New Energy Vehicle Battery Charging Management System

Abstract

The present invention relates to the field of battery management technology, in particular to a new energy vehicle battery charging management system, including: a charging management platform; multiple charging management nodes, which are connected to the charging management platform in communication, each charging management node corresponds to a charging pile, and the charging management node includes a master node, a slave node, and a backup node; a fault management module, which is connected to the charging management platform in communication; controls the display and alarm of fault information; remotely handles faults; a monitoring module, which is connected to the charging management platform and the charging management node in communication; sends fault information to the charging management platform when a fault is detected, and the coordinated work of the master node, the slave node, and the backup node not only disperses the system load, but also provides fault redundancy, significantly improving the reliability of the system. In terms of fault management, the present invention introduces a predictive maintenance technology based on machine learning, which can identify potential faults in advance, greatly reducing the equipment failure rate and maintenance costs.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, in particular to a new energy vehicle battery charging management system. Background Art

[0002] With the rapid development of the new energy vehicle industry, the importance of electric vehicle charging management systems has become increasingly prominent. The existing charging management system has met basic charging needs to a certain extent, but with the continuous expansion of the scale of charging stations and the increasing diversification of user needs, the limitations of traditional systems have gradually become apparent.

[0003] At present, most charging management systems adopt a centralized management architecture, and all charging piles are directly connected to the central server. This architecture performs well in small-scale applications, but often faces serious scalability and reliability issues in large charging stations. When the scale of the charging station increases, the load of the central server increases sharply, which can easily become a bottleneck of the system, causing response delays or even system crashes. In addition, the centralized architecture also has the risk of single point failure. Once there is a problem with the central server, the operation of the entire charging station will be affected.

[0004] In terms of fault management, existing systems usually adopt a passive response approach and only handle faults after they occur. This approach not only affects the user experience, but may also cause equipment damage and increase maintenance costs. At the same time, most systems lack an effective fault priority management mechanism and are unable to reasonably allocate resources according to the severity and scope of the fault, resulting in low maintenance efficiency.

[0005] In terms of charging load balancing, existing technologies often use simple first-come-first-served or polling strategies, which cannot effectively cope with complex practical scenarios. For example, during peak charging periods, this simple strategy may cause some charging piles to be overloaded while others are idle, which not only reduces the overall charging efficiency, but also may cause unnecessary pressure on the power grid.

[0006] In addition, existing systems also have deficiencies in data security and reliability. Most systems lack effective data backup and synchronization mechanisms. Once data loss or system failure occurs, it may lead to long-term service interruption. At the same time, in terms of security protection, many systems only rely on a single protection measure and cannot cope with complex security risks. Summary of the invention

[0007] In view of the above problems, the present invention proposes a new type of new energy vehicle battery charging management system. The system aims to solve the problems of poor scalability, passive fault management, low load balancing efficiency, insufficient data reliability, etc. in the prior art, and provides a new solution for the efficient management of large-scale charging stations.

[0008] The present invention proposes a new energy vehicle battery charging management system, comprising:

[0009] Charging management platform for:

[0010] Store and manage charging pile information and charging management node information;

[0011] Receive and process fault information from the monitoring module;

[0012] A plurality of charging management nodes are communicatively connected with the charging management platform, each of the charging management nodes corresponds to a charging pile, and the charging management nodes include a master node, a slave node and a standby node, wherein:

[0013] The master node is used to obtain and process the real-time charging information of nearby charging piles and control the charging sequence;

[0014] The slave node is used to obtain the real-time charging information of the corresponding charging pile and transmit the information to the master node;

[0015] The standby node is used to provide a backup function when the master node or the slave node fails;

[0016] A fault management module is connected to the charging management platform for:

[0017] Prioritize charging pile failures based on charging time;

[0018] Control the display and alarm of fault information;

[0019] Remote troubleshooting;

[0020] A monitoring module is connected to the charging management platform and the charging management node for:

[0021] Detect the operating status of each charging pile and charging management node;

[0022] When a fault is detected, fault information is sent to the charging management platform.

