A control method and system for a mobile charging pile

Abstract

This invention relates to the field of mobile charging pile technology, specifically to a control method and system for mobile charging piles, comprising the following steps: S1, charging pile positioning and user demand reception: obtaining real-time location information of the mobile charging pile via GPS; S2, path planning and charging pile movement: driving the charging pile to the user-specified location via a control device; S3, charging preparation: performing charging preparation work; S4, electromagnetic interference suppression: suppressing electromagnetic interference; S5, charging completion: after charging is completed, disconnecting the power connection and notifying the user of charging completion via the user terminal; S6, dynamic load balancing: monitoring and adjusting the output power of the charging pile in real time; S7, charging pile return and data recording: the mobile charging pile returns to its initial position or moves to a standby position according to instructions from the charging pile control center. This invention achieves efficient scheduling and management of mobile charging piles.

Description

Technical Field

[0001] This invention relates to the field of mobile charging pile technology, and in particular to a control method and system for mobile charging piles. Background Technology

[0002] With the increasing popularity of electric vehicles, the demand for charging infrastructure is growing. Traditional fixed charging piles may not be able to meet the flexible and ever-changing charging needs in some cases, especially in remote areas or special scenarios. The installation and maintenance costs of fixed charging piles are high, and they cannot effectively cover all areas. Therefore, mobile charging piles, as a new type of charging solution, are gradually attracting attention and research.

[0003] Existing mobile charging stations lack precision and efficiency in scheduling and management, often failing to respond to users' charging needs in a timely manner, resulting in long waiting times and poor service quality. In complex electromagnetic environments, electromagnetic interference significantly affects the communication and control signals of charging stations. Existing technologies have limited effectiveness in suppressing electromagnetic interference, leading to system instability and impacting the reliability of the charging process. Traditional load management methods struggle to predict and adjust the output power of charging stations in real time and accurately, failing to effectively balance the load among multiple charging stations, resulting in unbalanced grid load and affecting overall charging efficiency and system stability.

[0004] To address the above problems, this invention provides a control method for mobile charging piles. Through steps such as charging pile positioning and user demand reception, path planning and charging pile movement, charging preparation, electromagnetic interference suppression, charging completion, dynamic load balancing, and charging pile return and data recording, the method achieves efficient scheduling and management of mobile charging piles, ensuring the stability and reliability of the charging process, while optimizing the efficiency of charging services and user experience. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides a control method and system for mobile charging piles.

[0006] A control method for a mobile charging station includes the following steps:

[0007] S1, Charging Pile Positioning and User Demand Reception: Obtain the real-time location information of the mobile charging pile through GPS and send the location information to the charging pile control center. Receive the user's charging demand through the user terminal. The charging demand includes charging time, charging amount, and charging location.

[0008] S2, Path planning and charging pile movement: Based on the user's charging needs and the real-time location information of the charging pile, the optimal driving route of the mobile charging pile is calculated using a path planning algorithm, and the driving route is sent to the charging pile control center. According to the optimal driving route, the charging pile is driven to the user's designated location by controlling the mobile device.

[0009] S3, Charging Preparation: After the mobile charging station arrives at the designated location, it performs charging preparation work, including automatically connecting to the power supply and checking the status of the charging equipment.

[0010] S4, Electromagnetic Interference Suppression: Adaptive filtering algorithms are used to process the communication and control signals of the charging pile to suppress electromagnetic interference;

[0011] S5, Charging and Completion: After confirming that the charging preparation work is completed, the electric vehicle will start charging and the charging status will be monitored in real time. After charging is completed, the power connection will be disconnected and the user will be notified of the completion of charging through the user terminal.

[0012] S6, Dynamic Load Balancing: Real-time monitoring and adjustment of the output power of charging piles, dynamically adjusting the charging power according to the status of electric vehicle batteries and charging needs, while balancing the load among multiple charging piles;

[0013] S7, Charging Pile Return and Data Recording: After charging is completed, the mobile charging pile returns to its initial position or moves to a standby position according to the instructions of the charging pile control center. It records relevant data for each charging session, including charging time, charging amount, and charging cost, and sends the relevant data to the charging pile control center for analysis.

[0014] Furthermore, the path planning and charging pile movement in S2 include:

[0015] S21, Optimal route calculation: Combining user charging needs and real-time location information of charging piles, the optimal driving route is calculated using the Dijkstra algorithm.

