New energy automobile intelligent charging management system

Abstract

The invention discloses an intelligent charging management system of a new energy automobile, and relates to the technical field of charging control of the new energy automobile, and the system comprises a user identification and authentication module which supports three authentication modes, account association related information and authentication encryption transmission; the charging demand prediction module collects multiple types of data, and predicts charging related parameters through a fusion algorithm; the charging pile and battery state monitoring module collects various operation data in real time; the power grid load sensing module collects power grid information and synchronizes peak and valley periods; the charging strategy optimization module dynamically adjusts a charging scheme in combination with multi-source data; the safety protection module has six protection functions and a fault diagnosis capability; the data storage module adopts a distributed database and an encryption technology; the man-machine interaction module supports multiple operation modes and displays key information; and the communication coordination module is responsible for multi-terminal data interaction and protocol conversion. According to the invention, intelligence and accuracy of charging management are improved, safety protection and data safety are enhanced, and multi-scene charging requirements are met.

Description

TECHNICAL FIELD

[0001] The application relates to the field of new energy vehicle charging control technology, and particularly relates to a new energy vehicle intelligent charging management system. BACKGROUND

[0002] With the rapid growth of the number of new energy vehicles, the intelligent level and management efficiency of charging infrastructure have become key factors restricting the development of the industry. The current charging management systems on the market generally have the problem of single user authentication mode, most of which only support single verification of card swiping or APP, lack multi-mode collaborative authentication mechanism, and lead to complicated authentication process and slow response, especially in peak periods, which is prone to queuing and congestion. At the same time, the prediction of user charging demand lacks accuracy, and the existing systems mostly roughly estimate the charging capacity based on the current SOC value of the battery, without fully combining multi-dimensional information such as user driving habits, real-time traffic, historical charging data, etc., leading to mismatch between charging time and actual demand, either insufficient charging affecting travel, or excessive charging causing battery wear and energy waste.

[0003] In terms of power grid and device cooperation, the existing systems lack dynamic perception and adaptive adjustment capability of power grid load, and a large number of vehicles concentrated charging is prone to cause power grid load overload and excessive voltage fluctuation, affecting the stable operation of the power grid; and the charging pile state monitoring only focuses on basic parameters such as output power, voltage and current, and lacks monitoring of key indicators such as the running state of the heat dissipation system and the fastening degree of the interface connection, which is prone to cause equipment failure in long-term operation. In terms of battery health management, the SOH evaluation of the existing systems mostly relies on cycle count statistics, without considering influencing factors such as charging and discharging rate, historical temperature, and single cell voltage consistency, the evaluation result has large deviation, potential battery faults cannot be found in time, and there is lack of targeted voltage equalization regulation and attenuation suppression strategy, which shortens the service life of the battery. In addition, the safety protection system has short boards, the insulation detection mostly adopts single detection method, the response speed is slow and the resolution is low, the fault diagnosis can only identify common fault types, and lacks cause analysis and traceability capability.

[0004] Data security and compatibility problems are also prominent, the data encryption of the existing systems mostly adopts single encryption mode, user privacy data and charging transaction data have the risk of leakage, and the data tampering is difficult to trace back without introducing a certificate storage mechanism; the protocol adaptation capability is insufficient, the communication protocols used by new energy vehicles and charging piles of different brands and models are different, leading to that part of the vehicles cannot charge normally, and the compatibility is poor. At the same time, the fault handling relies on manual operation and maintenance, lacks self-repairing function, and professional personnel are needed to handle slight faults, which prolongs the downtime of the equipment and increases the operation and maintenance cost; the man-machine interaction experience is poor, the accuracy rate of voice recognition is greatly reduced in a noisy environment, the touch response is delayed, the remote control function is imperfect, and it cannot meet the diversified operation needs of users. SUMMARY

[0005] This invention proposes an intelligent charging management system for new energy vehicles to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent charging management system for new energy vehicles, comprising the following modules:

[0007] The user identification and authentication module supports three authentication methods: license plate recognition, APP binding verification, and NFC card swiping. The account is associated with charging history, payment method and vehicle parameters. The authentication process is encrypted throughout the entire transmission.

[0008] The charging demand prediction module collects vehicle battery parameters, driving habit characteristics and real-time road condition information, and uses an LSTM neural network and random forest weighted fusion algorithm to predict the user's required charging amount, target full charge time and optimal charging power range.

[0009] The charging pile status monitoring module collects data in real time on the charging pile's output power, charging interface temperature, output voltage, output current, heat dissipation system operating parameters, and interface connection tightness, and transmits the data using CAN bus and ModbusTCP protocol.

[0010] The power grid load sensing module collects real-time power grid load rate, voltage fluctuation value, frequency deviation and peak-valley electricity price information through power line carrier communication, and automatically synchronizes with the power grid release standard during peak-valley periods.

[0011] The charging strategy optimization module combines charging demand forecast results, charging pile status data and grid load information to dynamically adjust the charging mode, charging power and charging start and stop time. The segmented charging mode dynamically switches the power according to the SOC range.

[0012] The battery status monitoring module collects real-time data on individual battery cell voltage, total voltage, SOC value, SOH value, internal temperature of the battery pack, and voltage balance. Temperature collection points cover the positive and negative electrodes, the middle and edge areas.

[0013] The safety protection and fault diagnosis module has six protection functions: overcurrent, overtemperature, overvoltage, undervoltage, short circuit, and insulation detection. The fault diagnosis adopts the fault tree analysis method to generate fault codes and cause identifiers in real time.

[0014] The data storage and management module uses the MongoDB distributed database to store user information, charging data, equipment operation data and fault records, and uses the AES-256 encryption algorithm to protect data transmission and storage.

[0015] The human-computer interaction module is equipped with a touch screen, supports voice interaction and remote APP control, and displays charging power, duration, charged amount, remaining time and account balance in real time.

[0016] The communication coordination module is responsible for data interaction between modules and communication between the system and new energy vehicles, power grid dispatch center, and cloud management platform, and has protocol conversion function.