[0023] Preferably, the charging management node further includes:

[0024] Processor module for:

[0025] Processing real-time charging information from charging piles;

[0026] Execute charging control logic;

[0027] A communication module is electrically connected to the processor module and is used to:

[0028] Exchange data with the charging management platform;

[0029] Transmit information between different charging management nodes;

[0030] A positioning module is electrically connected to the processor module and is used to:

[0031] Obtain the geographic location information of the charging management node;

[0032] A battery management module is electrically connected to the processor module and is used to:

[0033] Monitor battery charge status, temperature and voltage;

[0034] Perform overheat protection and overcurrent protection functions.

[0035] Preferably, the master node further includes:

[0036] An intelligent interactive interface, electrically connected to the processor module, for:

[0037] Display charging pile information, power information and fault information;

[0038] Receive modification and control instructions from users;

[0039] A scheduling module, electrically connected to the processor module, is used to:

[0040] When there are sufficient charging piles, users are allowed to freely choose charging piles;

[0041] When charging piles are in short supply, charging scheduling is performed according to preset priority rules. Preferably, the charging management platform also includes:

[0042] Data synchronization module, used for:

[0043] When the data of the master node changes, the data will be synchronized to the slave node in real time; after the master node returns to normal, the latest data will be automatically updated and synchronized;

[0044] Data backup module, used for:

[0045] Regularly back up key system data to the backup node;

[0046] When both the master and slave nodes fail at the same time, the system data is restored from the backup node.

[0047] Preferably, the charging management node further includes:

[0048] Multiple safety protection modules for:

[0049] Perform voltage matching detection and only enable charging ports with matching voltages;

[0050] Monitor battery temperature in real time and automatically cut off charging when the temperature exceeds the preset threshold;

[0051] Monitor charging current and automatically disconnect charging when abnormality is detected;

[0052] Wherein, the multiple safety protection module includes a plurality of relays connected in series, which are used to improve the reliability of charging control.

[0053] As a preferred feature, it is characterized in that:

[0054] The charging management nodes communicate with each other and with the charging management platform using the CAN bus, which is used to:

[0055] Achieve high-reliability real-time data transmission;

[0056] Facilitates system expansion and upgrade.

[0057] Preferably, the fault management module further includes:

[0058] Fault diagnosis unit for:

[0059] Analyze the operating data of charging piles and charging management nodes;

[0060] Identify potential failure risks;

[0061] Predictive maintenance unit for:

[0062] Generate maintenance recommendations based on fault diagnosis results;

[0063] Schedule preventive maintenance tasks to reduce system failure rates.

[0064] Preferably, the charging management platform further includes a load balancing module, which is used to:

[0065] Real-time monitoring of the usage status of each charging pile;

[0066] Dynamically adjust charging task allocation according to charging demand and charging pile load;

[0067] While ensuring user experience, maximize the overall utilization efficiency of the charging station.

[0068] Preferably, the charging management platform further includes a user management module, which is used to:

[0069] Manage user accounts and permissions;

[0070] Record user charging history and preferences;

[0071] Provide personalized charging recommendations and services based on user data.

[0072] Preferably, the charging management platform further includes an energy management module for:

[0073] Monitor grid load conditions;

[0074] Optimize charging schedule based on electricity price information and peak electricity consumption;

[0075] Supports bidirectional charging and discharging functions to realize energy interaction between electric vehicles and power grids.

[0076] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0077] The system of the present invention adopts a hierarchical distributed architecture design, combines advanced fault prediction and management technologies, realizes intelligent load balancing and energy management, and introduces multiple data security protection mechanisms. This innovative system design not only greatly improves the operational efficiency and reliability of charging stations, but also provides users with more intelligent and convenient charging services.

[0078] Specifically, the system of the present invention effectively solves the scalability problem of large-scale charging stations by introducing a multi-level node architecture. The collaborative work of the master node, slave node, and backup node not only disperses the system load, but also provides fault redundancy, significantly improving the reliability of the system. In terms of fault management, the present invention introduces predictive maintenance technology based on machine learning, which can identify potential faults in advance, greatly reducing equipment failure rate and maintenance costs.

