A static electric quantity balance control method, device and medium for a light commercial electric vehicle

Abstract

The application discloses a static electric quantity balance control method and device for a light commercial electric vehicle and a medium, relates to the technical field of electric quantity control, and comprises the following steps: collecting multi-source state parameters under a static working condition of the vehicle; generating a power supply mode decision instruction corresponding to the multi-source state parameters based on a preset power supply priority rule and a dynamic balance strategy; calculating a state of charge difference index based on the state of charge of each power battery, and generating a dynamic grouping balance instruction when the state of charge difference index exceeds a preset difference threshold; performing safety risk judgment based on environmental temperature and insulation impedance data, in combination with voltage and temperature change trends of each power battery, and generating a safety protection instruction when it is judged that there is a safety risk; and controlling the electric quantity balance system of the vehicle to enter different levels of sleep states or to be awakened from the sleep states according to the duration of the static working condition and the change of real-time power demand.

Description

Technical Field

[0001] This application relates to the field of power control technology, and in particular to a static power balance control method, device and medium for light commercial electric vehicles. Background Technology

[0002] With the increasing prevalence of light commercial electric vehicles such as new energy logistics vehicles and cold chain delivery vehicles, the energy management issues of these vehicles under static operating conditions are becoming increasingly prominent. These vehicles often involve numerous parking operations in actual operation, such as loading and unloading goods, waiting for dispatch, or parking at night. During these periods, onboard low-voltage systems such as refrigeration units, communication modules, and monitoring equipment still need to operate continuously. Current mainstream battery management systems primarily optimize energy dispatch for vehicle driving conditions. Under static operating conditions, they typically only maintain basic low-voltage power supply, lacking refined control strategies for the coordinated dispatch of multi-source energy. This results in the power battery frequently being in a shallow charge and discharge state during static standby, causing not only considerable energy loss but also accelerating battery cycle life degradation. Summary of the Invention

[0003] This application provides a static power balance control method, device, and medium for light commercial electric vehicles to solve the above-mentioned technical problems.

[0004] On one hand, embodiments of this application provide a static power balance control method for light commercial electric vehicles, including:

[0005] Real-time acquisition of multi-source state parameters under static vehicle conditions; the multi-source state parameters include the state of charge and health of each power battery, the state of charge of the low-voltage battery, the real-time power demand of the vehicle load, the ambient temperature, and the insulation impedance data of the high-voltage circuit.

[0006] Based on preset power supply priority rules and dynamic balancing strategies, power supply mode decision instructions corresponding to the multi-source state parameters are generated to control the switching of power supply paths between the power battery, low-voltage battery and solar power module to adapt to the real-time power demand; the power supply mode decision instructions are used to instruct at least one of the power battery pack, the low-voltage battery or the external power supply device to supply power to the vehicle load.

[0007] Based on the state of charge of each power battery, a state of charge difference index is calculated. When the state of charge difference index exceeds a preset difference threshold, a dynamic group balancing command is generated to control the energy transfer between battery packs and reduce the state of charge difference between battery packs.

[0008] Based on the ambient temperature and insulation impedance data, and combined with the voltage and temperature change trends of each power battery, a safety risk assessment is performed. When a safety risk is identified, a safety protection command is generated to adjust the system operating parameters or trigger a protection action.

[0009] Based on the duration of the static operating condition and the changes in the real-time power demand, the vehicle's power balancing system is controlled to enter different levels of sleep state or be awakened from sleep state.

[0010] In one implementation of this application, a power supply mode decision instruction corresponding to the multi-source state parameters is generated based on a preset power supply priority rule and a dynamic balancing strategy, specifically including:

[0011] The system acquires the real-time power demand, the overall state of charge of the power battery pack, the state of charge of the low-voltage battery, and the current output power of the external power supply device.

[0012] Determine whether the real-time power demand is less than or equal to a preset first power threshold, and determine whether the current output power of the external power supply device is greater than zero. If so, generate a first decision instruction; the first decision instruction is used to instruct the external power supply device to supply power to the vehicle load.

[0013] If not, it is determined whether the real-time power demand is greater than the preset first power threshold, and whether the overall state of charge of the power battery pack is greater than the preset first charge threshold. If so, a second decision instruction is generated; the second decision instruction is used to instruct the power battery pack to supply power to the vehicle load.

[0014] If the real-time power demand is greater than the preset first power threshold and the overall state of charge of the power battery pack is less than or equal to the preset first energy threshold, then it is determined whether the state of charge of the low-voltage battery is greater than the preset second energy threshold. If so, a third decision instruction is generated. The third decision instruction is used to instruct the low-voltage battery to supply power to the vehicle load.

[0015] If the state of charge of the low-voltage battery is less than or equal to the preset second charge threshold, a fourth decision instruction is generated; the fourth decision is used to instruct the initiation of a forced charging process, in which the power battery pack or the external charging device charges the low-voltage battery.

[0016] In one implementation of this application, a state-of-charge (POC) difference index is calculated based on the POC of each power battery. When the POC difference index exceeds a preset difference threshold, a dynamic group balancing command is generated to control energy transfer between battery packs and reduce the POC difference between battery packs. Specifically, this includes:

[0017] Calculate the standard deviation of the state of charge (SOC) of all power batteries in the power battery pack, and use the standard deviation as an index of SOC difference.

[0018] The state of charge difference index is compared with a preset difference threshold.

[0019] When the state of charge difference index is greater than the preset difference threshold, all power batteries are divided into multiple equalization subgroups according to the state of charge value of each power battery, so that the difference between the maximum and minimum state of charge values ​​of the power batteries in the same equalization subgroup is less than or equal to the preset grouping tolerance threshold.

[0020] A dynamic group balancing command is generated to perform bidirectional energy transfer between and within multiple balancing subgroups via a bidirectional DC-DC converter; the dynamic group balancing command includes multiple balancing subgroup division information and target balancing current.

[0021] In one implementation of this application, generating dynamic packet equalization instructions specifically includes:

[0022] Obtain the state of charge, temperature gradient, and health status of each power battery;

[0023] The state of charge, temperature gradient, and health status of each power battery are input to the fuzzy controller to perform fuzzification processing, fuzzy rule reasoning, and defuzzification calculation on the state of charge, temperature gradient, and health status, and output the control value of the target equalization current.

[0024] The information from multiple equalization subgroups is integrated with the control value of the target equalization current to form a dynamic group equalization command.

[0025] In one implementation of this application, a safety risk assessment is performed based on the ambient temperature and the insulation resistance data, combined with the voltage and temperature variation trends of each power battery. Specifically, this includes:

[0026] A high-frequency AC signal is injected into the vehicle's high-voltage circuit, and the response of the high-frequency AC signal is detected.