[0017] Furthermore, it also includes a dynamic charging load prediction unit. The prediction unit constructs a load prediction model based on historical power grid load data, regional charging demand distribution, meteorological data, and holiday factors. It improves prediction accuracy by dynamically adjusting the model weight coefficients, and the prediction period covers three time periods: the next 1 hour, 3 hours, and 6 hours.

[0018] Furthermore, it also includes a battery health status assessment unit. This assessment unit combines battery cycle count, charge / discharge rate, historical temperature data, and individual cell voltage consistency to construct a battery degradation model, calculate the battery health status SOH value in real time, and analyze potential battery failure risks. When the SOH value is below 80%, it automatically issues a battery maintenance reminder, and when the individual cell voltage difference exceeds 0.3V, it initiates voltage equalization adjustment.

[0019] Furthermore, it also includes a dynamic charging power adjustment unit. This unit dynamically optimizes the charging power based on battery status, grid load, and charging pile operating status using a multi-parameter coupling algorithm. The specific expression is as follows: ,in For optimal charging power, for Influence coefficient For the battery maximum value, For the current battery value, This is the battery temperature influence coefficient. This is the current average temperature of the battery. This is the power grid load impact factor. The current load factor of the power grid. This is the maximum output power of the charging station. The maximum charging power allowed for the vehicle battery.

[0020] Furthermore, it also includes an insulation detection enhancement unit. The enhancement unit adopts a combination of AC injection method and DC detection method. It calculates the insulation resistance based on the impedance measurement method. The AC injection method injects a signal with a frequency of 1kHz and an amplitude of 5V. The DC detection method detects a voltage of 500V. When the detected value is lower than 500MΩ, the system automatically reduces the charging power to 3kW. When it is lower than 300MΩ, the charging circuit is immediately cut off, and an audible and visual alarm is issued. The system also stores the continuous change data of the insulation resistance.

[0021] Furthermore, it also includes a data encryption enhancement unit. The enhancement unit adopts a triple protection mechanism of "transmission encryption + storage encryption + blockchain notarization" for user privacy data, charging transaction data and key equipment operation data. Transmission encryption adopts the TLS1.3 protocol, storage encryption adopts the AES-256-GCM algorithm, and blockchain notarization adopts a consortium blockchain architecture. Each data block contains a data hash value, timestamp, and operation node information.

[0022] Furthermore, it also includes a voice interaction optimization unit. The optimization unit adopts an end-to-cloud collaborative voice recognition solution. The local end completes basic command recognition and noise reduction processing through an adaptive noise suppression algorithm, while the cloud is responsible for complex semantic understanding and command parsing. It supports the recognition of 200+ charging-related voice commands, and the voice wake-up word can be customized.

[0023] Furthermore, it also includes a protocol conversion and adaptation unit, which has more than 15 mainstream communication protocols between new energy vehicles and charging piles built in. It can automatically identify and dynamically convert protocols by parsing the protocol identifier through handshake signals, and supports remote updates of protocol versions via the cloud without the need to disassemble the equipment hardware on site.

[0024] Furthermore, it also includes a peak-valley electricity price adaptation unit. The adaptation unit synchronizes the peak-valley and flat electricity price periods and price standards released by the power grid every 24 hours. It updates in real time when the power grid makes temporary adjustments. Combining the user's charging demand forecast results and battery health status, it dynamically adjusts the charging strategy. During peak hours, it adopts slow charging mode or suspends charging. During flat hours, it adopts conventional power charging. During off-peak hours, it adopts fast charging mode or increases charging power. It also supports users to customize the electricity price sensitivity level.

[0025] Furthermore, it also includes a fault self-repair unit. The self-repair unit has 12 preset fault repair schemes for common charging pile faults. After a fault occurs, it automatically starts the diagnostic process to locate the fault type and cause, and starts the corresponding repair scheme according to the fault level. The repair time for minor faults is ≤30 seconds, and the repair time for moderate faults is ≤5 minutes. After successful repair, charging is automatically restored. If the repair fails, the faulty module is locked and a maintenance reminder is issued, and the fault repair process data is recorded.

[0026] Compared with existing technologies, the beneficial effects of this invention are:

[0027] In terms of user experience, the multi-mode authentication mechanism covers different usage scenarios, the authentication process is fast and convenient, and the accurate charging demand prediction combined with driving habits and real-time road conditions ensures that the charging amount and charging time are perfectly matched with actual needs, avoiding resource waste and travel inconvenience; the end-to-cloud collaborative voice interaction and multi-channel control methods are adapted to complex environments and diverse operating habits, significantly improving the ease of operation.

[0028] In terms of grid and equipment coordination, the combination of dynamic grid load sensing and charging strategy optimization enables charging behavior to dynamically adapt to the grid load curve, effectively reducing the impact of concentrated charging on the grid and ensuring stable grid operation. The charging pile's full-dimensional status monitoring covers basic parameters and key operating indicators, promptly detecting equipment anomalies. Combined with a fault self-repair unit, this significantly improves equipment operational stability, reduces downtime, and lowers maintenance costs. Regarding battery health management, the multi-dimensional SOH assessment model comprehensively considers factors such as cycle count, charge / discharge rate, and temperature, resulting in more accurate assessments. Voltage balancing and degradation suppression strategies slow down battery aging, extend battery life, and reduce user costs.

[0029] In terms of safety and data security, the combination of multiple detection methods for insulation testing and all-scenario safety protection enhances the comprehensiveness and response speed of safety protection. Fault diagnosis and cause analysis capabilities provide precise support for operation and maintenance. Triple data encryption and blockchain evidence storage mechanisms achieve full lifecycle data security and traceability, eliminating the risk of data leakage and tampering. Enhanced protocol compatibility breaks down compatibility barriers between different vehicle brands and charging piles, achieving seamless integration and broadening the system's applicability. Overall, this invention, through multi-dimensional technological improvements, achieves efficient, safe, intelligent, and compatible charging management, providing reliable technical support for the large-scale development of new energy vehicle charging infrastructure, and possesses significant practical value and promotional significance. Attached Figure Description

[0030] Figure 1 This is a schematic block diagram of an intelligent charging management system for new energy vehicles proposed in this invention;

[0031] Figure 2 A line graph showing the change in charging power as a function of battery SOC;

[0032] Figure 3 A bar chart comparing the core performance indicators of the traditional system and the system of this invention;

[0033] Figure 4 A bar chart comparing repair times for different fault types. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0036] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0037] Reference Figures 1 to 4 A smart charging management system for new energy vehicles includes the following modules:

[0038] The user identification and authentication module supports three authentication methods: license plate recognition, APP binding verification, and NFC card swiping. The license plate recognition adopts the YOLOv5 deep learning architecture with an accuracy rate of ≥99.5%. The APP binding supports Bluetooth 5.0 and 5G dual-mode communication. The account is associated with charging history, payment methods, and vehicle parameters. The NFC card swiping response time is ≤0.3 seconds. The authentication process is encrypted throughout the entire transmission.