[0079] The load balancing module of the present invention adopts an advanced multi-objective optimization algorithm, which can dynamically adjust the charging strategy according to the real-time charging demand and grid load, which not only guarantees the charging demand of users, but also optimizes the overall efficiency of the charging station. At the same time, the energy management module of the system realizes the two-way energy interaction between electric vehicles and the grid through the intelligent scheduling algorithm, providing strong support for the development of smart grids.

[0080] In terms of data security, the present invention introduces multiple data synchronization and backup mechanisms, and cooperates with the high-reliability communication of the CAN bus to effectively ensure the integrity and consistency of system data. The design of multiple security protection modules provides reliable security protection from the hardware level, greatly reducing the risk of equipment failure and safety accidents.

[0081] In general, the new energy vehicle battery charging management system of the present invention comprehensively improves the management efficiency, reliability and safety of charging stations through innovative system architecture and advanced technical means. This not only provides better management tools for charging station operators, but also brings a more convenient and efficient charging experience to electric vehicle users. At the same time, the intelligent energy management function of the system has also made important contributions to the stable operation of the power grid and the efficient use of new energy. It can be foreseen that with the promotion and application of the system of the present invention, it will greatly promote the development of the new energy vehicle industry and promote the transportation energy revolution to a deeper level. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 It is a logic block diagram of the entire system of the present invention.

[0083] Figure 2 This is a logic block diagram of the charging management platform of the present invention.

[0084] Figure 3 This is a logic block diagram of the charging management node of the present invention.

[0085] Figure 4 It is a logic block diagram of the fault management module of the present invention. DETAILED DESCRIPTION

[0086] See also Figure 1-4 The present invention provides a new energy vehicle battery charging management system, which aims to solve the problems existing in the existing charging management system in large-scale charging station management, fault handling, load balancing, etc. The present invention will be described in detail below in conjunction with specific implementation methods.

[0087] The new energy vehicle battery charging management system of the present invention comprises a charging management platform 1, multiple charging management nodes 2, a fault management module 3 and a monitoring module 4. These components are interconnected via a wired or wireless network to form an efficient and reliable charging management system.

[0088] The charging management platform 1 is the core of the entire system and is responsible for storing and managing charging pile information and charging management node information. Preferably, the charging management platform 1 adopts a distributed database architecture to ensure high availability and fast access to data. In one embodiment of the present invention, the charging management platform 1 uses Apache Cassandra as the database system. This choice can support large-scale concurrent read and write operations and is very suitable for processing real-time data of a large number of charging stations.

[0089] The charging management platform 1 is also responsible for receiving and processing fault information from the monitoring module 4. After receiving the fault information, the charging management platform 1 will immediately start the corresponding fault handling process. For example, when the output voltage of a charging pile is detected to be abnormal (such as the voltage fluctuation exceeds ±5% of the nominal value), the system will automatically generate a fault work order and assign it to the corresponding maintenance personnel according to the severity of the fault.

[0090] Multiple charging management nodes 2 are connected to the charging management platform 1, and each charging management node 2 corresponds to a charging pile. This one-to-one correspondence ensures that the system can accurately control and monitor the status of each charging pile. The charging management node 2 includes a master node 21, a slave node 22, and a backup node 23, forming a hierarchical management structure.

[0091] The master node 21 is the core component of the charging management node 2, which is responsible for acquiring and processing the real-time charging information of the nearby charging piles and controlling the charging sequence. In a preferred embodiment of the present invention, the master node 21 uses a priority-based scheduling algorithm to control the charging sequence. The algorithm can be expressed as follows:

[0092] P i =w 1 *T i +w 2 *S i +w 3 *D i ,

[0093] Among them, P i is the priority of the i-th charging request, T i is the waiting time, S i is the remaining battery power, D i is the distance to the destination, w 1 、w 2 、w 3 are weight coefficients. By adjusting these weight coefficients, the system can flexibly balance factors such as user waiting time, battery status, and travel requirements. For example, you can set w 1 =0.5, w 2 =0.3, w 3 =0.2, which takes into account the importance of waiting time as well as the battery status and user travel requirements.