[0016] S22, Information transmission: The calculated optimal driving route is sent to the charging pile control center, and the charging pile control center receives the driving route information;

[0017] S23, Charging Pile Movement: Based on the optimal driving route, the charging pile is driven to the user's designated location by controlling a mobile device (such as an autonomous driving system, path tracking control module, etc.). The mobile device adjusts the driving path in real time based on the received driving route information and sensor data (such as LiDAR, cameras, etc.).

[0018] Furthermore, the electromagnetic interference suppression in S4 includes:

[0019] S41, Electromagnetic Interference Detection: Real-time monitoring of the communication and control signals of the charging pile through an electromagnetic interference detection mechanism to obtain characteristic information of electromagnetic interference, including interference frequency and amplitude;

[0020] S42, Adaptive Filter Initialization: The acquired electromagnetic interference characteristic information is input into the adaptive filter, and the adaptive filter dynamically adjusts the filtering parameters according to the input signal to suppress electromagnetic interference;

[0021] S43, Filter weight update: The weights of the adaptive filter are updated using the recursive least squares (RLS) algorithm;

[0022] S44, Signal Processing and Application: Using the signal processed by the adaptive filter for communication and control of charging piles.

[0023] Furthermore, the electromagnetic interference detection in S41 includes:

[0024] S411, Electromagnetic Interference Detection Device Installation: An electromagnetic interference detection device is installed in the communication and control circuit of the charging pile. The electromagnetic interference detection device includes an electromagnetic interference sensor and a signal processing unit to monitor and process electromagnetic interference signals in real time.

[0025] S412, Signal Processing Unit Analysis: The signal processing unit performs spectral analysis on the electromagnetic interference signal, extracts the characteristic information of the interference signal, and the calculation formula is as follows:

[0026]

[0027] Where X(f) is the frequency domain signal, x(t) is the time domain signal, f is the frequency, and t is the time;

[0028] S413, Feature Information Transmission: The acquired electromagnetic interference feature information is transmitted to the adaptive filter for electromagnetic interference suppression processing.

[0029] Furthermore, the recursive least squares (RLS) algorithm includes:

[0030] Gain vector calculation:

[0031] Where P(n-1) is the covariance matrix in the (n-1)th iteration, x(n) is the input signal vector in the nth iteration, and λ is the forgetting factor (0<λ≤1);

[0032] Weight update: w(n) = w(n-1)· + K(n)e(n);

[0033] Where w(n) is the filter weight in the nth iteration, K(n) is the gain vector, and e(n) is the current error;

[0034] Covariance matrix update:

[0035] Furthermore, the dynamic load balancing in S6 includes:

[0036] S61, Battery Status Monitoring: Real-time monitoring of electric vehicle battery status information, including battery charge, battery health status, and charging requirements;

[0037] S62, Load Forecasting: Based on battery status information and charging demand, the load forecasting model is used to predict the real-time load demand of the charging pile.

[0038] S63, dynamically adjusts the output power of charging piles: based on predicted load demand and real-time monitored battery status, calculates the optimal output power of each charging pile and adjusts the output power of the charging piles in real time.

[0039] S64, balancing the load among charging piles: By monitoring the load status of charging piles in real time, when the load of a charging pile is too high, the load is automatically distributed, and the load balancing algorithm is used to optimize the load distribution.

[0040] Furthermore, the load prediction model in S62 employs a Generative Adversarial Network (GAN) model, which includes:

[0041] S621, Data Acquisition and Feature Extraction: Acquire electric vehicle battery status information (battery power, battery health status) and charging demand (charging time, charging amount), and extract time series features, including battery power change rate, historical charging records and environmental factors.

[0042] S622, Initialize the generator and discriminator: Initialize the weights and biases of the generator G and the discriminator D;

[0043] S623, Conditional Input: The electric vehicle battery status and charging demand are used as conditional input c, combined with random noise z input to the generator;

[0044] S624, Loss function definition:

[0045] Discriminator loss function:

[0046]

[0047] Among them, L D Here, L is the discriminator's loss function, c is the actual load demand, and p is the conditional input (electric vehicle battery state and charging demand). data Here, D(L|c) is the actual data distribution, D(L|c) is the discriminator's output of the actual load demand L under given condition c, z is a random noise vector, and p zG(z,c) is the distribution of the noise vector, G(z,c) is the load demand generated by the generator under a given noise vector z and condition c, and D(G(z,c)|c) is the output of the discriminator on the generated load demand G(z,c) under a given condition c.