[0027] Perform spectral analysis on the detected response signal to calculate the current insulation impedance value of the high-voltage circuit;

[0028] The current insulation impedance value is compared with a preset multi-level insulation impedance threshold, and the insulation fault level is determined based on the comparison result; the insulation fault level includes a first-level alarm and a second-level alarm.

[0029] The voltage fluctuation rate and temperature rise rate of each power battery are calculated in real time, and combined with the ambient temperature and gas sensing data, the data are input into a pre-trained long short-term memory network prediction model to output the current probability value of thermal runaway risk.

[0030] In one implementation of this application, when a security risk is determined to exist, a security protection instruction is generated, specifically including:

[0031] When the insulation fault level is determined to be a Level 1 alarm, a first safety command is generated; the first safety command is used to instruct the power supply switching module to cut off the power supply to non-critical vehicle loads;

[0032] When the insulation fault level is determined to be a level 2 alarm, a second safety command is generated; the second safety command is used to instruct the power supply switching module to perform a full power-off operation of the high-voltage system and start the system self-test program;

[0033] When the probability value of thermal runaway risk is greater than the preset first risk probability threshold, a third safety command is generated; the third safety command is used to instruct the vehicle to increase the heat dissipation power to the maximum value and trigger the audible and visual alarm device.

[0034] When the probability value of thermal runaway risk is greater than a preset second risk probability threshold and less than or equal to the preset first risk probability threshold, a fourth safety instruction is generated; the fourth safety instruction is used to instruct the reduction of the equalization current and the increase of the temperature sampling frequency of each power battery.

[0035] In one implementation of this application, the vehicle's battery balancing system is controlled to enter different levels of sleep states or be awakened from a sleep state based on the duration of the static operating condition and the change in the real-time power demand, specifically including:

[0036] Monitor the duration of vehicle stationary status and compare the duration of stationary status with a preset sleep time threshold;

[0037] When the duration of the static state is greater than a preset first sleep threshold and less than or equal to a preset second sleep threshold, a shallow sleep command is generated; the shallow sleep command is used to instruct the vehicle display screen and infotainment system to be turned off, while maintaining power supply to other vehicle loads and communication networks;

[0038] When the duration of the static state exceeds the preset second sleep threshold, a deep sleep command is generated; the deep sleep command is used to indicate that the high-voltage bus connection is disconnected, and only the standby of the controller local area network communication and low-power wireless communication module is maintained.

[0039] In one implementation of this application, it further includes:

[0040] In deep sleep mode, the low-power wireless communication module is controlled to send heartbeat packets to the remote monitoring platform at preset time intervals; the heartbeat packets contain vehicle identification and basic status data.

[0041] The low-power wireless communication module continuously monitors the remote wake-up command from the remote monitoring platform, the wake-up signal from the vehicle key, or the power surge signal from the vehicle load.

[0042] When any one of the remote wake-up command, the wake-up signal, or the power surge signal is detected, a system wake-up command is generated. The system wake-up command is used to instruct the restoration of the high-voltage bus connection and to power on and start the vehicle and each on-board load step by step, so that the vehicle exits the hibernation state.

[0043] On the other hand, this application also provides a static power balance control device for light commercial electric vehicles, the device comprising:

[0044] At least one processor;

[0045] And, a memory communicatively connected to the at least one processor;

[0046] The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform a static power balance control method for a light commercial electric vehicle as described above.

[0047] On the other hand, this application embodiment also provides a non-volatile computer storage medium storing computer-executable instructions, which, when executed, implement the static power balance control method for a light commercial electric vehicle as described above.

[0048] This application provides a static power balance control method, device, and medium for light commercial electric vehicles, which has at least the following beneficial effects:

[0049] By collecting multi-source state parameters of the vehicle under static conditions in real time, a comprehensive perception foundation for static conditions is constructed. Based on preset power supply priority rules and dynamic balancing strategies, power supply mode decision commands are generated to realize automatic switching of power supply paths between the power battery, low-voltage battery, and solar charging module. Based on the state of charge (SCC) of each power battery, the SCC difference index is calculated. By dynamically dividing battery cells with similar SCCs into the same balancing subgroup and controlling the bidirectional DC-DC converter to perform bidirectional energy transfer between and within subgroups, the SCC difference is quickly converged. Based on ambient temperature and insulation impedance data, combined with the voltage and temperature change trends of each power battery, safety risk assessment is performed, and corresponding safety protection commands are generated, enabling early detection and graded response to insulation faults. According to the duration of static conditions and changes in real-time power demand, the vehicle's power balancing system is controlled to enter different levels of sleep states or be awakened from sleep states. This can minimize static standby power consumption while ensuring the vehicle's basic functions and remote interconnection requirements, and avoid the impact on the battery caused by frequent awakenings through graded sleep strategies. Attached Figure Description

[0050] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0051] Figure 1 A flowchart illustrating a static power balance control method for a light commercial electric vehicle provided in this application embodiment;

[0052] Figure 2 This is a schematic diagram of the internal structure of a static power balance control device for a light commercial electric vehicle, provided as an embodiment of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0055] Figure 1 This is a flowchart illustrating a static power balance control method for a light commercial electric vehicle provided in an embodiment of this application.

[0056] The analysis method involved in the embodiments of this application can be implemented by a terminal device or a server, and this application does not impose any special limitations on it. For ease of understanding and description, the following embodiments are all described in detail using a server as an example.

[0057] It should be noted that the server can be a single device or a system composed of multiple devices, i.e., a distributed server. This application does not make any specific limitations on this.

[0058] like Figure 1 As shown in the embodiment of this application, a static power balance control method for a light commercial electric vehicle includes:

[0059] Step 101: Real-time acquisition of multi-source state parameters of the vehicle under static conditions.

[0060] It should be noted that the multi-source state parameters in the embodiments of this application include the state of charge and health of each power battery, the state of charge of the low-voltage battery, the real-time power demand of the vehicle load, the ambient temperature, and the insulation impedance data of the high-voltage circuit.

[0061] The present application discloses a static power balance control method for a light commercial electric vehicle, which is applied to a vehicle control system including a power battery pack, a low-voltage battery, a power supply switching module, a battery equalization module, a safety monitoring module, and a vehicle controller. The applicable vehicle types are light commercial electric vehicles, including electric vehicle models with long-time parking operation scenarios such as logistics distribution vehicles, cold-chain transport vehicles, and urban van trucks. The static condition refers to the condition where the vehicle is in a stationary state, or the vehicle speed is lower than a preset low-speed threshold such that the vehicle drive system does not output power. In this condition, although the vehicle does not move, the on-vehicle low-voltage electrical systems such as the refrigeration unit, communication module, monitoring equipment, cargo compartment lighting, etc. may still operate continuously, and energy management is required.