[0094] The slave node 22 is responsible for obtaining the real-time charging information of the corresponding charging pile and passing the information to the master node 21. In one embodiment of the present invention, the slave node 22 collects the data of the charging pile every 5 seconds, including parameters such as charging voltage, current, and temperature. These data are transmitted to the master node 21 via the CAN bus, ensuring the real-time and reliability of data transmission.

[0095] The backup node 23 provides a backup function when the master node 21 or the slave node 22 fails. Preferably, the backup node 23 adopts a hot backup mode to synchronize the data of the master node 21 and the slave node 22 in real time. When the master node 21 or the slave node 22 fails (for example, three consecutive heartbeat detection failures), the backup node 23 will take over its function within 500 milliseconds to ensure the continuous operation of the system.

[0096] The fault management module 3 is connected to the charging management platform 1 and is a key component to ensure the reliable operation of the system. The module first prioritizes the charging pile faults according to the charging time. In one embodiment of the present invention, the fault priority F p The calculation formula is as follows:

[0097] Fp =α*T c +β*S f +γ*I u ,

[0098] Among them, T c is the charging time, S f is the fault severity (an integer from 1 to 10), I u is the frequency of use of the charging pile, and α, β, and γ are weight coefficients. For example, α can be set to 0.5, β to 0.3, and γ to 0.2, which not only gives priority to long-term charging faults, but also takes into account the severity of the fault and the importance of the charging pile.

[0099] The fault management module 3 is also responsible for controlling the display and alarm of fault information. In a preferred embodiment of the present invention, fault information is displayed through a multi-level alarm mechanism. For example, for minor faults (such as a slight decrease in charging speed), the system will display a yellow warning on the management interface; for serious faults (such as overheating of the charging pile), the system will issue an audible and visual alarm and automatically disconnect the charging connection.

[0100] In addition, the fault management module 3 also supports remote fault handling. Through remote diagnosis and control technology, maintenance personnel can solve some faults without going to the site, greatly improving maintenance efficiency. For example, for software-related faults, maintenance personnel can remotely restart the system or update the software version; for faults that require hardware intervention, the system can automatically generate a maintenance work order and assign it to the nearest maintenance personnel.

[0101] The monitoring module 4 is connected to the charging management platform 1 and the charging management node 2 in communication, and is responsible for real-time monitoring of the operating status of the entire system. This module adopts a distributed monitoring architecture, and each charging pile is equipped with a local monitoring unit, which is connected to the central monitoring server through the network. The local monitoring unit collects the key parameters of the charging pile once a second, including output voltage, current, temperature, etc., and performs preliminary anomaly detection. If an abnormality is detected (for example, the temperature exceeds 60°C or the current fluctuation exceeds 10% of the rated value), the local monitoring unit will immediately report to the central monitoring server.

[0102] The central monitoring server aggregates the data of all charging piles for global analysis and optimization. For example, by analyzing the usage patterns of charging piles, the system can predict future peak electricity consumption and adjust the charging strategy in advance. In addition, monitoring module 4 also implements intelligent alarm function. Through machine learning algorithms, the system can gradually learn the normal operation mode and identify abnormal conditions more accurately. For example, the system may find that the normal operating temperature of a charging pile in summer is 5°C higher than that in winter, and this seasonal change will not be misjudged as a fault.

[0103] When a fault is detected, the monitoring module 4 will immediately send fault information to the charging management platform 1. This information includes key data such as the type, severity, and occurrence time of the fault. After receiving this information, the charging management platform 1 will immediately start the corresponding fault handling process to ensure that the system can quickly respond to various abnormal situations.

[0104] In general, the new energy vehicle battery charging management system of the present invention realizes efficient, reliable and intelligent charging management through the charging management platform 1, multi-level charging management nodes 2, intelligent fault management module 3 and comprehensive monitoring module 4. The system can not only optimize the charging process and improve the charging efficiency, but also quickly respond to and handle various faults to ensure the safety of the charging process. Through this advanced management system, the user experience of new energy vehicles can be greatly improved, and the rapid development of the new energy vehicle industry can be promoted. In a preferred embodiment of the present invention, the charging management platform 1 also includes a data synchronization module 11 and a data backup module 12, and these two modules jointly ensure the consistency, integrity and reliability of the system data.