[0048] Generator loss function:

[0049]

[0050] Among them, L G It is the loss function of the generator, p c It is the distribution of conditional inputs;

[0051] S625, Adaptive Learning Rate Adjustment: The learning rate is dynamically adjusted based on changes in loss during training. The calculation formula is as follows:

[0052]

[0053] Where α(t) is the learning rate for the t-th iteration, and β is the adjustment coefficient. The rate of change of the loss function;

[0054] S626, Training the producer and discriminator: Repeat the training process until the loss function converges. The training process is as follows:

[0055] Update the discriminator:

[0056] Update generator:

[0057] Where, θ D These are the parameters of the discriminator, α D It is the learning rate of the discriminator. The discriminator loss function L D For parameter θ D The gradient, θ G It is the parameter of the generator, α G It is the learning rate of the generator. The generator loss function L G For parameter θ G The gradient;

[0058] S627, Load Prediction: Using a trained generator G, receiving conditional input c and random noise z, to generate predicted load demand.

[0059] Furthermore, the dynamic adjustment of the charging pile output power in S63 includes:

[0060] S631, Initial Output Power Calculation: Obtain the predicted load demand Based on the real-time monitored battery status S, the initial output power P of each charging station is calculated according to the current battery capacity E and charging demand D. init The calculation formula is:

[0061]

[0062] Where D is the battery's charging requirement, E is the current battery level, and T is the expected charging time;

[0063] S632, Optimal Output Power Calculation: Based on the predicted load demand and real-time monitored battery status, the output power is adjusted using a PID control algorithm. The calculation formula is as follows:

[0064]

[0065] Among them, P i (t) represents the output power of the i-th charging pile at time t. For the error, K p For proportional gain, K i For integral gain, K d This is the differential gain;

[0066] S633, Real-time adjustment of charging pile output power: Based on the output power calculated by the PID control algorithm, the output power of each charging pile is adjusted in real time. The calculation formula is as follows:

[0067]

[0068] in, α is the output power of the i-th charging pile at time t, calculated by the PID control algorithm, where α is the adjustment coefficient.

[0069] Furthermore, the load between the balancing charging piles in S64 includes:

[0070] S641, real-time monitoring of the load status of charging piles: real-time monitoring of the load information of each charging pile, including the current output power and load status, and recording the load status of each charging pile to form historical load data;

[0071] S642, Detecting overload: Analyzes the load information monitored in real time, and triggers the load distribution mechanism when the load of the charging pile exceeds the preset threshold.

[0072] S643, Automatic load distribution: After detecting an excessively high load, it automatically distributes the predetermined load to a charging station with a low load.

[0073] S644, Optimize load distribution using a load balancing algorithm: The load balancing algorithm optimizes the load distribution among charging stations. The calculation formula is as follows:

[0074]

[0075] in, For the load of the i-th charging pile after redistribution, The load before allocation is ΔP, the total load to be allocated is N, and the number of charging piles with lower load is N.

[0076] A control system for a mobile charging pile, used to implement the aforementioned control method for a mobile charging pile, includes the following modules:

[0077] Charging pile positioning and user demand receiving module: acquires the real-time location information of the mobile charging pile, sends the location information to the charging pile control center, and receives the user's charging demand;

[0078] Path planning and charging pile mobility module: Based on the user's charging needs and the real-time location information of the charging pile, calculate the optimal driving route of the mobile charging pile and send the driving route to the charging pile control center. According to the optimal driving route, drive the charging pile to the user's designated location by controlling the mobile device.

[0079] Charging preparation module: After the mobile charging station arrives at the designated location, it performs charging preparation work and checks the status of the charging equipment;

[0080] Electromagnetic interference suppression module: processes the communication and control signals of the charging pile to suppress electromagnetic interference;

[0081] Charging and Completion Module: After confirming that the charging preparation work is completed, the module starts charging the electric vehicle and monitors the charging status in real time. After charging is completed, the power connection is disconnected and the user is notified of the completion of charging through the user terminal.