[0062] In this embodiment, the vehicle controller establishes communication connections with each functional module through the in-vehicle controller area network, and obtains various state parameters in real time at a preset sampling frequency, such as once per second or once per hundred milliseconds. The state of charge of each power battery is used to represent the percentage of the current remaining power of the power battery in its nominal capacity, and is the core index for judging the available energy of the battery. This data is calculated in real time by the battery management system and periodically sent to the CAN bus. The state of health of the power battery is used to represent the attenuation degree of the current actual capacity of the battery relative to the initial capacity at the factory, and reflects the aging condition of the battery. It is also estimated by the battery management system based on historical data such as the number of battery cycles, internal resistance change, capacity attenuation, etc. The state of charge of the low-voltage battery is used to represent the remaining power of the 12-volt or 24-volt low-voltage battery configured in the vehicle. This data can be obtained through the battery sensor supporting the low-voltage battery or the low-voltage battery management system. The real-time power demand of the on-vehicle load refers to the total instantaneous power consumed by all the low-voltage electrical devices running under the static condition, such as the operating power of the refrigeration compressor, the power of the ventilation fan, the self-consumption power of each control unit, the cargo compartment lighting power, etc. This data can be estimated by summarizing the rated power and operating state of each electrical device, or directly measured by setting current and voltage sensors on the low-voltage bus. The ambient temperature refers to the temperature of the environment where the battery pack is located, and can be collected through temperature sensors arranged inside the battery pack or outside the vehicle. The ambient temperature has an important impact on the battery performance and safety. High temperature will accelerate battery aging and increase the risk of thermal runaway, while low temperature will cause a decrease in the available capacity of the battery and an increase in internal resistance. The insulation impedance data of the high-voltage circuit is used to evaluate the insulation performance between the high-voltage system of the power battery and the vehicle chassis, and is the core monitoring index to ensure personal safety. This data is calculated by the insulation monitoring module by injecting a detection signal into the high-voltage circuit and analyzing the response signal.

[0063] For example, in the application scenario of a cold chain logistics vehicle, when the vehicle stops to unload at the delivery station, the vehicle is in a static operating condition, and the refrigeration unit continues to run to maintain the low temperature of the cargo compartment. At this time, the vehicle controller reads the SOC and SOH data of each power battery reported by the power battery management system, the low-voltage battery SOC data reported by the low-voltage battery sensor, the real-time power consumption reported by the refrigeration unit controller, the current ambient temperature value collected by the ambient temperature sensor, and the high-voltage circuit insulation impedance value periodically reported by the insulation monitoring module through the CAN bus to complete the data acquisition.

[0064] Step 102: Based on the preset power supply priority rules and dynamic balancing strategy, generate power supply mode decision instructions corresponding to multi-source state parameters to control the switching of power supply paths between the power battery, low-voltage battery and solar power module, and adapt to real-time power demand.

[0065] It should be noted that the power supply mode decision instruction in the embodiments of this application is used to instruct at least one of the power battery pack, low-voltage battery or external power supply device to supply power to the vehicle load.

[0066] In this embodiment, based on multi-source status parameters, a smart decision is made through preset power supply priority rules to determine which one or more power sources will supply power to the vehicle load, and corresponding path switching is performed, thereby maximizing energy utilization efficiency and protecting battery life while meeting load requirements.

[0067] Specifically, the first step is to obtain several key parameters: real-time power demand, the overall state of charge (SOC) of the power battery pack, the SOC of the low-voltage battery, and the current output power of the external charging device. Real-time power demand refers to the sum of instantaneous power consumed by all operating low-voltage electrical equipment under static conditions. This data can be estimated by summarizing the operating status and rated power of each electrical device, or directly measured by current and voltage sensors installed on the low-voltage bus. The overall SOC of the power battery pack characterizes the remaining charge level of the entire battery pack. It can be calculated by the battery management system based on the SOC of each individual battery cell using weighted averaging or minimum values, and is usually expressed as a percentage. The SOC of the low-voltage battery refers to the remaining charge of the 12V or 24V low-voltage battery configured in the vehicle, provided by a low-voltage battery sensor or the low-voltage battery management system. The current output power of the external charging device refers to the actual electrical power that the vehicle's solar charging module can output under current sunlight conditions. This data is calculated and reported in real-time by the solar controller using a maximum power point tracking algorithm.

[0068] After acquiring the above parameters, the vehicle controller performs a step-by-step judgment according to the preset power supply priority rules. First, it determines whether the real-time power demand is less than or equal to a preset first power threshold, and simultaneously determines whether the current output power of the external power supply device is greater than zero. The first power threshold is a preset power limit used to distinguish between low-power loads and high-power loads, and its value can be set according to the typical power distribution of the vehicle's low-voltage electrical system. If the real-time power demand is low (less than or equal to the first power threshold) and the solar system has power output (current output power greater than zero), it indicates that the load is small and clean energy is available, and the system should prioritize solar power supply. At this time, a first decision instruction is generated, which instructs the external power supply device (solar power) to directly supply power to the vehicle load. If the solar output power is greater than the load demand, the excess power can be used to supplement and charge the low-voltage battery through a bidirectional DC-DC converter, achieving full utilization of energy. For example, in a daytime parking scenario, if the refrigeration unit operates at a low power and there is sufficient sunlight, the system will prioritize solar power supply, completely avoiding the consumption of power from the power battery and low-voltage battery.

[0069] If the above conditions are not met—that is, the real-time power demand exceeds the first power threshold, or the output power of the external power supply device is less than or equal to zero (e.g., at night or on rainy days)—then the system proceeds to the second level of judgment. At this point, it checks whether the overall state of charge (SBC) of the power battery pack exceeds a preset first charge threshold. The first charge threshold is a preset lower limit for determining whether the power battery has sufficient power supply capability; it is typically set to the minimum charge value required to ensure continued vehicle operation. If the overall SBC of the power battery pack exceeds the first charge threshold, it indicates that the power battery has sufficient charge, and the system selects to supply power to the vehicle load by stepping down the voltage from the power battery pack through a bidirectional DC-DC converter. A second decision command is then generated, instructing the connection of the high-voltage DC side of the power battery pack to the bidirectional DC-DC converter and setting the converter to step-down mode to output stable low-voltage DC power to the low-voltage bus to meet the load demand.

[0070] If the real-time power demand exceeds the first power threshold and the overall state of charge (SBC) of the power battery pack is less than or equal to the first charge threshold (i.e., the power battery is low on charge), the system proceeds to the third level of judgment. At this point, it checks whether the SBC of the low-voltage battery is greater than a preset second charge threshold. The second charge threshold is a preset low-charge warning value used to determine whether the low-voltage battery has sufficient reserve power for use. If the SBC of the low-voltage battery is greater than the second charge threshold, it indicates that the low-voltage battery still has sufficient charge, and the system selects to use the low-voltage battery to power the vehicle load to avoid over-discharge of the power battery affecting the vehicle's subsequent driving capability. A third decision command is then generated, instructing the power supply switching module to directly connect the low-voltage battery to the low-voltage bus, allowing the low-voltage battery to assume the power supply task.