[0105] The data synchronization module 11 is responsible for synchronizing the data to the slave node 22 in real time when the data of the master node 21 changes. This real-time synchronization mechanism ensures the data consistency of the entire system and avoids decision errors or system failures that may be caused by data inconsistency. In a specific embodiment of the present invention, the data synchronization module 11 adopts a log-based synchronization algorithm. The core idea of ​​the algorithm is to record each data change on the master node 21 in a transaction log, and then transmit this log to the slave node 22 in real time, and the slave node 22 replays these transactions to maintain data consistency.

[0106] Preferably, the data synchronization module 11 also implements an intelligent conflict resolution mechanism. When the data between the master and slave nodes is detected to be inconsistent, the system will automatically start the conflict resolution process. For example, if the master node 21 and the slave node 22 have different records of the status of the same charging pile, the system will give priority to the latest data and record the conflict for subsequent analysis and improvement.

[0107] In addition, the data synchronization module 11 is also responsible for automatically updating and synchronizing the latest data after the master node 21 returns to normal. This process is usually called catch-up. In one embodiment of the present invention, the system adopts an incremental synchronization strategy, synchronizing only the data that has changed during the offline period of the master node 21, rather than full synchronization, which greatly reduces the time and network bandwidth required for synchronization.

[0108] The main responsibility of the data backup module 12 is to regularly back up the key system data to the backup node 23. In a preferred embodiment of the present invention, a multi-level backup strategy is used for data backup. For example, the system may perform an incremental backup once an hour, a full backup once a day, and a remote backup once a week. This multi-level backup strategy ensures the security of the data while taking into account the backup efficiency and storage cost.

[0109] Preferably, the data backup module 12 also implements an intelligent backup scheduling function. The system will dynamically adjust the backup frequency according to the usage of the charging station. For example, during peak power consumption, the system may increase the backup frequency; during low power consumption periods such as late at night, the backup frequency may be reduced to reduce the impact on system performance.

[0110] In the extreme case where both the master and slave nodes fail at the same time, the data backup module 12 can restore the system data from the backup node 23. The system of the present invention adopts a fast recovery mechanism, and controls the recovery time to a minimum through parallel recovery, incremental recovery and other technologies. For example, for a medium-sized charging station (about 50 charging piles), the system can complete data recovery within 5 minutes to ensure the continuity of charging services.

[0111] In another embodiment of the present invention, the charging management node 2 further includes a multiple safety protection module 24. This module is a key component to ensure the safety and reliability of the charging process, and it implements a multi-level, all-round safety protection mechanism.

[0112] First, the multiple safety protection module 24 performs voltage matching detection and only enables charging interfaces with matching voltages. This function can effectively prevent equipment damage or safety accidents caused by voltage mismatch. In actual applications, the system sets an allowable voltage deviation range, usually ±5% of the nominal voltage. For example, for a charging interface with a nominal voltage of 400V, the system will only allow charging to start when the actual voltage detected is between 380V and 420V.

[0113] Secondly, the multiple safety protection module 24 is also responsible for real-time monitoring of the battery temperature and automatically cutting off charging when the temperature exceeds a preset threshold. In a preferred embodiment of the present invention, the system adopts a multi-level temperature warning and protection mechanism. For example, when the battery temperature reaches 45°C, the system will issue a primary warning; when the temperature reaches 50°C, the system will reduce the charging power; when the temperature exceeds 55°C, the system will immediately cut off charging and start a forced cooling program. This progressive temperature control strategy ensures the safety of charging while maintaining the continuity of charging as much as possible.

[0114] In addition, the multi-safety protection module 24 also monitors the charging current and automatically disconnects the charging connection when an abnormality is detected. The criteria for determining abnormal current are usually based on the charging protocol and battery specifications. For example, for a system with a rated charging current of 100A, if the actual charging current exceeds 110A for more than 5 seconds, the system will determine it as abnormal and take protective measures.

[0115] It is worth noting that the multi-safety protection module 24 of the present invention uses multiple relays in series to control the charging process, which greatly improves the reliability of the system. In traditional systems, only one relay is usually used to control the charging switch. If this relay fails, it may cause serious safety problems. The system of the present invention uses at least two relays in series. Even if one of the relays fails, the other can still ensure safe disconnection. This redundant design significantly improves the safety of the system.