[0039] The charging demand prediction module collects the vehicle battery's current SOC value, remaining driving range, historical charging curves, driving habit characteristics, and real-time road condition information. It uses an LSTM neural network and a weighted fusion algorithm of random forest (with weights of 0.6 and 0.4, respectively) to predict the user's required charging amount, target charging time, and optimal charging power range. The prediction error is ≤3%, and the data update frequency is 1 minute / time.

[0040] The charging pile status monitoring module collects real-time data on charging pile output power, charging interface temperature, output voltage, output current, heat dissipation system operating parameters (fan speed, heat sink temperature), and interface connection tightness. It uses CAN bus and ModbusTCP protocol for transmission, with a data acquisition frequency of 10Hz, temperature measurement accuracy of ±0.5℃, voltage measurement accuracy of ±0.1V, and current measurement accuracy of ±0.05A.

[0041] The power grid load sensing module collects real-time load rate, voltage fluctuation value, frequency deviation and peak-valley electricity price information of the power grid through power line carrier communication (PLC). The load rate measurement range is 0-100% with an accuracy of ±1%, the voltage fluctuation monitoring range is 220V±10%, the frequency deviation monitoring range is 50Hz±0.5Hz, and the peak-valley time period is automatically synchronized with the power grid release standard, with an update frequency of 24 hours / time.

[0042] The charging strategy optimization module combines charging demand forecasts, charging pile status data, and grid load information to dynamically adjust the charging mode (fast charging / slow charging / segmented charging), charging power, and charging start / stop time. The maximum output power of the fast charging mode is 180kW, the output power of the slow charging mode is 3.3-7kW, and the segmented charging mode dynamically switches the power according to the SOC range (0-30% / 30-80% / 80-100%).

[0043] The battery status monitoring module collects real-time data on individual battery pack voltages, total voltage, SOC value, SOH value, internal battery pack temperature, and voltage balance. The SOC measurement accuracy is ±1%, the SOH assessment error is ≤2%, the temperature acquisition points cover the positive and negative electrodes, the middle and edge areas, the sampling frequency is 5Hz, and the voltage balancing current is ≤5A.

[0044] The safety protection and fault diagnosis module has six protection functions: overcurrent, overtemperature, overvoltage, undervoltage, short circuit, and insulation detection. The overcurrent protection threshold is 1.2 times the rated current, the overtemperature protection threshold is 60℃, the insulation resistance detection threshold is ≥500MΩ, the short circuit protection response time is ≤10ms, and the fault diagnosis adopts the fault tree analysis method with a fault identification accuracy of ≥98%, generating fault codes and cause identifiers in real time.

[0045] The data storage and management module uses a MongoDB distributed database to store user information, charging data, equipment operation data and fault records, with a storage capacity of ≥10TB. It uses AES-256 encryption algorithm to protect data transmission and storage, integrates consortium blockchain evidence storage function (nodes include operators, users and regulatory agencies), and supports multi-dimensional retrieval by user, time and charging pile number.

[0046] The human-computer interaction module is equipped with a 10.1-inch touch screen (1920×1200 resolution), supports voice interaction and remote APP control, touch response time ≤0.2 seconds, voice recognition supports Mandarin Chinese and 6 dialects including Cantonese and Sichuanese, recognition accuracy ≥95%, and displays charging power, duration, charged amount, remaining time and account balance in real time.

[0047] The communication coordination module is responsible for data interaction between modules and communication between the system and new energy vehicles, power grid dispatch center, and cloud management platform. It supports four communication methods: 5G, Bluetooth 5.0, WiFi 6, and power line carrier. The communication latency is ≤50ms. It has protocol conversion function and is compatible with mainstream protocols such as GB / T27930 and ISO15118.

[0048] This invention also includes a dynamic charging load prediction unit. The prediction unit constructs a multi-dimensional load prediction model based on historical grid load data, regional charging demand distribution, meteorological data (temperature, precipitation, wind speed), and holiday factors. The prediction accuracy is improved by dynamically adjusting the model weight coefficients. The prediction period covers three time periods: 1 hour, 3 hours, and 6 hours. The short-term prediction (1 hour) error is ≤5%, the medium-term prediction (3 hours) error is ≤8%, and the long-term prediction (6 hours) error is ≤10%. This provides grid load prediction data for charging strategy optimization, enabling charging behavior to dynamically adapt to the grid load curve.

[0049] This invention also includes a battery health status assessment unit. The assessment unit combines the number of battery cycles (cumulative for complete charge-discharge cycles, with partial charge-discharge cycles calculated proportionally), charge-discharge rate, historical temperature data, and individual cell voltage consistency to construct a battery degradation model, calculate the battery health status (SOH) value in real time, and analyze potential battery failure risks. When the SOH value is below 80%, a battery maintenance reminder is automatically issued. When the individual cell voltage difference exceeds 0.3V, voltage equalization adjustment is initiated to slow down the battery degradation process and reduce the frequency of fault triggering.

[0050] This invention also includes a dynamic charging power adjustment unit. This unit dynamically optimizes the charging power based on battery status, grid load, and charging pile operating status using a multi-parameter coupling algorithm. The specific expression is as follows: ,in Optimal charging power, in kW. for The influence coefficient ranges from 1.2 to 1.5. For the battery maximum Value, taking the value 100%. For the current battery Value, ranging from 0 to 100%. This is the battery temperature influence coefficient, with a value ranging from 0.02 to 0.05℃⁻¹. The current average temperature of the battery, in °C. This is the power grid load impact coefficient, with a value ranging from 0.8 to 1.0. This represents the current load factor of the power grid, with a value between 0 and 1. This refers to the maximum output power of the charging station, in kW. The maximum allowable charging power for the vehicle battery, in kW, is calculated using multi-parameter coupling to slow down battery thermal degradation and reduce grid load fluctuations while maintaining charging efficiency.