[0071] If none of the above conditions are met—namely, high real-time power demand, insufficient power battery charge, and low-voltage battery charge—the system determines that current power resources are strained and a forced charging process needs to be initiated to ensure subsequent power supply. At this point, a fourth decision instruction is generated, which directs the initiation of the forced charging process. The remaining power battery pack, with some charge, replenishes the low-voltage battery via a bidirectional DC-DC converter. Alternatively, if solar power output is available, it will be used first to supplement the charge, ensuring that the low-voltage battery can maintain the power supply requirements of the basic load and reserve necessary energy for subsequent battery balancing or vehicle starting.

[0072] Understandably, the aforementioned multi-level judgment logic covers various power supply scenarios, from optimal energy to emergency backup, achieving dynamic optimization of the power supply mode through step-by-step judgment. For example, in the actual application of a cold chain logistics vehicle, if the refrigeration unit operates at low power and there is ample sunlight during daytime unloading, the system executes the first-level judgment, using solar power; if solar power is unavailable at night but the power battery has sufficient charge, the system executes the second-level judgment, using the power battery; if the power battery is low but the low-voltage battery still has reserves, the system executes the third-level judgment, using the low-voltage battery; if both the power battery and the low-voltage battery are low, the system initiates a forced charging process, utilizing the limited remaining power battery charge or solar energy to replenish the low-voltage battery, ensuring uninterrupted power supply to the basic load. This application, while meeting load requirements, maximizes the cycle life of the power battery and improves the overall energy economy of the vehicle.

[0073] Step 103: Based on the state of charge of each power battery, calculate the state of charge difference index. When the state of charge difference index exceeds the preset difference threshold, generate a dynamic group balancing command to control the energy transfer between battery packs and reduce the state of charge difference between battery packs.

[0074] This application aims to address the issue of state-of-charge (SOC) differentiation within a battery pack under static operating conditions, caused by differences in self-discharge rates, uneven temperature distribution, and varying historical operating conditions among individual battery cells. If this differentiation is not addressed promptly, the usable capacity of the battery pack will be limited to the cell with the lowest SOC, and the risk of overcharging or over-discharging some cells will be exacerbated. Therefore, active balancing technology is needed to correct this.

[0075] In this embodiment, a state-of-charge (POC) difference index is calculated based on the POC of each individual power battery cell. First, the standard deviation of the POC of all individual power battery cells is used as the POC difference index. Standard deviation is a commonly used statistical indicator to measure the dispersion of data; a larger value indicates a more significant difference in POC between individual cells and a more severe inconsistency within the battery pack. This index is obtained by statistically calculating the current POC values ​​of all individual cells. After obtaining the POC difference index, it is compared with a preset difference threshold. The preset difference threshold is a pre-defined allowable deviation limit used to determine whether the current inconsistency is severe enough to require initiating equalization. This threshold is calibrated based on battery type, service life, and system design goals. If the POC difference index does not exceed the preset difference threshold, it indicates good battery pack consistency, and equalization is not required; the process can return to continue monitoring or proceed to subsequent steps. If the POC difference index exceeds the preset difference threshold, the equalization process is triggered.

[0076] After triggering the equalization process, all power batteries need to be dynamically grouped based on the current state of charge (SOC) value of each individual battery cell. Specifically, a grouping tolerance threshold is set to define the maximum permissible SOC difference within the same equalization subgroup. Starting with the power battery with the highest SOC, power batteries with SOC differences within the grouping tolerance threshold range are assigned to the first equalization subgroup. Then, the remaining power batteries are divided into second, third, and so on, until all power batteries are assigned to a specific equalization subgroup. This division method controls the SOC difference within the same equalization subgroup to within the grouping tolerance threshold, while the SOC difference between different equalization subgroups is relatively large. This grouping structure creates conditions for subsequent efficient inter-group energy transfer. It is understandable that the grouping tolerance threshold setting needs to balance equalization accuracy and implementation complexity. A smaller tolerance results in higher consistency within a subgroup but may lead to an excessive number of subgroups, increasing control complexity; a larger tolerance reduces the number of subgroups, but larger intra-group differences may affect the equalization effect. For example, in a battery pack consisting of twenty cells, if the state of charge of each cell is distributed between 85% and 95%, it may be divided into two or three equalization subgroups, and the maximum difference in the state of charge of each cell in each subgroup does not exceed a preset 2%.

[0077] Furthermore, generating dynamic group balancing commands requires two key pieces of information: first, the division information of multiple balancing subgroups, that is, clearly informing the battery balancing module which power battery cells belong to the same subgroup; second, the target balancing current, that is, the magnitude of the current that controls the bidirectional DC-DC converter to transfer energy between subgroups. For determining the target balancing current, this application employs a fuzzy control algorithm for intelligent adjustment to avoid the overshoot problem that may occur in traditional proportional-integral-derivative control during the balancing process, and to make the balancing process smoother and more efficient.

[0078] In determining the target equalization current, the state of charge (SOC), temperature gradient, and health status of each power battery are first acquired as input variables for the fuzzy controller. The temperature gradient refers to the temperature difference between different locations within the battery pack, reflecting the degree of temperature unevenness. Since temperature significantly affects the battery's chemical reaction rate and internal resistance, a large temperature gradient may exacerbate inconsistencies in SOC; therefore, it must be considered when determining the equalization current. The health status characterizes the degree of battery aging. Batteries with poor health have higher internal resistance and weaker current-accepting capacity, requiring appropriate current limiting during equalization to protect them. These data can be acquired in real-time through the battery management system. The three input variables are then fed into the fuzzy controller, which has a pre-set fuzzy inference system based on linguistic variables and fuzzy rules. Linguistic variables are tools for converting precise numerical values ​​into fuzzy concepts; for example, describing the SOC difference as small, medium, or large; the temperature gradient as low or high; and the health status as good, average, or poor. The fuzzy rule base contains a series of conditional statements. These rules are typically derived from expert experience and battery characteristics, such as "if the state-of-charge difference is large, the temperature gradient is low, and the battery health is good, then the balancing current is large," and "if the state-of-charge difference is small, the temperature gradient is high, and the battery health is poor, then the balancing current is small." The fuzzy controller first fuzzifies the input state-of-charge, temperature gradient, and health data, converting them into membership values ​​of corresponding linguistic variables. Then, it infers based on the fuzzy rule base, combining the activation degrees of each rule to obtain the fuzzy distribution of the output variable. Finally, through defuzzification calculation, the fuzzy distribution is converted into a precise target balancing current control value. This control value is output as a digital signal and can be directly used to drive the bidirectional DC-DC converter.