[0116] In the system of the present invention, the charging management nodes 2 and the charging management nodes 2 and the charging management platform 1 use the CAN bus for communication. The CAN (Controller Area Network) bus is a communication protocol widely used in the automotive field, with the characteristics of high reliability, strong real-time performance, strong anti-interference ability, etc., and is very suitable for electric vehicle charging systems.

[0117] The use of the CAN bus enables the system of the present invention to achieve high-reliability real-time data transmission. During the charging process, the system needs to frequently transmit various real-time data, such as charging voltage, current, temperature, etc. The high bandwidth (up to 1Mbps) and low latency characteristics of the CAN bus can meet these data transmission requirements. For example, in one embodiment of the present invention, the system transmits charging status data every 100 milliseconds, and the CAN bus can easily handle this high-frequency data transmission.

[0118] In addition, the multi-host feature of the CAN bus also facilitates the expansion and upgrade of the system. In the system of the present invention, new charging management nodes or other functional modules can be easily added without making major changes to the existing system. For example, if a new energy management module needs to be added, it only needs to be connected to the CAN bus and assigned a unique identifier to achieve communication with other modules.

[0119] In addition, the error detection and correction mechanism of the CAN bus also enhances the reliability of the system. The CAN protocol has built-in error detection mechanisms such as cyclic redundancy check (CRC), which can effectively identify and handle data errors during the communication process. In the system of the present invention, if a data transmission error is detected, the system will automatically retransmit the data to ensure that the charging process will not be interrupted due to communication problems.

[0120] In general, by adopting the data synchronization module 11, the data backup module 12, the multiple safety protection module 24 and the CAN bus communication, the new energy vehicle battery charging management system of the present invention achieves high data reliability, high safety of the charging process and high scalability of the system. These characteristics enable the system to better meet the management needs of large-scale charging stations and provide strong technical support for the popularization of new energy vehicles. In a preferred embodiment of the present invention, the fault management module 3 also includes a fault diagnosis unit 31 and a predictive maintenance unit 32, which together constitute an advanced fault prevention and processing system, greatly improving the reliability and operational efficiency of the charging station.

[0121] The main function of the fault diagnosis unit 31 is to analyze the operating data of the charging pile and the charging management node and identify potential fault risks. The unit uses a fault diagnosis algorithm based on machine learning, which can extract valuable information from massive operating data and detect abnormal conditions in a timely manner. For example, the fault diagnosis unit 31 may find that the charging efficiency of a charging pile has gradually decreased over the past week. Although it has not yet reached the fault standard, it has already shown potential problems.

[0122] In a specific embodiment of the present invention, the fault diagnosis unit 31 uses an improved long short-term memory network (LSTM) model to perform fault prediction. The model can be expressed as:

[0123] h t =tanh(W h [h t-1 ,x t ]+b h ),

[0124] y t =σ(W y h t +b y ),

[0125] Among them, h t is the hidden state, x t is the input vector (including charging voltage, current, temperature and other parameters), y t is the failure probability of the output, W h , W y is the weight matrix, b h 、b y is the bias vector, tanh and σ are the hyperbolic tangent function and sigmoid function respectively.

[0126] This model can effectively capture the time series characteristics of charging data, thereby accurately predicting potential failures. For example, by analyzing the small fluctuation patterns of the charging current, the system may predict the impending failure of the charging module several days in advance. The predictive maintenance unit 32 generates maintenance recommendations and schedules preventive maintenance tasks based on the results of the fault diagnosis. This unit adopts a maintenance scheduling algorithm based on risk assessment. The algorithm can be expressed as:

[0127] M i =αF i +βC i +γT i ,

[0128] Among them, M i is the maintenance priority of the i-th charging pile, F i is the predicted failure probability, C i is the maintenance cost, T i is the time since the last maintenance, and α, β, and γ are weight coefficients. By adjusting these weight coefficients, the system can achieve the best balance between maintenance cost and equipment reliability.