[0051] This invention also includes an insulation detection enhancement unit. The enhancement unit adopts a combination of AC injection method and DC detection method. It calculates the insulation resistance based on impedance measurement method. The AC injection method injects a signal frequency of 1kHz and an amplitude of 5V, while the DC detection method detects a voltage of 500V. The insulation resistance detection resolution reaches 1MΩ. When the detected value is lower than 500MΩ, the system automatically reduces the charging power to 3kW. When it is lower than 300MΩ, the charging circuit is immediately cut off, and an audible and visual alarm is issued. The system stores the continuous change data of insulation resistance to provide a basis for subsequent fault tracing.

[0052] This invention also includes a data encryption enhancement unit. The enhancement unit adopts a triple protection mechanism of "transmission encryption + storage encryption + blockchain notarization" for user privacy data, charging transaction data, and key equipment operation data. Transmission encryption adopts the TLS1.3 protocol (encryption suite ECDHE-RSA-AES256-GCM-SHA384), storage encryption adopts the AES-256-GCM algorithm, and blockchain notarization adopts a consortium blockchain architecture. Each data block contains a data hash value, timestamp, and operation node information. The block generation interval is 10 minutes, and the data tampering detection response time is ≤1 second, realizing full life-cycle security protection and full traceability of data.

[0053] This invention also includes a voice interaction optimization unit. The optimization unit adopts an end-to-cloud collaborative voice recognition scheme. The local end completes basic command recognition and noise reduction processing through an adaptive noise suppression algorithm, while the cloud is responsible for complex semantic understanding and command parsing. It supports the recognition of 200+ charging-related voice commands (including charging start, pause, power adjustment, fee inquiry, etc.), and the voice wake-up word can be customized. The wake-up distance is ≤5 meters, the wake-up success rate is ≥98%, the command recognition accuracy is ≥95% when the ambient noise is in the range of 40-60dB, and the command recognition accuracy is ≥90% when the ambient noise is in the range of 60-80dB.

[0054] This invention also includes a protocol conversion and adaptation unit. The adaptation unit has more than 15 built-in communication protocols between new energy vehicles and charging piles, including GB / T27930, ISO15118, CHAdeMO and CCS. It can automatically identify and dynamically convert protocols by parsing the protocol identifier through handshake signals. The identification time is ≤1 second and the conversion delay is ≤10ms. It can achieve seamless connection between new energy vehicles of different brands and models and charging piles. It supports remote update of protocol versions through the cloud without the need for on-site disassembly of equipment hardware.

[0055] This invention also includes a peak-valley electricity price adaptation unit. The adaptation unit synchronizes the peak-valley and flat electricity price periods and price standards published by the power grid every 24 hours. It updates in real time when the power grid makes temporary adjustments. Combining the user's charging demand forecast results and battery health status, it dynamically adjusts the charging strategy. During peak hours, it adopts a slow charging mode or suspends charging. During flat hours, it adopts a regular power charging mode. During off-peak hours, it adopts a fast charging mode or increases the charging power. At the same time, it supports users to customize the electricity price sensitivity level (high / medium / low). Under the high sensitivity level, it concentrates charging during the lowest electricity price period. Under the medium sensitivity level, it balances charging speed and electricity price cost control. Under the low sensitivity level, it prioritizes charging speed.

[0056] This invention also includes a self-repair unit. This self-repair unit is designed for common charging pile faults (minor heat dissipation system faults manifested as fan speed below 80% of rated value, brief communication link interruptions manifested as three consecutive communication timeouts, and poor interface contact manifested as current fluctuations ≥5A). It pre-sets 12 fault repair schemes. Upon fault occurrence, it automatically initiates a diagnostic process to locate the fault type and cause, and activates the corresponding repair scheme based on the fault level. Minor fault repair time is ≤30 seconds with a success rate ≥95%, while moderate fault repair time is ≤5 minutes with a success rate ≥75%. Upon successful repair, charging is automatically resumed; if repair fails, the faulty module is locked, a maintenance reminder is issued, and fault repair process data is recorded to support equipment operation and maintenance.

[0057] The following two examples further illustrate specific embodiments of the present invention:

[0058] Example 1: Implementation of Intelligent Charging Management System for New Energy Vehicles in Public and Commercial Charging Stations

[0059] This embodiment is applied to a public commercial charging station in a core urban area. The station deploys 20 dual-gun charging piles, supporting fast and slow charging modes. It serves more than 15 mainstream vehicle types, including pure electric sedans, SUVs, and commercial vehicles. Peak charging times are 7:00-9:00 and 17:00-19:00 daily. The system of this invention achieves intelligent management of the entire charging process. The specific implementation process is as follows:

[0060] 1. System module deployment and parameter configuration

[0061] The user identification and authentication module deploys a license plate recognition camera and an NFC card reader next to each charging station. The app binding function supports mainstream smart terminals. License plate recognition uses the YOLOv5 deep learning architecture, extracting and matching features from images of the vehicle's front. The recognition process includes image preprocessing, target detection, and feature comparison, with a total time of ≤0.5 seconds. App binding is via Bluetooth 5.0 near-field pairing or 5G remote connection. During user registration, users need to enter their vehicle VIN code, battery parameters, and payment information. The NFC card reader has a built-in induction coil, with a card-swiping distance of ≤5 cm and a response time of 0.2 seconds. After successful authentication, the system automatically associates the user's charging account balance, historical charging records, vehicle battery capacity, charging interface type, and other parameters.