[0082] Dynamic load balancing module: Real-time monitoring and adjustment of the output power of charging piles, dynamic adjustment of charging power based on the status of electric vehicle batteries and charging demand, and balance of load among multiple charging piles;

[0083] Charging pile return and data recording module: After charging is completed, the mobile charging pile returns to the initial position or moves to the standby position according to the instructions of the charging pile control center, records the relevant data for each charging, and sends the relevant data to the charging pile control center for analysis.

[0084] The beneficial effects of this invention are:

[0085] This invention achieves efficient scheduling and management of mobile charging stations through precise charging station positioning and user demand reception, route planning, and charging station movement. By using Dijkstra's algorithm to calculate the optimal driving route, it ensures that mobile charging stations can quickly and efficiently reach the user's designated location, reducing travel time and energy consumption, and improving the scheduling efficiency and service quality of charging stations. In addition, the automatic power connection and equipment status check functions of the charging stations ensure the efficiency and safety of charging preparation, optimizing the overall efficiency of charging services and user experience.

[0086] This invention processes the communication and control signals of charging piles using an adaptive filtering algorithm, effectively suppressing electromagnetic interference and ensuring system stability and reliability. Through real-time electromagnetic interference detection and dynamic adjustment of the adaptive filter, the charging pile system can operate normally in complex electromagnetic environments, improving communication quality and control accuracy. At the same time, the dynamic load balancing mechanism balances the load among multiple charging piles by real-time monitoring and adjustment of the output power of the charging piles, ensuring grid stability and maximizing charging efficiency, avoiding faults caused by overload, and further improving system reliability.

[0087] This invention utilizes a Generative Adversarial Network (GAN) model to accurately predict the load demand of mobile charging stations based on the battery status and charging demand information of electric vehicles. The GAN model can generate high-quality load demand prediction results, improving the response speed and prediction accuracy of the charging station system, and optimizing the stability and reliability of the charging service. Furthermore, it dynamically adjusts the output power of the charging stations through a PID control algorithm and optimizes the load distribution among the charging stations using a load balancing algorithm, ensuring the efficient operation and overall efficiency of the charging network. This mechanism not only improves the quality of the charging service but also ensures the stable operation of the power grid. Attached Figure Description

[0088] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0089] Figure 1 This is a schematic diagram of the control method flow according to an embodiment of the present invention;

[0090] Figure 2 This is a schematic diagram of the system functional modules according to an embodiment of the present invention. Detailed Implementation

[0091] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0092] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0093] like Figure 1 As shown, a control method for a mobile charging station includes the following steps:

[0094] S1, Charging Pile Positioning and User Demand Reception: Obtain the real-time location information of the mobile charging pile through GPS and send the location information to the charging pile control center. Receive the user's charging demand through the user terminal. The charging demand includes charging time, charging amount, and charging location.

[0095] S2, Path planning and charging pile movement: Based on the user's charging needs and the real-time location information of the charging pile, the optimal driving route of the mobile charging pile is calculated using a path planning algorithm, and the driving route is sent to the charging pile control center. According to the optimal driving route, the charging pile is driven to the user's designated location by controlling the mobile device.

[0096] S3, Charging Preparation: After the mobile charging station arrives at the designated location, it performs charging preparation work, including automatically connecting to the power supply and checking the status of the charging equipment.

[0097] S4, Electromagnetic Interference Suppression: Adaptive filtering algorithms are used to process the communication and control signals of the charging pile to suppress electromagnetic interference and ensure the stability and reliability of the system.

[0098] S5, Charging and Completion: After confirming that the charging preparation work is completed, the electric vehicle will start charging and the charging status will be monitored in real time. After charging is completed, the power connection will be disconnected and the user will be notified of the completion of charging through the user terminal.

[0099] S6, Dynamic Load Balancing: Real-time monitoring and adjustment of the output power of charging piles, dynamic adjustment of charging power based on the status of electric vehicle batteries and charging demand, and balance of load among multiple charging piles to ensure grid stability and maximize charging efficiency.

[0100] S7, Charging Pile Return and Data Recording: After charging is completed, the mobile charging pile returns to its initial position or moves to a standby position according to the instructions of the charging pile control center. It records relevant data for each charging session, including charging time, charging amount, and charging cost, and sends the relevant data to the charging pile control center for analysis to optimize future charging services.

[0101] Through the above steps, efficient scheduling and management of mobile charging stations are achieved, ensuring the stability and reliability of the charging process, while optimizing the efficiency of charging services and user experience.

