage check.

[0047] After the vehicle's low-voltage battery management subsystem enters Level 1 hibernation mode, the built-in low-power timing module synchronously starts timing and continuously accumulates the hibernation time. If the network wake-up signal in step 41 is not detected during Level 1 hibernation and the timing time reaches 2 hours, the periodic wake-up process is immediately executed: first, the core monitoring module is started, and non-core modules are temporarily kept off to control energy consumption; then, the core monitoring task is executed: the filtered data of the current battery's SOC=92% (slight decrease due to self-discharge), voltage=13.8V, and temperature=26℃ are extracted, the battery fault status is verified to be fault-free, and the subsystem core power supply voltage is checked to be 3.3V (normal). After monitoring is completed, the monitoring results are evaluated: if SOC=92% is still higher than the preset sleep threshold of 30%, there are no new faults, and the core power supply is normal, then it is determined that continuous wake-up is not necessary, and the sleep return process is executed: the core monitoring module is shut down, the system re-enters the first-level sleep mode, and the periodic wake-up timer is reset; if an anomaly is detected (such as SOC=28% being lower than the sleep threshold, a fault warning appearing, or an abnormal core power supply), then the continuous wake-up process is triggered: all working modules are started, the system returns to normal working mode, and the abnormal information detected by the periodic wake-up is recorded, a warning is sent to the vehicle controller, and if necessary, the intelligent power replenishment process in step 31 or other protective controls are triggered. All data of the entire periodic wake-up process (wake-up time, monitoring results, and subsequent processing actions) are synchronously recorded in the system log for easy subsequent fault tracing and status analysis.

[0048] Step 43: When the Level 2 hibernation conditions are met, the vehicle's low-voltage battery management subsystem enters Level 2 hibernation ultra-low power mode and no longer listens to vehicle network signals. At this time, the low-voltage battery management subsystem, which has entered Level 2 hibernation, can be woken up via hard-wire or jump-start. When step 40 determines that the conditions for Level 2 hibernation are met (the relay is disconnected, such as when the SOC drops to 15% and triggers power lock-up, the relay automatically disconnects, and the hibernation determination time reaches 2 minutes), the vehicle's low-voltage battery management subsystem immediately executes the Level 2 hibernation mode entry process: first, it shuts down non-essential working modules (such as the high-frequency data acquisition module and the network communication module), and only keeps the core power management module running to reduce system energy consumption (Level 2 hibernation power consumption ≤ 5mA); at the same time, it records data such as SOC = 15% and battery temperature = 25℃ at the time of hibernation entry.

[0049] Upon entering Level 2 sleep mode, the core power management module continuously monitors the hardwire signal and the terminal jumper status. If a predefined hardwire wake-up signal is detected (e.g., a user triggering the hardwire wake-up button) or a terminal jumper is detected (when the battery is deeply discharged, jump-started via an external power source), the sleep exit procedure is immediately triggered: The shut-down modules are quickly restarted, and module initialization is completed (e.g., data acquisition channel calibration, communication link establishment). After all modules return to normal operation, Level 2 sleep mode is exited, and normal operation mode is resumed (synchronously updating SOC, battery temperature, and other data). Simultaneously, the wake-up signal source (hardwire wake-up or jump-start wake-up) and wake-up time are recorded, the system operating status is updated to normal operation, and feedback is sent to the vehicle controller. If no wake-up signal is detected, Level 2 sleep mode is maintained until a hardwire wake-up signal is received or a jump-start is initiated.

[0050] This embodiment achieves high-precision collaborative sensing, multi-state coupling estimation, dynamic strategy control, and system-level power consumption management of low-voltage batteries through the above four steps. It effectively solves the problems of insufficient real-time data acquisition, high false alarm rate in fault diagnosis, large state estimation error, fixed control strategy, and weak system wake-up and recovery capability in traditional low-voltage battery management solutions. It improves the state estimation accuracy and service life of low-voltage batteries, enhances the safety and reliability of the power system, optimizes the static energy consumption and energy utilization efficiency of the vehicle, and improves the user experience and maintenance convenience of the vehicle.

[0051] The low-voltage battery control system includes: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0052] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A low-voltage battery control method, applied to a vehicle low-voltage battery management subsystem, the method comprising: Step 1: Obtain the status data of the low-voltage battery and perform diagnosis based on the status data to determine the health status of the low-voltage battery. Step 2: Based on the health status and the status data, estimate the operating status of the low-voltage battery; Step 3: Based on the operating status and health status, dynamically execute the charging, discharging, or power-locking control strategy for the low-voltage battery; Step 4: Based on the charging, discharging, or power-locking control strategy of the low-voltage battery, control the vehicle's low-voltage battery management subsystem to switch between different power management modes.

2. The low-voltage battery control method according to claim 1, characterized in that, Step 1: Obtain the status data of the low-voltage battery and perform diagnostics based on the status data to determine the health status of the low-voltage battery, including: Step 11: Collect state data of individual cell voltage, total voltage, current and temperature parameters of low-voltage batteries in parallel with different preset sampling periods; perform moving average filtering on the state data to obtain filtered data; Step 12: Based on the filtered data, identify the status of the sampling circuit, switching devices and communication links, and generate a comprehensive hardware fault code; Step 13: Based on the hardware fault codes and filtered data, perform battery body fault diagnosis to generate battery fault status; Step 14: Determine the health status of the low-voltage battery based on the hardware fault codes and battery fault status.