[0129] Preferably, the predictive maintenance unit 32 also implements an intelligent maintenance scheduling function. The system automatically generates an optimal maintenance plan based on factors such as the usage of the charging station and the availability of maintenance personnel. For example, the system may choose to perform maintenance at night when charging demand is low to minimize the impact on users.

[0130] In another embodiment of the present invention, the charging management platform 1 further includes a load balancing module 13. The module monitors the usage status of each charging pile in real time, and dynamically adjusts the charging task allocation according to the charging demand and the load of the charging pile, thereby maximizing the overall utilization efficiency of the charging station while ensuring the user experience.

[0131] The load balancing module 13 adopts a task allocation algorithm based on multi-objective optimization. The algorithm can be expressed as:

[0132]

[0133] Among them, X is the allocation scheme of charging tasks, f i (X) are different optimization objectives, such as waiting time, charging pile utilization, grid load balance, etc. This multi-objective optimization problem is solved by an improved non-dominated sorting genetic algorithm (NSGA-II), which can find the best balance among multiple conflicting objectives.

[0134] For example, during peak charging periods, the system may prioritize minimizing user waiting time; while during peak grid load periods, the system may focus more on balancing the loads of each charging pile to avoid excessive pressure on the grid. Through this dynamic adjustment, the system of the present invention can adapt to charging needs in different time periods and scenarios, greatly improving the operating efficiency of charging stations.

[0135] In a preferred embodiment of the present invention, the charging management platform 1 further includes a user management module 14. This module is responsible for managing user accounts and permissions, recording user charging history and preferences, and providing personalized charging suggestions and services based on these data.

[0136] The user management module 14 adopts a recommendation algorithm based on collaborative filtering, which can recommend the most suitable charging time and place for users. The core idea of ​​this algorithm is to use the user's historical behavior data to find similar users, and then make recommendations based on the behavior of similar users. Specifically, user similarity can be calculated by cosine similarity:

[0137]

[0138] Among them, u and v are two users, I uv is the set of charging stations that two users have evaluated together, r ui and r vi are the ratings of user u and user v on charging station i respectively.

[0139] Based on this similarity calculation, the system can recommend the most suitable charging station and charging time for the user. For example, if the system finds that a user often charges after get off work and prefers fast charging stations, the system may recommend a nearby fast charging station to the user before the user gets off work and reserve a charging spot.

[0140] In addition, the user management module 14 also implements an intelligent authority management function. The system dynamically adjusts the user's authority level based on factors such as the user's usage frequency and credit record. For example, users with high frequency of use and good credit may be given priority reservation rights or longer reservation time. This mechanism not only improves the user experience, but also helps to improve the operational efficiency of the charging station.

[0141] Finally, in one embodiment of the present invention, the charging management platform 1 also includes an energy management module 15. This module is responsible for monitoring the load of the power grid, optimizing the charging schedule according to the electricity price information and the peak power consumption, and supporting the bidirectional charging and discharging function to realize the energy interaction between the electric vehicle and the power grid.

[0142] The energy management module 15 adopts an energy scheduling algorithm based on model predictive control (MPC). The algorithm can be expressed as:

[0143]

[0144] stx t +1 = Ax t +Bu t ,t=1,…,T-1,

[0145] x min ≤x t ≤x max ,t=1,…,T,

[0146] u min ≤u t ≤u max ,t=1,…,T,

[0147] Among them, u t is the control variable (such as charging power), x t is the state variable (such as battery power, grid load), c t is the cost vector (such as electricity price), Q and R are weight matrices, and A and B are system dynamic matrices.

[0148] By solving this optimization problem, the system can formulate the best charging strategy based on multiple factors such as electricity price, grid load, and user demand. For example, at night when electricity prices are low, the system may encourage users to charge slowly; and during peak hours, the system may use the batteries of electric vehicles to reversely supply power to the grid to achieve the effect of peak load reduction and valley load filling.