[0102] The route planning and charging station relocation in S2 include:

[0103] S21, Optimal route calculation: Combining user charging needs and real-time location information of charging piles, the optimal driving route is calculated using the Dijkstra algorithm.

[0104] Dijkstra's algorithm includes:

[0105] Constructing a graph model: The service area of ​​the mobile charging station is constructed as a graph model, where nodes represent locations, edges represent paths, and the weight of the edges is the path distance or travel time.

[0106] Initialize distance set: Set the distance from the starting node (the current location of the charging pile) to 0, the distance of other nodes to ∞, and add all nodes to the unvisited set;

[0107] Select the node with the smallest distance: Select the node with the smallest distance from the unvisited set as the current node, denoted as u;

[0108] Update adjacent node distances: For each adjacent node v of the current node u, calculate the distance from the starting point to v. If this distance is less than the distance of the current record, update the distance. The calculation formula is:

[0109] d(v)=min(d(v),d(u)+w(u,v));

[0110] Where d(v) is the current shortest distance of node v, d(u) is the current shortest distance of node u, and w(u,v) is the path weight (distance or time) from node u to node v.

[0111] Mark node: Mark the current node u as visited and remove it from the unvisited set;

[0112] Repeatedly select the shortest distance node: Repeatedly select the shortest distance node to the marked node until the shortest path from the start point to the end point is found or the unvisited set is empty;

[0113] S22, Information transmission: The calculated optimal driving route is sent to the charging pile control center, and the charging pile control center receives the driving route information;

[0114] S23, Charging Pile Movement: Based on the optimal driving route, the charging pile is driven to the user's designated location by controlling the mobile device (such as an autonomous driving system, path tracking control module, etc.). The mobile device adjusts the driving path in real time based on the received driving route information and sensor data (such as LiDAR, camera, etc.) to ensure safe arrival at the user's designated location.

[0115] This invention combines the user's charging needs with the real-time location information of the charging station, and uses Dijkstra's algorithm to calculate the optimal driving route, ensuring that the mobile charging station can quickly and efficiently reach the user's designated location. Dijkstra's algorithm optimizes route selection by accurately calculating path distance or travel time, reducing travel time and energy consumption, improving the scheduling efficiency and service quality of the charging station, and ensuring the reliable operation of the mobile charging station in complex urban environments by controlling the mobile device to accurately execute the optimal driving route.

[0116] Electromagnetic interference suppression in S4 includes:

[0117] S41, Electromagnetic Interference Detection: Real-time monitoring of the communication and control signals of the charging pile through an electromagnetic interference detection mechanism to obtain characteristic information of electromagnetic interference, including interference frequency and amplitude;

[0118] S42, Adaptive Filter Initialization: The acquired electromagnetic interference characteristic information is input into the adaptive filter, and the adaptive filter dynamically adjusts the filtering parameters according to the input signal to suppress electromagnetic interference;

[0119] S43, Filter weight update: The weights of the adaptive filter are updated using the recursive least squares (RLS) algorithm;

[0120] S44, Signal Processing and Application: The signal processed by the adaptive filter is used for the communication and control of the charging pile to ensure the stability and reliability of the system;

[0121] By using an adaptive filtering algorithm to process the communication and control signals of the charging pile, this invention can suppress electromagnetic interference in real time, ensuring the stability and reliability of the system. The adaptive filter dynamically adjusts the filtering parameters through the LMS or RLS algorithm, which can effectively filter interference signals in complex electromagnetic environments, improve the communication quality and control accuracy of the charging pile, and thus enhance the performance of the entire system and the user experience.

[0122] Electromagnetic interference detection in S41 includes:

[0123] S411, Electromagnetic Interference Detection Device Installation: An electromagnetic interference detection device is installed in the communication and control circuit of the charging pile. The electromagnetic interference detection device includes an electromagnetic interference sensor and a signal processing unit to monitor and process electromagnetic interference signals in real time.

[0124] S412, Signal Processing Unit Analysis: The signal processing unit performs spectral analysis on the electromagnetic interference signal, extracts the characteristic information of the interference signal, and the calculation formula is as follows:

[0125]

[0126] Where X(f) is the frequency domain signal, x(t) is the time domain signal, f is the frequency, and t is the time;

[0127] S413, Feature Information Transmission: The acquired electromagnetic interference feature information is transmitted to the adaptive filter for electromagnetic interference suppression processing. This feature information can help the adaptive filter dynamically adjust the filtering parameters and accurately suppress electromagnetic interference.