[0062] The charging demand prediction module communicates with the charging pile via the vehicle's OBD interface, collecting the vehicle's current SOC value and remaining driving range. This data is combined with historical charging curves from the past 30 days stored in the cloud, including initial SOC, charging duration, charging power changes, and driving habit characteristics such as average daily mileage and road condition coverage. Real-time road condition information, including congestion level and estimated travel time, is also obtained through a map interface. A weighted fusion algorithm of LSTM neural network and random forest is employed. The LSTM network captures time-series features, i.e., historical charging trends, while the random forest mines multi-dimensional feature associations, i.e., the mapping relationship between road conditions and charging demand. The weights are set to 0.6 and 0.4 respectively, with a data update frequency of 1 minute. This predicts the user's required charging amount, target full charge time, and optimal charging power range.

[0063] The charging pile status monitoring module collects data on the output power, charging interface temperature, output voltage, and output current of each charging pile through sensors. Fan speed sensors and heat sink temperature sensors are deployed inside the charging pile, and interface connection tightness is detected by pressure sensors. Data transmission uses a CAN bus and Modbus TCP protocol. The CAN bus handles data exchange between modules within the charging pile, while the Modbus TCP protocol handles communication with the cloud management platform. The data acquisition frequency is 10Hz, the temperature measurement range is -20℃ to 80℃, the voltage measurement range is 200V to 750V, and the current measurement range is 0A to 500A.

[0064] The power grid load sensing module connects to the regional power grid through power line carrier communication equipment, collecting real-time load factor, voltage fluctuation value, and frequency deviation data. Peak and valley electricity prices are synchronized with the standards published by the local power grid. Peak hours are 8:00-12:00 and 14:00-17:00, flat hours are 12:00-14:00 and 17:00-22:00, and valley hours are from 22:00 to 8:00 the next day. The update frequency is 24 hours / time. The system receives update instructions in real time when the power grid temporarily adjusts its time period.

[0065] The charging strategy optimization module is set to a maximum output power of 180kW in fast charging mode and 3.3-7kW in slow charging mode. The segmented charging mode dynamically switches according to the SOC range: 80% rated power for SOC 0-30%, 100% rated power for SOC 30-80%, and 30% rated power for SOC 80-100%. The battery status monitoring module incorporates voltage and current sensors in the charging gun of the charging pile. It communicates with the vehicle's battery management system via the charging interface, collecting data on the voltage of 96 individual battery cells, the total voltage, SOC value, and SOH value. Six temperature sampling points are arranged inside the battery pack: one each for the positive and negative terminals, two in the middle, and two at the edge. The sampling frequency is 5Hz, and the voltage balancing current is set to 3A.

[0066] The safety protection and fault diagnosis module is equipped with six major protection circuits: overcurrent, overtemperature, overvoltage, undervoltage, short circuit, and insulation detection. The rated current in fast charging mode is 250A, the overcurrent protection threshold is set to 1.2 times the rated current, i.e., 300A, the overtemperature protection threshold is 60℃, the insulation resistance detection threshold is 500MΩ, and the short circuit protection response time is 8ms. The fault diagnosis adopts the fault tree analysis method, which classifies the fault types into 8 major categories and 32 subcategories, such as power supply faults, communication faults, and interface faults. Each type of fault corresponds to a unique fault code and cause identifier.

[0067] The data storage and management module uses the MongoDB distributed database, deploying 3 data nodes and 1 backup node, with a storage capacity of 15TB. The AES-256 encryption algorithm is used for data transmission and storage. The consortium blockchain evidence storage nodes include charging station operators, power grid companies, and regulatory agencies. Each data block contains a data hash value, timestamp, and operation node information, with a block generation interval of 10 minutes.

[0068] The human-computer interaction module is equipped with a 10.1-inch touchscreen display with a resolution of 1920×1200. Voice interaction supports Mandarin Chinese and six dialects including Cantonese and Sichuanese. It has over 200 preset charging-related voice commands, including starting fast charging, pausing charging, and checking charging costs. The remote APP supports charging reservation, status viewing, and payment functions. The communication coordination module supports four communication methods: 5G, Bluetooth 5.0, WiFi 6, and power line carrier. It is compatible with protocols such as GB / T27930 and ISO15118 for communication with new energy vehicles, and uses power line carrier communication with the power grid dispatch center, with communication latency controlled within 40ms.

[0069] 2. System Operation Flow

[0070] After a user drives their vehicle into the charging station, the license plate recognition camera automatically captures the vehicle image, completes recognition within 0.3 seconds, and matches it with the system database. If the user is already registered, authentication is completed directly. Unregistered users can complete authentication via the app or by NFC card swiping. The entire authentication process is encrypted. After successful authentication, the charging demand prediction module collects the vehicle's current battery SOC value (35%), remaining range (120 km), historical charging curves (average charging to 85% SOC over the past 30 days), driving habits (average daily mileage of 80 km), and current traffic conditions (moderate congestion level). It estimates a 20% increase in driving time and uses an LSTM and random forest fusion algorithm to predict the required charging amount (50 kWh), target charging time (1 hour), and optimal charging power range (80-120 kW).

[0071] The charging pile status monitoring module collects real-time data on the charging pile's output voltage (380V), current (150A), interface temperature (35℃), cooling system fan speed (2000 rpm), and interface connection tightness pressure (0.3MPa). This data is transmitted to the charging strategy optimization module via the CAN bus. The grid load sensing module collects data on the current grid load rate (65%), voltage fluctuation (220V±3%), and frequency deviation (50Hz±0.1Hz), indicating that the current period is a flat electricity price period.

[0072] The charging power dynamic adjustment unit initiates a multi-parameter coupling algorithm, using the formula... Calculate the optimal charging power. Take 1.5, =100%, =35%, Take 0.02℃⁻¹, =35℃, Take 0.9, =0.65, =180kW, =150kW, substituting into the formula, we get: The system, combined with the segmented charging mode settings, determined the charging power to be 100kW, which is within the optimal power range.

[0073] The battery status monitoring module collects the individual cell voltages of the battery pack in real time. Upon detecting a voltage difference of 0.35V between two cells, it initiates voltage equalization adjustment with a 3A equalization current. After 10 minutes, the individual cell voltage difference drops to 0.1V. The safety protection module monitors the charging circuit in real time. The insulation detection enhancement unit combines AC injection and DC detection methods, injecting a 1kHz, 5V AC signal and applying a 500V DC detection voltage. The insulation resistance is measured at 600MΩ, meeting the safety threshold. The data encryption enhancement unit transmits user payment information using the TLS 1.3 protocol and stores it using the AES-256-GCM algorithm. The blockchain notarization node synchronously records the charging transaction data.