3. The low-voltage battery control method according to claim 2, characterized in that, The operating states include at least the charged state and the power state.

4. The low-voltage battery control method according to claim 3, characterized in that, Step 11: Collect state data of individual cell voltage, total voltage, current and temperature parameters of low-voltage batteries in parallel with different preset sampling periods; The state data is filtered using a moving average to obtain filtered data, including: Step 110: Initiate multiple data acquisition tasks in parallel. The voltage and current parameters are acquired using a first preset period, and the temperature parameters are acquired using a second preset period that is longer than the first preset period. The corresponding raw sampling data are obtained respectively. Step 111: For the raw sampling data acquired by each acquisition task, perform a moving average calculation using multiple consecutive sampling values ​​within a preset time window to generate preliminary filtered data. Step 112: Perform validity verification on each preliminary filtered data to obtain verified data; use the verified data as the filtered data.

5. The low-voltage battery control method according to claim 4, characterized in that, Step 12: Based on the filtered data, identify the status of the sampling circuit, switching devices, and communication link, and generate a comprehensive hardware fault code, including: Step 120: Based on the individual voltages and total voltage in the filtered data, by comparing the voltage sampled values ​​with a preset reasonable range and trend, diagnose whether there is an open circuit or a short circuit to ground / power supply in each voltage sampling channel, and generate a first type of hardware fault code. Step 121: Based on the current value and switching device temperature in the filtered data, and combined with the control command status of the charging and discharging switch, diagnose whether the charging and discharging switch device has a sticking or unexpected disconnection fault, and generate a second type of hardware fault code. Step 122: Based on the communication verification results with the front-end acquisition chip and the voltage status output by the power monitoring chip, diagnose whether the communication link and core power supply are abnormal and generate a third type of hardware fault code. Step 123: Based on the first type of hardware fault code, the second type of hardware fault code, and the third type of hardware fault code, a comprehensive hardware fault code is formed to characterize the overall status of the sampling circuit, switching devices, and communication link.

6. The low-voltage battery control method according to claim 5, characterized in that, Step 13: Based on the hardware fault codes and filtered data, perform battery body fault diagnosis to generate the battery fault status, including: Step 130: Based on the filtered data, perform first-level fault diagnosis to determine whether the individual cell voltage, total voltage, current and temperature of the low-voltage battery exceed the preset real-time warning threshold. If they exceed the threshold, generate a warning-level fault state. Step 131: Based on the filtered data and the early warning level fault status, perform a second-level fault diagnosis to obtain a diagnosis result; Step 132: Based on the filtered data, integrated hardware fault codes, and diagnostic results, perform a third-level safety protection diagnosis to generate an emergency fault state; Step 133: Based on the warning-level fault status, diagnostic results, and emergency fault status, a battery fault status is formed.

7. The low-voltage battery control method according to claim 6, characterized in that, Step 3: Based on the operating status and health status, dynamically execute the charging, discharging, or power-locking control strategy for the low-voltage battery, including: Step 30: Based on the state of charge, power status and battery fault status in the operating state, determine whether the preset charging conditions are met; if so, select the pre-charge, main charge or float charge mode according to the current battery voltage and temperature, and send the corresponding charging request to the DC-DC converter to execute the charging process. Step 31: During the charging process or routine monitoring, if the state of charge is detected to be lower than the first preset threshold, local wake-up is triggered and intelligent charging is started, controlling the DC-DC converter to charge the low-voltage battery until the state of charge reaches the preset charging target value. Step 32: During the discharge process or normal monitoring, if the state of charge is detected to be lower than the second preset threshold below the first preset threshold, a discharge power-locking warning is first issued; if the state of charge is not effectively improved within the warning period, a discharge power-locking operation is performed to control the disconnection of the discharge switching device to prohibit discharge. Step 33: When performing the above charging or discharging control, if a fault state requiring performance limitation or safety protection is detected in the health status, a protective control strategy of derating charging, prohibiting charging, or immediately disconnecting is executed according to the fault level.

8. The low-voltage battery control method according to claim 7, characterized in that, Step 4: Based on the charging, discharging, or power-locking control strategy of the low-voltage battery, control the vehicle's low-voltage battery management subsystem to switch between different power management modes, including: Step 40: When the charging process is being executed or the vehicle network is in an active state, the vehicle low-voltage battery management subsystem remains in normal working mode; when charging is complete, it determines whether to enter the first-level sleep mode based on the network status and state of charge. Step 41: When the first-level hibernation conditions are met, the vehicle's low-voltage battery management subsystem enters the first-level hibernation mode; if a network wake-up signal is detected during the first-level hibernation period, it exits to the normal operating mode. Step 42: If no wake-up signal is received in the first-level sleep mode and the preset periodic wake-up time has been reached since the last sleep, the vehicle low-voltage battery management subsystem shall perform periodic wake-up. Step 43: When the conditions for Level 2 hibernation are met, the vehicle's low-voltage battery management subsystem enters Level 2 hibernation ultra-low power mode and no longer listens to the vehicle network signal; the low-voltage battery management subsystem enters Level 2 hibernation by hard-wire wake-up or jump-start wake-up.

9. A low-voltage battery control system, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 9.

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