[0128] By using an electromagnetic interference detection device to monitor the communication and control signals of the charging pile in real time, the characteristic information of electromagnetic interference, including interference frequency and amplitude, can be obtained. This provides accurate input data for subsequent adaptive filtering. This mechanism can effectively identify and analyze electromagnetic interference sources, thereby more accurately suppressing interference and ensuring the stability and reliability of the charging pile system.

[0129] Recursive Least Squares (RLS) algorithms include:

[0130] Gain vector calculation:

[0131] Where P(n-1) is the covariance matrix in the (n-1)th iteration, x(n) is the input signal vector in the nth iteration, and λ is the forgetting factor (0<λ≤1);

[0132] Weight update: w(n) = w(n-1) + K(n)e(n);

[0133] Where w(n) is the filter weight in the nth iteration, K(n) is the gain vector, and e(n) is the current error;

[0134] Covariance matrix update:

[0135] The RLS algorithm ensures that the filter can quickly adapt to new signal characteristics, thereby effectively suppressing electromagnetic interference and ensuring the stability and reliability of communication and control signals.

[0136] Dynamic load balancing in S6 includes:

[0137] S61, Battery Status Monitoring: Real-time monitoring of electric vehicle battery status information, including battery charge, battery health status, and charging requirements;

[0138] S62, Load Forecasting: Based on battery status information and charging demand, the load forecasting model is used to predict the real-time load demand of the charging pile.

[0139] S63, dynamically adjusts the output power of charging piles: based on predicted load demand and real-time monitored battery status, calculates the optimal output power of each charging pile and adjusts the output power of the charging piles in real time to ensure a stable and efficient charging process.

[0140] S64, balancing the load among charging piles: By monitoring the load status of charging piles in real time, when the load of a charging pile is too high, the load is automatically distributed, and the load balancing algorithm is used to optimize the load distribution, ensuring the stable and efficient operation of the charging network and avoiding failures caused by overload.

[0141] By monitoring the status of electric vehicle batteries and charging needs in real time, this invention can dynamically adjust the output power of charging piles to ensure the stability and efficiency of the charging process. At the same time, through load prediction and balancing algorithms, it optimizes the load distribution among multiple charging piles, improving the efficiency and reliability of the entire charging network. This mechanism not only improves the quality of charging services but also ensures the stable operation of the power grid.

[0142] The load prediction model in S62 uses a Generative Adversarial Network (GAN) model, which includes:

[0143] S621, Data Acquisition and Feature Extraction: Acquire electric vehicle battery status information (battery power, battery health status) and charging demand (charging time, charging amount), and extract time series features, including battery power change rate, historical charging records and environmental factors.

[0144] S622, Initialize the generator and discriminator: Initialize the weights and biases of the generator G and the discriminator D;

[0145] S623, Conditional Input: The electric vehicle battery status and charging demand are used as conditional input c, combined with random noise z input to the generator;

[0146] S624, Loss function definition:

[0147] Discriminator loss function:

[0148]

[0149] Among them, L D Here, L is the discriminator's loss function, c is the actual load demand, and p is the conditional input (electric vehicle battery state and charging demand).data Here, D(L|c) is the actual data distribution, D(L|c) is the discriminator's output of the actual load demand L under given condition c, z is a random noise vector, and p z G(z,c) is the distribution of the noise vector, G(z,c) is the load demand generated by the generator under a given noise vector z and condition c, and D(G(z,c)|c) is the output of the discriminator on the generated load demand G(z,c) under a given condition c.

[0150] Generator loss function:

[0151]

[0152] Among them, L G It is the loss function of the generator, p c It is the distribution of conditional inputs;

[0153] S625, Adaptive Learning Rate Adjustment: The learning rate is dynamically adjusted based on changes in loss during training. The calculation formula is as follows:

[0154]

[0155] Where α(t) is the learning rate for the t-th iteration, and β is the adjustment coefficient. The rate of change of the loss function;

[0156] S626, Training the producer and discriminator: Repeat the training process until the loss function converges. The training process is as follows:

[0157] Update the discriminator:

[0158] Update generator:

[0159] Where, θ D These are the parameters of the discriminator, α D It is the learning rate of the discriminator. The discriminator loss function L D For parameter θ D The gradient, θ G It is the parameter of the generator, α G It is the learning rate of the generator. The generator loss function L G For parameter θ G The gradient;

[0160] S627, Load Prediction: Using a trained generator G, receiving conditional input c and random noise z, to generate predicted load demand.