[0074] During the charging process, the fault self-repair unit detected that the fan speed of a charging pile was 80% lower than the rated value (rated speed 2500 rpm, actual speed 2000 rpm). It determined that this was a minor fault in the heat dissipation system and initiated a repair plan: controlling the fan speed to increase to 3000 rpm, while reducing the charging power by 10%. After 30 seconds, the fan speed returned to normal, and the system automatically restored the original charging power.

[0075] 3. Effectiveness verification data

[0076] Table 1 compares the performance of traditional charging systems in public charging stations with the system of this invention.

[0077] Performance index Traditional charging system Inventive system Charging demand prediction error 12% 2.8% Power grid load fluctuation amplitude 15% 4.2% Battery attenuation rate 1.2 times of conventional rate 0.7 times of conventional rate Fault handling time length 40 minutes 3 minutes Vehicle model compatibility rate 75% 98.5%

[0078] Table 1 shows that traditional charging systems rely solely on State of Charge (SOC) values ​​for demand prediction, resulting in an error of 12%. During concentrated charging, grid load fluctuations reach 15%, impacting grid stability. Battery degradation rates are rapid, and fault handling relies on manual maintenance, taking up to 40 minutes, with a vehicle compatibility rate of only 75%. The system of this invention, through a demand prediction algorithm that integrates multi-dimensional data, reduces the error to 2.8%. Combined with a dynamic grid load adjustment charging strategy, load fluctuations are controlled at 4.2%. A multi-parameter coupled battery health management strategy reduces the degradation rate to 0.7 times the conventional rate, the fault self-repair unit shortens processing time to 3 minutes, and the protocol conversion and adaptation unit achieves 98.5% vehicle compatibility. These results demonstrate the significant advantages of this invention in accurate prediction, grid coordination, battery protection, fault handling, and compatibility, fully meeting the efficient, stable, and compatible operational requirements of public and commercial charging stations.

[0079] Example 2: Implementation of a Smart Charging Management System for New Energy Vehicles in a Residential Community

[0080] This embodiment is applied to a large residential community, which deploys 10 wall-mounted charging piles, mainly serving residents' private cars. The peak charging period is from 21:00 to 7:00 the next day, with users primarily demanding slow charging at night and charging during off-peak hours. The system of this invention achieves coordinated optimization of charging with residential electricity consumption and grid load. The specific implementation process is as follows:

[0081] 1. System module deployment and parameter configuration

[0082] The user identification and authentication module deploys centralized license plate recognition equipment at the entrance of the community parking lot. Each charging pile is equipped with an NFC card reader. The APP binding function supports a dedicated registration channel for community residents. The license plate recognition camera uses a high-definition night vision lens with a night recognition accuracy of ≥99.3%. The APP binding is connected via Bluetooth 5.0 and home WiFi. The NFC card reader is integrated into the charging pile control panel with a response time of 0.25 seconds. After successful authentication, it automatically associates the resident's house number, charging account, and vehicle battery parameters.

[0083] The charging demand prediction module collects the current SOC value and remaining driving range of the battery through the vehicle's OBD interface. It combines this data with nearly 30 days of historical charging data stored in the cloud, focusing on nighttime off-peak charging, averaging 90% SOC, driving habits of an average daily mileage of 50 kilometers, primarily commuting, and information such as next-day traffic restrictions obtained from the community property management system. A weighted fusion algorithm of LSTM and Random Forest is used, with LSTM weights of 0.6 and Random Forest weights of 0.4, and data updates every minute to predict the user's required charging amount, target full charge time, and optimal charging power range.

[0084] The charging pile status monitoring module collects the charging pile's output power, interface temperature, voltage, and current. A fan speed sensor is deployed inside the charging pile, and the interface connection tightness is detected by a pressure sensor. Data transmission uses CAN bus and Modbus TCP protocol, with a sampling frequency of 10Hz, temperature measurement accuracy of ±0.5℃, voltage measurement accuracy of ±0.1V, and current measurement accuracy of ±0.05A.

[0085] The power grid load sensing module accesses the community's power distribution network via power line carrier communication, collecting real-time load rate, voltage fluctuation value, and frequency deviation of the community's power grid. Peak and valley electricity prices are synchronized with local power grid standards during peak hours (8:00-12:00 and 17:00-21:00), flat hours (12:00-17:00), and valley hours (21:00 to 8:00 the next day). The update frequency is 24 hours / time, and it is updated in real time when the power grid is temporarily adjusted.

[0086] The charging strategy optimization module is set to a maximum output power of 180kW in fast charging mode and 3.3-7kW in slow charging mode. The segmented charging mode switches according to SOC range: 70% rated power for 0-30%, 100% rated power for 30-80%, and 25% rated power for 80-100%. The battery status monitoring module collects data on individual battery pack cell voltage, total voltage, SOC value, SOH value, and temperature at 6 points, with a sampling frequency of 5Hz and a voltage balancing current of 2A.

[0087] The safety protection and fault diagnosis module is equipped with six major protection functions: a rated current of 16A in slow charging mode, an overcurrent protection threshold of 1.2 times the rated current (19.2A), an overtemperature protection threshold of 60℃, an insulation resistance detection threshold of 500MΩ, a short circuit protection response time of 9ms, and fault diagnosis using fault tree analysis, covering 8 major categories and 28 subcategories of faults.

[0088] The data storage and management module uses the MongoDB distributed database with a storage capacity of 10TB. Data is protected by AES-256 encryption algorithm. The consortium blockchain evidence storage nodes include community property management, power grid company, and homeowner representatives. Block generation interval is 10 minutes.

[0089] The human-machine interface module is equipped with a 10.1-inch touchscreen display with a resolution of 1920×1200. Voice interaction supports Mandarin Chinese and three regional dialects, and it has over 150 preset charging-related commands. The remote app supports charging reservation, timed start / stop, and fee inquiry functions. The communication coordination module supports 5G, Bluetooth 5.0, WiFi 6, and power line carrier communication, and is compatible with mainstream communication protocols in the vehicle, with a communication latency of ≤45ms.