[0149] In general, the new energy vehicle battery charging management system of the present invention realizes efficient, intelligent and reliable charging management through the intelligent diagnosis and predictive maintenance of the fault management module 3, the dynamic task allocation of the load balancing module 13, the personalized service of the user management module 14, and the intelligent scheduling of the energy management module 15. These advanced functions not only greatly improve the operating efficiency and user experience of the charging station, but also make important contributions to promoting the popularization of new energy vehicles and the development of smart grids.

[0150] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. New energy vehicle battery charging management system, characterized in that: include: Charging management platform for: Store and manage charging pile information and charging management node information; Receive and process fault information from the monitoring module; A plurality of charging management nodes are communicatively connected with the charging management platform, each of the charging management nodes corresponds to a charging pile, and the charging management nodes include a master node, a slave node and a standby node, wherein: The master node is used to obtain and process the real-time charging information of nearby charging piles and control the charging sequence; The slave node is used to obtain the real-time charging information of the corresponding charging pile and transmit the information to the master node; The standby node is used to provide a backup function when the master node or the slave node fails; A fault management module is connected to the charging management platform for: Prioritize charging pile failures based on charging time; Control the display and alarm of fault information; Remote troubleshooting; A monitoring module is connected to the charging management platform and the charging management node for: Detect the operating status of each charging pile and charging management node; When a fault is detected, fault information is sent to the charging management platform.

2. The system according to claim 1, characterized in that The charging management node also includes: Processor module for: Processing real-time charging information from charging piles; Execute charging control logic; A communication module is electrically connected to the processor module and is used to: Exchange data with the charging management platform; Transmit information between different charging management nodes; A positioning module is electrically connected to the processor module and is used to: Obtain the geographic location information of the charging management node; A battery management module is electrically connected to the processor module and is used to: Monitor battery charge status, temperature and voltage; Perform overheat protection and overcurrent protection functions.

3. The system according to claim 2, characterized in that The master node also includes: An intelligent interactive interface, electrically connected to the processor module, for: Display charging pile information, power information and fault information; Receive modification and control instructions from users; A scheduling module, electrically connected to the processor module, is used to: When there are sufficient charging piles, users are allowed to freely choose charging piles; When charging piles are in short supply, charging scheduling is carried out according to preset priority rules.

4. The system according to claim 1, characterized in that The charging management platform further includes: a data synchronization module, which is used to: When the data of the master node changes, the data is synchronized to the slave node in real time; After the master node returns to normal, it automatically updates and synchronizes the latest data; Data backup module, used for: Regularly back up key system data to the backup node; When both the master and slave nodes fail at the same time, the system data is restored from the backup node.

5. The system according to claim 1, characterized in that The charging management node also includes: a multiple safety protection module, which is used to: Perform voltage matching detection and only enable charging ports with matching voltages; Monitor battery temperature in real time and automatically cut off charging when the temperature exceeds the preset threshold; Monitor charging current and automatically disconnect charging when abnormality is detected; Wherein, the multiple safety protection module includes a plurality of relays connected in series, which are used to improve the reliability of charging control.

6. The system according to claim 1, characterized in that: The charging management nodes communicate with each other and with the charging management platform using the CAN bus, which is used to: Achieve high-reliability real-time data transmission; Facilitates system expansion and upgrade.

7. The system according to claim 1, characterized in that The fault management module also includes: Fault diagnosis unit for: Analyze the operating data of charging piles and charging management nodes; Identify potential failure risks; Predictive maintenance unit for: Generate maintenance recommendations based on fault diagnosis results; Schedule preventive maintenance tasks to reduce system failure rates.

8. The system according to claim 1, characterized in that: The charging management platform also includes a load balancing module, which is used to: Real-time monitoring of the usage status of each charging pile; Dynamically adjust charging task allocation according to charging demand and charging pile load; While ensuring user experience, maximize the overall utilization efficiency of the charging station.

9. The system according to claim 1, characterized in that: The charging management platform also includes a user management module, which is used to: Manage user accounts and permissions; Record user charging history and preferences; Provide personalized charging recommendations and services based on user data.

10. The system according to claim 1, characterized in that: The charging management platform also includes an energy management module for: Monitor grid load conditions; Optimize charging schedule based on electricity price information and peak electricity consumption; Supports bidirectional charging and discharging functions to realize energy interaction between electric vehicles and power grids.

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