[0161] This invention achieves efficient and accurate prediction of load demand for mobile charging stations by using a Generative Adversarial Network (GAN) model. The GAN model can utilize the battery status and charging demand information of electric vehicles to generate high-quality load demand prediction results, improve the response speed and prediction accuracy of the charging station system, and thus optimize the stability and reliability of charging services.

[0162] The S63's dynamic adjustment of charging station output power includes:

[0163] S631, Initial Output Power Calculation: Obtain the predicted load demand Based on the real-time monitored battery status S, the initial output power P of each charging station is calculated according to the current battery capacity E and charging demand D. init The calculation formula is:

[0164]

[0165] Where D is the battery's charging requirement, E is the current battery level, and T is the expected charging time;

[0166] S632, Optimal Output Power Calculation: Based on the predicted load demand and real-time monitored battery status, the output power is adjusted using a PID control algorithm. The calculation formula is as follows:

[0167]

[0168] Among them, P i (t) represents the output power of the i-th charging pile at time t. For the error, K p For proportional gain, K i For integral gain, K d This is the differential gain;

[0169] S633, Real-time adjustment of charging pile output power: Based on the output power calculated by the PID control algorithm, the output power of each charging pile is adjusted in real time to ensure that the real-time load demand is met. The calculation formula is as follows:

[0170]

[0171] in, The output power of the i-th charging pile at time t is calculated by the PID control algorithm, and α is the adjustment coefficient.

[0172] Through the above steps, the present invention can realize the dynamic adjustment of the output power of the charging pile, ensuring that the charging pile system provides efficient and reliable charging services under complex and ever-changing load demands and battery conditions.

[0173] The load between the balanced charging piles in S64 includes:

[0174] S641, real-time monitoring of the load status of charging piles: real-time monitoring of the load information of each charging pile, including the current output power and load status, and recording the load status of each charging pile to form historical load data for analysis and prediction.

[0175] S642, detecting overload: Analyze the load information monitored in real time. When the load of the charging pile is detected to exceed the preset threshold, the load distribution mechanism is triggered. The preset threshold is set according to the rated power and safe operation requirements of the charging pile.

[0176] S643, Automatic load distribution: After detecting an excessively high load, it automatically distributes the predetermined load to a charging station with a low load.

[0177] S644, Optimize load distribution using a load balancing algorithm: The load balancing algorithm optimizes the load distribution among charging stations. The calculation formula is as follows:

[0178]

[0179] in, For the load of the i-th charging pile after redistribution, The load before allocation is ΔP, the total load to be allocated is N, and the number of charging piles with lower load is N.

[0180] Through the above steps, the present invention can achieve load balancing among charging piles, avoid failure of a charging pile due to overload, and improve the overall efficiency and reliability of the charging pile system.

[0181] like Figure 2 As shown, a control system for a mobile charging pile, used to implement the aforementioned control method for a mobile charging pile, includes the following modules:

[0182] Charging pile positioning and user demand receiving module: acquires the real-time location information of the mobile charging pile, sends the location information to the charging pile control center, and receives the user's charging demand;

[0183] Path planning and charging pile mobility module: Based on the user's charging needs and the real-time location information of the charging pile, calculate the optimal driving route of the mobile charging pile and send the driving route to the charging pile control center. According to the optimal driving route, drive the charging pile to the user's designated location by controlling the mobile device.

[0184] Charging preparation module: After the mobile charging station arrives at the designated location, it performs charging preparation work and checks the status of the charging equipment;

[0185] Electromagnetic interference suppression module: processes the communication and control signals of the charging pile to suppress electromagnetic interference;

[0186] Charging and Completion Module: After confirming that the charging preparation work is completed, the module starts charging the electric vehicle and monitors the charging status in real time. After charging is completed, the power connection is disconnected and the user is notified of the completion of charging through the user terminal.

[0187] Dynamic load balancing module: Real-time monitoring and adjustment of the output power of charging piles, dynamic adjustment of charging power based on the status of electric vehicle batteries and charging demand, and balance of load among multiple charging piles;

[0188] Charging pile return and data recording module: After charging is completed, the mobile charging pile returns to the initial position or moves to the standby position according to the instructions of the charging pile control center, records the relevant data for each charging, and sends the relevant data to the charging pile control center for analysis.