[0079] After obtaining the control value of the target equalization current, it is integrated with the previously generated equalization subgroup division information to form a complete dynamic group equalization command. The vehicle controller sends this command to the battery equalization module through the communication network. After parsing the command, the battery equalization module controls the bidirectional DC-DC converter to perform bidirectional energy transfer according to the specified subgroup division and current magnitude. The bidirectional DC-DC converter has the ability to flow energy in both directions, transferring electrical energy from high-state-of-charge (SOC) subgroups to low-SOC subgroups, or temporarily storing it in intermediate energy storage elements and then releasing it, thereby achieving energy redistribution and rapid convergence of SOC. Since the energy is not consumed but redistributed, the equalization efficiency is much higher than that of traditional passive equalization methods, and the energy recovery efficiency can reach a high level.

[0080] Understandably, this application achieves efficient balancing of battery state of charge (SOC) under static conditions by combining dynamic grouping and fuzzy control. For example, in the application scenario of a cold chain logistics vehicle, assuming that after a period of parking, the battery cells near the refrigeration unit maintain a higher SOC due to their lower temperature and lower self-discharge rate, while the battery cells farther from the refrigeration unit have a lower SOC due to their higher temperature and higher self-discharge rate, with the difference exceeding a preset threshold. Upon detecting this difference, a dynamic grouping balancing algorithm is activated, classifying cells with similar SOCs into high-charge and low-charge groups respectively. A fuzzy controller comprehensively considers the SOC difference, temperature gradient, and health status to calculate a suitable balancing current, and then controls a bidirectional DC-DC converter to transfer energy from the high-charge group to the low-charge group. After a period of balancing, the SOCs of each cell gradually converge to similar levels, eliminating inconsistencies and improving the overall performance and usable capacity of the battery pack.

[0081] Step 104: Based on ambient temperature and insulation impedance data, and combined with the voltage and temperature change trends of each power battery, a safety risk assessment is performed. When a safety risk is identified, a safety protection command is generated to adjust system operating parameters or trigger protection actions.

[0082] In this embodiment, for insulation safety monitoring, this application employs a high-frequency injection method for real-time detection of insulation impedance. First, the insulation monitoring module injects a high-frequency AC signal into the vehicle's high-voltage circuit. The frequency of this signal is selected to be in the kilohertz range, which effectively penetrates the distributed capacitance of the high-voltage circuit without interfering with the electromagnetic compatibility of the vehicle's normal operation. After the high-frequency AC signal is injected, a loop is formed between the high-voltage circuit and the vehicle chassis. Due to the distributed capacitance and insulation resistance between the high-voltage system and the chassis, the response of the injected signal will change in amplitude and phase with changes in the insulation state. The insulation monitoring module simultaneously detects the response signal of this high-frequency AC signal, that is, it collects the voltage and current responses generated in the loop after injection.

[0083] After acquiring the response signal, spectral analysis is required. Specifically, the time-domain response signal is converted into a frequency-domain signal, and the amplitude and phase information of the frequency components corresponding to the injection frequency are extracted. Based on this information, combined with the known parameters of the injected signal, the insulation impedance value of the current high-voltage circuit can be calculated. The advantage of this high-frequency injection method is that it maintains high-sensitivity monitoring capability even when the vehicle is stationary and the high-voltage system is unloaded, and it is not affected by distributed capacitance, thus accurately reflecting the true insulation condition.

[0084] After obtaining the current insulation resistance value, it is compared with a preset multi-level insulation resistance threshold, and the insulation fault level is determined based on the comparison result. In this embodiment, the insulation fault level includes a Level 1 alarm and a Level 2 alarm. A Level 1 alarm corresponds to a level where the insulation performance has significantly deteriorated but has not yet reached an immediate danger, such as when the insulation resistance drops to a level of several hundred ohms per volt; a Level 2 alarm corresponds to a level where there is a serious risk of leakage current requiring immediate intervention, such as when the insulation resistance drops to a level of tens of ohms per volt. Through this hierarchical setting, early warnings can be issued in the early stages of insulation performance deterioration, and decisive protective measures can be taken in the event of a serious fault.

[0085] In terms of thermal safety monitoring, this application employs a thermal runaway prediction model based on a long short-term memory network. Thermal runaway is the most severe failure mode for battery safety, and its occurrence is often accompanied by a series of observable characteristic changes, which exhibit a regularity over time. Specifically, the voltage fluctuation rate and temperature rise rate of each power battery are calculated in real time. Voltage fluctuation rate refers to the coefficient of variation or maximum rate of change of voltage per unit time, reflecting the stability of battery voltage; abnormal voltage fluctuations often occur before thermal runaway. Temperature rise rate refers to the magnitude of temperature increase per unit time, reflecting abnormal changes in the rate of heat generation inside the battery. Simultaneously, gas sensing data inside the battery pack is collected, such as characteristic gas data like carbon monoxide concentration or volatile organic compound concentration. These gases are characteristic products released during the decomposition of internal battery materials, and their appearance often indicates the early stages of thermal runaway. This real-time data, along with ambient temperature data, is input into the pre-trained long short-term memory network prediction model.

[0086] Long Short-Term Memory (LSTM) networks are deep learning models suitable for time series forecasting, possessing the ability to remember long-term dependencies. This model is trained using extensive historical data to learn the parameter variation patterns under normal battery operating conditions, as well as the characteristic patterns preceding thermal runaway. During model training, laboratory battery thermal runaway test data or edge case data collected from real vehicles can be used to iteratively optimize model parameters, enabling it to accurately identify early signs of thermal runaway. In actual operation, when features similar to historical failure modes appear in the input data sequence, the model outputs a thermal runaway risk probability value, ranging from zero to one, with higher values ​​indicating a higher likelihood of thermal runaway. This allows for early warning before thermal runaway actually occurs.

[0087] When insulation monitoring or thermal runaway prediction indicates a safety risk, corresponding safety protection commands need to be generated, and a tiered response strategy should be adopted to balance safety and availability. When the insulation fault level is determined to be a Level 1 alarm, a first safety command is generated. This first safety command instructs the power supply switching module to cut off power to non-critical onboard loads. Non-critical onboard loads refer to electrical equipment that is not essential for vehicle safety and cargo preservation, such as in-vehicle infotainment systems, ambient lighting, and unnecessary comfort features. By cutting off power to these loads, the total load on the high-voltage system can be reduced, minimizing the impact of leakage risks, while preserving power supply to critical loads such as the drive system, braking system, and refrigeration unit, ensuring that basic vehicle functions and cargo safety are not affected. Simultaneously, the first safety command triggers an abnormality report via the controller area network bus to the instrument panel or remote monitoring platform, reminding the driver or maintenance personnel to arrange for timely inspection.