[0090] 2. System Operation Flow

[0091] When a user drives their vehicle into the community parking lot, the license plate recognition device at the entrance completes the recognition and authentication within 0.4 seconds. The user then issues a nighttime charging reservation command via the app, setting the target full charge time to 6:00 AM the following day. The charging demand prediction module collects data on the vehicle's current battery SOC value (40%), remaining range (100 km), and historical charging data (charged to 90% SOC during off-peak hours over the past 30 days, average daily driving distance of 50 km, and commuting conditions), predicting a required charging amount of 50 kWh and an optimal charging power range of 5-7 kW.

[0092] The power grid load sensing module collects data showing that the current grid load rate in the community is 55%, indicating a flat electricity price period. The predicted grid load rate for off-peak periods is 40%. The peak-valley electricity price adaptation unit automatically adjusts the charging strategy, setting charging to start at 21:00 during the off-peak period, using a slow charging mode with a power of 7kW.

[0093] The charging power dynamic adjustment unit uses the formula Calculate the optimal power. Take 1.8, =100%, =40%, Take 0.015℃⁻¹, =30℃, Take 0.85, =0.4, =7kW, =7kW, substituting into the formula, we get: The system combines peak-valley electricity pricing strategies with user reservation needs to determine the charging power during off-peak hours to be 7kW, ensuring that the battery is fully charged on time the next day.

[0094] The battery status monitoring module collects real-time data on the individual battery cell voltages, with a state of equilibrium (SOH) of 92% and a temperature of 32°C, showing no voltage imbalance. The safety protection module's insulation test shows an insulation resistance of 700MΩ, meeting requirements. The data encryption enhancement unit performs triple encryption on user privacy data and charging transaction data, with simultaneous recording by blockchain-based evidence nodes.

[0095] The voice interaction optimization unit allows users to adjust the charging power to 5kW and check the remaining charging time via voice commands. The command recognition accuracy is 96% at an ambient noise level of 55dB. The fault self-repair unit detects three consecutive timeouts in the communication link of a charging pile and initiates a repair plan: restarting the communication module, re-establishing the connection, and restoring normal communication after 20 seconds, ensuring uninterrupted charging.

[0096] 3. Effectiveness verification data

[0097] Table 2 is a performance comparison table between the traditional charging system in the community and the system of the present invention:

[0098] Performance index Traditional charging system Inventive system Valley section charging utilization rate 65% 92% User charging waiting time length 25 minutes 3 minutes Charging cost saving ratio 10% 35% Voice interaction accuracy rate 70% 95% Equipment failure rate 8% 2.5%

[0099] Table 2 shows that traditional community charging systems have a peak-valley charging utilization rate of only 65%, a user waiting time of 25 minutes, a charging cost saving of only 10%, a voice interaction accuracy of only 70% in noisy environments, and an equipment failure rate of 8%. The system of this invention, through a peak-valley electricity price adaptation unit and a charging reservation function, increases the peak-valley charging utilization rate to 92% and reduces the user waiting time to 3 minutes. Combined with peak-valley fast charging and power optimization, the charging cost saving reaches 35%. The end-to-cloud collaborative voice interaction solution achieves 95% accuracy in noisy community environments. A full-dimensional status monitoring and fault self-repair unit reduces the equipment failure rate to 2.5%. These results verify the adaptability and advantages of this invention in residential charging scenarios. Through precise strategy optimization, convenient interaction methods, and reliable operation guarantees, it meets the core needs of community residents for nighttime charging, cost savings, and convenient operation, while reducing equipment maintenance costs, achieving a win-win situation for residents, property management, and the power grid.

[0100] Reference Figure 2This diagram visually illustrates the power regulation logic of the segmented charging mode of this invention. During the SOC range of 30-80%, it employs a maximum power of 180kW for fast charging, balancing charging efficiency. After 80%, the power is gradually reduced to avoid overcharging and battery damage. Traditional systems often use constant power charging, which can easily lead to increased battery heating and accelerated battery degradation during high SOC stages. The dynamic power adjustment strategy of this invention not only meets the requirements of fast charging but also protects the battery through refined power control. The step-like power change in the diagram reflects the system's deep adaptation to battery characteristics. Combined with real-time adjustments based on battery status monitoring data, it achieves a balance between charging efficiency and battery life, serving as a core visual representation of the battery health management function.

[0101] Reference Figure 3 This bar chart quantifies and compares the technical advantages of this invention: the charging demand prediction error has been reduced from 12% to 3%, thanks to a multi-dimensional data fusion algorithm; the grid load fluctuation has been reduced by 10 percentage points, reflecting the synergistic optimization of grid load perception and charging strategy; the battery degradation rate has been slowed by 50%, thanks to precise power control and health management; fault handling time has been reduced by 92.5%, relying on the fault self-repair unit; and vehicle compatibility has been improved by 23 percentage points, relying on protocol conversion and adaptation functions. The data intuitively demonstrates the breakthroughs of this invention in accuracy, stability, and compatibility, providing data support for intelligent charging management.

[0102] Reference Figure 4 The bar chart illustrates the efficiency of the self-repair unit of this invention. Minor faults such as those related to the heat dissipation system, communication links, and poor interface contact are repaired within 1 minute, while software lag takes 2 minutes. Only power supply faults require manual intervention. In contrast, traditional systems require manual handling for all faults, with an average duration exceeding 40 minutes. This invention features 12 preset fault repair schemes, automatically repairing minor faults, significantly reducing downtime and lowering maintenance costs. The significant difference in time shown in the chart demonstrates the practicality of the self-repair function, especially the rapid handling of high-frequency minor faults, which improves equipment operational stability and ensures the continuous service capability of charging stations.