[0088] When an insulation fault level is determined to be a Level 2 alarm, a second safety command is generated. This second safety command instructs the power supply switching module to perform a complete power-off operation on the high-voltage system, i.e., disconnecting the main positive and negative relays to completely isolate the power battery from the high-voltage circuit, cutting off the leakage path and ensuring personal safety. Simultaneously, the second safety command triggers the system self-test program, records the fault code, and attempts to analyze the cause of the fault using built-in diagnostic logic, such as locating which high-voltage component or section of high-voltage line has experienced insulation degradation. This fault information will be stored and provided to technicians for reference during subsequent maintenance.

[0089] When the probability of thermal runaway exceeds a preset first risk probability threshold, a third safety command is generated. The first risk probability threshold is a high warning line, such as 0.7, indicating a very high probability of thermal runaway. The third safety command instructs the vehicle's thermal management system to operate at maximum power, such as activating the liquid cooling system at full speed or forcing the cooling fan to run at its highest speed, to maximize heat dissipation and suppress battery temperature rise, delaying or preventing thermal runaway. Simultaneously, the third safety command triggers audible and visual alarms, such as activating a buzzer or flashing warning lights, to alert nearby personnel to safety and evacuate quickly. If necessary, this command can also trigger the sending of emergency alarm information to a remote monitoring platform, notifying maintenance personnel to handle the situation promptly.

[0090] When the probability value of thermal runaway risk exceeds a preset second risk probability threshold but is less than or equal to a preset first risk probability threshold, a fourth safety command is generated. The second risk probability threshold is a low warning line, such as 0.4, indicating a potential risk that has not yet reached an emergency level. The fourth safety command instructs the battery balancing module to reduce the balancing current to decrease the additional heat generated during the balancing process and prevent the battery temperature from rising due to balancing heat. Simultaneously, the fourth safety command increases the temperature sampling frequency of each power battery, for example, from once per minute to once every ten seconds or higher, to more closely monitor temperature change trends and intervene promptly if the risk escalates. This progressive protection strategy allows for timely response at the initial stage of risk while avoiding unnecessary interference with normal vehicle operation due to overreaction.

[0091] Understandably, by combining high-frequency injection insulation monitoring with long short-term memory network thermal runaway prediction, this application achieves early detection and graded response to safety risks under static operating conditions. For example, in a scenario where the vehicle is parked at night, if the insulation performance at the high-voltage connector deteriorates due to damp weather, and the insulation monitoring module measures an insulation impedance below the first-level alarm threshold, the system generates a first safety command to disconnect non-critical loads and report the anomaly. If the insulation further deteriorates to below the second-level alarm threshold, the system immediately executes high-voltage power cut-off to ensure personal safety. Similarly, if an abnormal temperature rise occurs inside the battery pack, and the long short-term memory network model outputs a high risk probability in advance, the system can activate maximum power cooling and issue an audible and visual alarm in advance, effectively preventing thermal runaway accidents. If the risk probability is at a moderate level, the system takes preventative measures such as reducing the balancing current and strengthening monitoring to maintain the vehicle's normal standby state while ensuring safety.

[0092] Step 105: Based on the duration of static operating conditions and changes in real-time power demand, control the vehicle's power balancing system to enter different levels of sleep state or be awakened from sleep state.

[0093] In this embodiment, the first step is to monitor the vehicle's static duration. Static duration refers to the accumulated time from the end of the vehicle's last driving state to the start of its static state. The vehicle controller integrates a timing module or synchronizes with the system clock to record the elapsed time after the vehicle enters the static state. This duration is the core basis for determining whether the vehicle needs to enter hibernation and, if so, which hibernation level. Simultaneously, changes in real-time power demand must also be considered, but hibernation decisions are primarily based on static duration; power demand changes are more often used for wake-up decisions.

[0094] After obtaining the duration of stillness, it is compared with preset sleep time thresholds. These preset sleep time thresholds include a first sleep threshold and a second sleep threshold, where the first threshold is less than the second. These thresholds can be calibrated based on vehicle usage scenarios and energy consumption optimization goals. When the duration of stillness exceeds the preset first sleep threshold but is less than or equal to the preset second sleep threshold, the system determines that the vehicle is in a short-term stop state, such as when the driver temporarily leaves to load or unload goods, triggering a shallow sleep mode. In shallow sleep mode, a shallow sleep command is generated, instructing the system to shut down non-essential comfort and information loads such as the in-vehicle display and infotainment system. The in-vehicle display and infotainment system are major contributors to static power consumption; shutting them down effectively reduces energy consumption. Simultaneously, the command explicitly maintains power supply to other in-vehicle loads and communication networks. For example, critical equipment such as the refrigeration unit, anti-theft system, battery management system, and vehicle controller remain operational, and controller area network communication continues to ensure the vehicle can respond to load changes and remote queries at any time. Through shallow sleep, the system achieves initial energy savings while retaining essential functions.

[0095] When the duration of stillness exceeds a preset second sleep threshold, the system determines that the vehicle has entered a long-term parking state, such as overnight parking or weekend idleness, and triggers deep sleep mode. In deep sleep mode, a deep sleep command is generated, instructing the power supply switching module to disconnect the high-voltage bus connection. This cuts off the path between the power battery and the bidirectional DC-DC converter, causing the power battery to enter a near-zero current static state, thus completely eliminating static discharge. The system maintains only a low-power standby mode for controller area network communication and a standby mode for an independent low-power wireless communication module. The low-power wireless communication module can use, for example, LoRa technology, characterized by extremely low power consumption (microwatt level) and a certain long-distance communication capability, enabling it to maintain a connection with a remote monitoring platform with extremely low energy consumption. In deep sleep mode, the overall system power consumption is reduced to a minimum, retaining only the trace amount of power needed to maintain communication heartbeats and monitor wake-up signals, achieving maximum energy saving.

[0096] To maintain remote manageability and emergency response capabilities of the vehicle during deep sleep mode, a wake-up mechanism needs to be designed. Specifically, the low-power wireless communication module sends heartbeat packets to the remote monitoring platform at preset time intervals. The preset time interval can be set according to actual needs, such as sending once every ten minutes or every half hour. The heartbeat packet contains very concise information, including only the vehicle's unique identifier such as the vehicle identification number (VIN) and a very small amount of basic status data, such as the current timestamp, deep sleep status flag, and battery level overview, to minimize energy consumption for each communication. Through the heartbeat packet, the remote monitoring platform can confirm that the vehicle is online and obtain the most basic vehicle status information.

[0097] Meanwhile, the low-power wireless communication module continuously listens for external wake-up signals. These wake-up signals include three types: first, remote wake-up commands from a remote monitoring platform. For example, when a fleet administrator needs to check the real-time location of a vehicle, pre-cool the refrigerated compartment, or perform remote diagnostics, a wake-up command can be sent via the cloud, which is received and recognized by the low-power module; second, wake-up signals emitted by the vehicle key. When the driver approaches the vehicle and operates the key (e.g., pressing the unlock button), the radio frequency signal emitted by the key can be captured by the low-power wireless communication module or a dedicated key receiving module, generating a wake-up event; third, power surge signals from the vehicle's load. For example, when the refrigeration unit automatically starts due to reaching its temperature limit, the load power suddenly increases. This change can be detected by an ultra-low-power current monitoring circuit, which continuously monitors current changes on the low-voltage bus. When the detected current exceeds a preset wake-up threshold, a wake-up pulse is generated. These three wake-up methods together constitute a multi-source wake-up mechanism, ensuring that the vehicle can promptly exit sleep mode according to different needs.

[0098] When the low-power wireless communication module or related monitoring circuit detects any of the above-mentioned wake-up signals, it immediately generates a system wake-up command and sends it to the vehicle controller or power management unit. The system wake-up command instructs the power supply switching module to restore the high-voltage bus connection, i.e., to reconnect the power battery and the bidirectional DC-DC converter, allowing the power battery to resume external power supply. Subsequently, the vehicle controller starts each control unit and load sequentially according to a preset power-on sequence. For example, it first powers on key controllers such as the vehicle controller and battery management system, then initiates controller area network communication, and finally restores power supply to loads such as the refrigeration unit. This step-by-step power-on method avoids current surges, ensuring the system smoothly and orderly exits the hibernation state and returns to normal static monitoring mode or prepares to enter driving mode.

[0099] Understandably, through the aforementioned graded hibernation and multi-source wake-up mechanism, this application achieves an optimal balance between power consumption and functionality under static operating conditions. For example, in a scenario where a logistics vehicle is parked for two days over the weekend, the vehicle enters shallow hibernation after one hour of parking, turning off the display and entertainment system, but the refrigeration unit and communication network continue to operate normally; after three hours, it enters deep hibernation, disconnecting the high-voltage bus and maintaining only the low-power communication module in standby mode. During this period, the low-power module sends a heartbeat packet every ten minutes to remain online and manageable, while continuously listening for wake-up signals. If the administrator issues a pre-cooling command via the cloud before the vehicle departs on Monday, the vehicle is immediately woken up and the refrigeration unit starts, so the cargo compartment is pre-cooled by the time the driver arrives; if the driver arrives early and presses the key unlock button, the vehicle is also woken up and power is restored. In this way, both ease of use is ensured, and the static power consumption over two days is controlled to an extremely low level, effectively guaranteeing the available power when the vehicle is ready to depart.

[0100] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a static power balance control device for light commercial electric vehicles, the structure of which is as follows: Figure 2 As shown.

[0101] Figure 2 This is a schematic diagram of the internal structure of a static power balance control device for a light commercial electric vehicle, provided as an embodiment of this application. Figure 2 As shown, the device includes:

[0102] At least one processor;

[0103] And, a memory that is communicatively connected to at least one processor;

[0104] The memory stores instructions that can be executed by at least one processor, and the instructions, when executed by at least one processor, enable at least one processor to:

[0105] Real-time acquisition of multi-source status parameters under static vehicle conditions; multi-source status parameters include the state of charge and health of each power battery, the state of charge of the low-voltage battery, the real-time power demand of the vehicle load, ambient temperature, and insulation impedance data of the high-voltage circuit.

[0106] Based on preset power supply priority rules and dynamic balancing strategies, power supply mode decision instructions corresponding to multi-source state parameters are generated to control the switching of power supply paths between the power battery, low-voltage battery and solar power module, adapting to real-time power demand; the power supply mode decision instructions are used to indicate that at least one of the power battery pack, low-voltage battery or external power supply device supplies power to the vehicle load.

[0107] Based on the state of charge of each power battery, the state of charge difference index is calculated. When the state of charge difference index exceeds the preset difference threshold, a dynamic group balancing command is generated to control the energy transfer between battery packs and reduce the state of charge difference between battery packs.

[0108] Based on ambient temperature and insulation impedance data, combined with the voltage and temperature change trends of each power battery, a safety risk assessment is performed. When a safety risk is identified, a safety protection command is generated to adjust system operating parameters or trigger protection actions.

[0109] Based on the duration of static operating conditions and changes in real-time power demand, the vehicle's power balancing system is controlled to enter different levels of sleep states or be awakened from a sleep state.

[0110] This application also provides a non-volatile computer storage medium storing computer-executable instructions, which, when executed, can:

[0111] Real-time acquisition of multi-source status parameters under static vehicle conditions; multi-source status parameters include the state of charge and health of each power battery, the state of charge of the low-voltage battery, the real-time power demand of the vehicle load, ambient temperature, and insulation impedance data of the high-voltage circuit.

[0112] Based on preset power supply priority rules and dynamic balancing strategies, power supply mode decision instructions corresponding to multi-source state parameters are generated to control the switching of power supply paths between the power battery, low-voltage battery and solar power module, adapting to real-time power demand; the power supply mode decision instructions are used to indicate that at least one of the power battery pack, low-voltage battery or external power supply device supplies power to the vehicle load.

[0113] Based on the state of charge of each power battery, the state of charge difference index is calculated. When the state of charge difference index exceeds the preset difference threshold, a dynamic group balancing command is generated to control the energy transfer between battery packs and reduce the state of charge difference between battery packs.

[0114] Based on ambient temperature and insulation impedance data, combined with the voltage and temperature change trends of each power battery, a safety risk assessment is performed. When a safety risk is identified, a safety protection command is generated to adjust system operating parameters or trigger protection actions.

[0115] Based on the duration of static operating conditions and changes in real-time power demand, the vehicle's power balancing system is controlled to enter different levels of sleep states or be awakened from a sleep state.

[0116] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0117] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0118] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0123] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0124] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0125] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0126] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A static power balance control method for light commercial electric vehicles, characterized in that, The method includes: Real-time acquisition of multi-source state parameters under static vehicle conditions; the multi-source state parameters include the state of charge and health of each power battery, the state of charge of the low-voltage battery, the real-time power demand of the vehicle load, the ambient temperature, and the insulation impedance data of the high-voltage circuit. Based on preset power supply priority rules and dynamic balancing strategies, power supply mode decision instructions corresponding to the multi-source state parameters are generated to control the switching of power supply paths between the power battery, low-voltage battery and solar power module to adapt to the real-time power demand; the power supply mode decision instructions are used to instruct at least one of the power battery pack, the low-voltage battery or the external power supply device to supply power to the vehicle load. Based on the state of charge of each power battery, a state of charge difference index is calculated. When the state of charge difference index exceeds a preset difference threshold, a dynamic group balancing command is generated to control the energy transfer between battery packs and reduce the state of charge difference between battery packs. Based on the ambient temperature and insulation impedance data, and combined with the voltage and temperature change trends of each power battery, a safety risk assessment is performed. When a safety risk is identified, a safety protection command is generated to adjust the system operating parameters or trigger a protection action. Based on the duration of the static operating condition and the changes in the real-time power demand, the vehicle's power balancing system is controlled to enter different levels of sleep state or be awakened from sleep state.