[0103] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart charging management system for new energy vehicles, characterized in that, Includes the following modules: The user identification and authentication module supports three authentication methods: license plate recognition, APP binding verification, and NFC card swiping. The account is associated with charging history, payment method and vehicle parameters. The authentication process is encrypted throughout the entire transmission. The charging demand prediction module collects vehicle battery parameters, driving habit characteristics and real-time road condition information, and uses an LSTM neural network and random forest weighted fusion algorithm to predict the user's required charging amount, target full charge time and optimal charging power range. The charging pile status monitoring module collects data in real time on the charging pile's output power, charging interface temperature, output voltage, output current, heat dissipation system operating parameters, and interface connection tightness, and transmits the data using CAN bus and ModbusTCP protocol. The power grid load sensing module collects real-time power grid load rate, voltage fluctuation value, frequency deviation and peak-valley electricity price information through power line carrier communication, and automatically synchronizes with the power grid release standard during peak-valley periods. The charging strategy optimization module combines charging demand forecast results, charging pile status data and grid load information to dynamically adjust the charging mode, charging power and charging start and stop time. The segmented charging mode dynamically switches the power according to the SOC range. The battery status monitoring module collects real-time data on individual battery cell voltage, total voltage, SOC value, SOH value, internal temperature of the battery pack, and voltage balance. Temperature collection points cover the positive and negative electrodes, the middle and edge areas. The safety protection and fault diagnosis module has six protection functions: overcurrent, overtemperature, overvoltage, undervoltage, short circuit, and insulation detection. The fault diagnosis adopts the fault tree analysis method to generate fault codes and cause identifiers in real time. The data storage and management module uses the MongoDB distributed database to store user information, charging data, equipment operation data and fault records, and uses the AES-256 encryption algorithm to protect data transmission and storage. The human-computer interaction module is equipped with a touch screen, supports voice interaction and remote APP control, and displays charging power, duration, charged amount, remaining time and account balance in real time. The communication coordination module is responsible for data interaction between modules and communication between the system and new energy vehicles, power grid dispatch center, and cloud management platform, and has protocol conversion function.

2. The intelligent charging management system for new energy vehicles according to claim 1, characterized in that, It also includes a charging load dynamic prediction unit. The prediction unit constructs a load prediction model based on historical power grid load data, regional charging demand distribution, meteorological data and holiday factors. It improves prediction accuracy by dynamically adjusting the model weight coefficients. The prediction period covers three time periods: the next 1 hour, 3 hours and 6 hours.

3. The intelligent charging management system for new energy vehicles according to claim 1, characterized in that, It also includes a battery health status assessment unit. The assessment unit combines the number of battery cycles, charge and discharge rates, historical temperature data and individual cell voltage consistency to construct a battery degradation model, calculate the battery health status SOH value in real time, and analyze potential battery failure risks. When the SOH value is lower than 80%, it automatically issues a battery maintenance reminder, and when the individual cell voltage difference exceeds 0.3V, it initiates voltage equalization adjustment.

4. The intelligent charging management system for new energy vehicles according to claim 1, characterized in that, It also includes a dynamic charging power adjustment unit, which dynamically optimizes the charging power based on battery status, grid load, and charging pile operating status using a multi-parameter coupling algorithm. The specific expression is as follows: ,in For optimal charging power, for Influence coefficient For the battery maximum value, For the current battery value, This is the battery temperature influence coefficient. This is the current average temperature of the battery. This is the power grid load impact factor. The current load factor of the power grid. This is the maximum output power of the charging station. The maximum charging power allowed for the vehicle battery.

5. The intelligent charging management system for new energy vehicles according to claim 1, characterized in that, It also includes an insulation detection enhancement unit, which adopts a combination of AC injection method and DC detection method. The insulation resistance is calculated based on the impedance measurement method. The AC injection method injects a signal with a frequency of 1kHz and an amplitude of 5V, and the DC detection method detects a voltage of 500V. When the detected value is lower than 500MΩ, the system automatically reduces the charging power to 3kW. When it is lower than 300MΩ, the charging circuit is immediately cut off, and an audible and visual alarm is issued. The system also stores the continuous change data of the insulation resistance.

6. The intelligent charging management system for new energy vehicles according to claim 1, characterized in that, It also includes a data encryption enhancement unit, which adopts a triple protection mechanism of "transmission encryption + storage encryption + blockchain notarization" for user privacy data, charging transaction data and key equipment operation data. Transmission encryption adopts the TLS1.3 protocol, storage encryption adopts the AES-256-GCM algorithm, and blockchain notarization adopts a consortium blockchain architecture. Each data block contains a data hash value, timestamp, and operation node information.

7. The intelligent charging management system for new energy vehicles according to claim 1, characterized in that, It also includes a voice interaction optimization unit, which adopts an end-to-cloud collaborative voice recognition solution. The local end completes basic command recognition and noise reduction processing through an adaptive noise suppression algorithm, while the cloud is responsible for complex semantic understanding and command parsing. It supports the recognition of 200+ charging-related voice commands, and the voice wake-up word can be customized.

8. The intelligent charging management system for new energy vehicles according to claim 1, characterized in that, It also includes a protocol conversion and adaptation unit, which has more than 15 mainstream communication protocols between new energy vehicles and charging piles built in. It can automatically identify and dynamically convert protocols by parsing the protocol identifier through handshake signals, and supports remote updates of protocol versions via the cloud without the need to disassemble the equipment hardware on site.

9. The intelligent charging management system for new energy vehicles according to claim 1, characterized in that, It also includes a peak-valley electricity price adaptation unit, which synchronizes the peak-valley and flat electricity price periods and price standards released by the power grid every 24 hours. It updates in real time when the power grid makes temporary adjustments. It dynamically adjusts the charging strategy based on the user's charging demand forecast and battery health status. During peak hours, it adopts slow charging mode or suspends charging; during flat hours, it adopts conventional power charging; and during off-peak hours, it adopts fast charging mode or increases charging power. It also supports users to customize the electricity price sensitivity level.

10. A new energy vehicle intelligent charging management system according to claim 1, characterized in that, It also includes a fault self-repair unit, which has 12 preset fault repair schemes for common charging pile faults. After a fault occurs, the diagnostic process is automatically started to locate the fault type and cause, and the corresponding repair scheme is started according to the fault level. The repair time for minor faults is ≤30 seconds, and the repair time for moderate faults is ≤5 minutes. After successful repair, charging is automatically restored. If the repair fails, the faulty module is locked and a maintenance reminder is issued, and the fault repair process data is recorded.

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