2. The static power balance control method for a light commercial electric vehicle according to claim 1, characterized in that, Based on preset power supply priority rules and dynamic balancing strategies, power supply mode decision instructions corresponding to the multi-source state parameters are generated, specifically including: The system acquires the real-time power demand, the overall state of charge of the power battery pack, the state of charge of the low-voltage battery, and the current output power of the external power supply device. Determine whether the real-time power demand is less than or equal to a preset first power threshold, and determine whether the current output power of the external power supply device is greater than zero. If so, generate a first decision instruction; the first decision instruction is used to instruct the external power supply device to supply power to the vehicle load. If not, it is determined whether the real-time power demand is greater than the preset first power threshold, and whether the overall state of charge of the power battery pack is greater than the preset first charge threshold. If so, a second decision instruction is generated; the second decision instruction is used to instruct the power battery pack to supply power to the vehicle load. If the real-time power demand is greater than the preset first power threshold and the overall state of charge of the power battery pack is less than or equal to the preset first energy threshold, then it is determined whether the state of charge of the low-voltage battery is greater than the preset second energy threshold. If so, a third decision instruction is generated. The third decision instruction is used to instruct the low-voltage battery to supply power to the vehicle load. If the state of charge of the low-voltage battery is less than or equal to the preset second charge threshold, a fourth decision instruction is generated; the fourth decision instruction is used to instruct the initiation of a forced charging process, in which the power battery pack or the external charging device charges the low-voltage battery.

3. The static power balance control method for a light commercial electric vehicle according to claim 1, characterized in that, Based on the state of charge (SOC) of each power battery, a SOC difference index is calculated. When the SOC difference index exceeds a preset difference threshold, a dynamic group balancing command is generated to control energy transfer between battery packs and reduce the SOC difference between battery packs. Specifically, this includes: Calculate the standard deviation of the state of charge (SOC) of all power batteries in the power battery pack, and use the standard deviation as an index of SOC difference. The state of charge difference index is compared with a preset difference threshold. When the state of charge difference index is greater than the preset difference threshold, all power batteries are divided into multiple equalization subgroups according to the state of charge value of each power battery, so that the difference between the maximum and minimum state of charge values ​​of the power batteries in the same equalization subgroup is less than or equal to the preset grouping tolerance threshold. A dynamic group balancing command is generated to perform bidirectional energy transfer between and within multiple balancing subgroups via a bidirectional DC-DC converter; the dynamic group balancing command includes multiple balancing subgroup division information and target balancing current.

4. The static power balance control method for a light commercial electric vehicle according to claim 3, characterized in that, Generate dynamic group balancing instructions, specifically including: Obtain the state of charge, temperature gradient, and health status of each power battery; The state of charge, temperature gradient, and health status of each power battery are input to the fuzzy controller to perform fuzzification processing, fuzzy rule reasoning, and defuzzification calculation on the state of charge, temperature gradient, and health status, and output the control value of the target equalization current. The information from multiple equalization subgroups is integrated with the control value of the target equalization current to form a dynamic group equalization command.

5. The static power balance control method for a light commercial electric vehicle according to claim 1, characterized in that, Based on the ambient temperature and insulation resistance data, and combined with the voltage and temperature variation trends of each power battery, a safety risk assessment is performed, specifically including: A high-frequency AC signal is injected into the vehicle's high-voltage circuit, and the response of the high-frequency AC signal is detected. Perform spectral analysis on the detected response signal to calculate the current insulation impedance value of the high-voltage circuit; The current insulation impedance value is compared with a preset multi-level insulation impedance threshold, and the insulation fault level is determined based on the comparison result; the insulation fault level includes a first-level alarm and a second-level alarm. The voltage fluctuation rate and temperature rise rate of each power battery are calculated in real time, and combined with the ambient temperature and gas sensing data, the data are input into a pre-trained long short-term memory network prediction model to output the current probability value of thermal runaway risk.

6. The static power balance control method for a light commercial electric vehicle according to claim 5, characterized in that, When a security risk is identified, a security protection command is generated, which includes: When the insulation fault level is determined to be a Level 1 alarm, a first safety command is generated; the first safety command is used to instruct the power supply switching module to cut off the power supply to non-critical vehicle loads; When the insulation fault level is determined to be a level 2 alarm, a second safety command is generated; the second safety command is used to instruct the power supply switching module to perform a full power-off operation of the high-voltage system and start the system self-test program; When the probability value of thermal runaway risk is greater than the preset first risk probability threshold, a third safety command is generated; the third safety command is used to instruct the vehicle to increase the heat dissipation power to the maximum value and trigger the audible and visual alarm device. When the probability value of thermal runaway risk is greater than a preset second risk probability threshold and less than or equal to the preset first risk probability threshold, a fourth safety instruction is generated; the fourth safety instruction is used to instruct the reduction of the equalization current and the increase of the temperature sampling frequency of each power battery.

7. The static power balance control method for a light commercial electric vehicle according to claim 1, characterized in that, Based on the duration of the static operating condition and the changes in real-time power demand, the vehicle's battery balancing system is controlled to enter different levels of sleep states or be awakened from a sleep state, specifically including: Monitor the duration of vehicle stationary status and compare the duration of stationary status with a preset sleep time threshold; When the duration of the static state is greater than a preset first sleep threshold and less than or equal to a preset second sleep threshold, a shallow sleep command is generated; the shallow sleep command is used to instruct the vehicle display screen and infotainment system to be turned off, while maintaining power supply to other vehicle loads and communication networks; When the duration of the static state exceeds the preset second sleep threshold, a deep sleep command is generated; the deep sleep command is used to indicate that the high-voltage bus connection is disconnected, and only the standby of the controller local area network communication and low-power wireless communication module is maintained.

8. The static power balance control method for a light commercial electric vehicle according to claim 7, characterized in that, The method further includes: In deep sleep mode, the low-power wireless communication module is controlled to send heartbeat packets to the remote monitoring platform at preset time intervals; the heartbeat packets contain vehicle identification and basic status data. The low-power wireless communication module continuously monitors the remote wake-up command from the remote monitoring platform, the wake-up signal from the vehicle key, or the power surge signal from the vehicle load. When any one of the remote wake-up command, the wake-up signal, or the power surge signal is detected, a system wake-up command is generated. The system wake-up command is used to instruct the restoration of the high-voltage bus connection and to power on and start the vehicle and each on-board load step by step, so that the vehicle exits the hibernation state.

9. A static power balance control device for light commercial electric vehicles, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform a static power balance control method for a light commercial electric vehicle as described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed, they implement a static power balance control method for a light commercial electric vehicle as described in any one of claims 1-8.

Images