[0189] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

[0190] This invention is intended to cover all such substitutions, modifications, and variations falling within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A control method for a mobile charging pile, characterized in that, Includes the following steps: S1, Charging Pile Positioning and User Demand Reception: Obtain the real-time location information of the mobile charging pile through GPS and send the location information to the charging pile control center. Receive the user's charging demand through the user terminal. The charging demand includes charging time, charging amount, and charging location. S2, Path planning and charging pile movement: Based on the user's charging needs and the real-time location information of the charging pile, the optimal driving route of the mobile charging pile is calculated using a path planning algorithm, and the driving route is sent to the charging pile control center. According to the optimal driving route, the charging pile is driven to the user's designated location by controlling the mobile device. S3, Charging Preparation: After the mobile charging station arrives at the designated location, it performs charging preparation work, including automatically connecting to the power supply and checking the status of the charging equipment. S4, Electromagnetic Interference Suppression: Adaptive filtering algorithms are used to process the communication and control signals of the charging pile to suppress electromagnetic interference; Specifically, it includes: S41, Electromagnetic Interference Detection: Real-time monitoring of the communication and control signals of the charging pile through an electromagnetic interference detection mechanism to obtain characteristic information of electromagnetic interference, including interference frequency and amplitude; S42, Adaptive Filter Initialization: The acquired electromagnetic interference characteristic information is input into the adaptive filter, and the adaptive filter dynamically adjusts the filtering parameters according to the input signal to suppress electromagnetic interference; S43, Filter weight update: The weights of the adaptive filter are updated using a recursive least squares algorithm; S44, Signal Processing and Application: Using the signal processed by the adaptive filter for communication and control of charging piles; The electromagnetic interference detection in S41 includes: S411, Electromagnetic Interference Detection Device Installation: An electromagnetic interference detection device is installed in the communication and control circuit of the charging pile. The electromagnetic interference detection device includes an electromagnetic interference sensor and a signal processing unit to monitor and process electromagnetic interference signals in real time. S412, Signal Processing Unit Analysis: The signal processing unit performs spectral analysis on the electromagnetic interference signal, extracts the characteristic information of the interference signal, and the calculation formula is as follows: ； in, For frequency domain signals, For time-domain signals, For frequency, For time; S413, Feature Information Transmission: The acquired electromagnetic interference feature information is transmitted to the adaptive filter for electromagnetic interference suppression processing. S5, Charging and Completion: After confirming that the charging preparation work is completed, the electric vehicle will start charging and the charging status will be monitored in real time. After charging is completed, the power connection will be disconnected and the user will be notified of the completion of charging through the user terminal. S6, Dynamic Load Balancing: Real-time monitoring and adjustment of the output power of charging piles, dynamically adjusting the charging power according to the status of electric vehicle batteries and charging needs, while balancing the load among multiple charging piles; S7, Charging Pile Return and Data Recording: After charging is completed, the mobile charging pile returns to its initial position or moves to a standby position according to the instructions of the charging pile control center. It records relevant data for each charging session, including charging time, charging amount, and charging cost, and sends the relevant data to the charging pile control center for analysis.

2. The control method for a mobile charging pile according to claim 1, characterized in that, The path planning and charging pile movement in S2 include: S21, Optimal route calculation: Combining user charging needs and real-time location information of charging piles, the optimal driving route is calculated using the Dijkstra algorithm. S22, Information transmission: The calculated optimal driving route is sent to the charging pile control center, and the charging pile control center receives the driving route information; S23, Charging Pile Movement: Based on the optimal driving route, the charging pile is driven to the user's designated location by controlling the mobile device. The mobile device adjusts the driving path in real time based on the received driving route information and sensor data.

3. The control method for a mobile charging pile according to claim 1, characterized in that, The recursive least squares algorithm includes: Gain vector calculation: ; in, It is in the The covariance matrix in the next iteration It is in the The input signal vector in the next iteration. It is a forgetting factor; Weight update: ; in, For the first The filter weights for the next iteration For the gain vector, This is the current error; Covariance matrix update: .

4. The control method for a mobile charging pile according to claim 1, characterized in that, The dynamic load balancing in S6 includes: S61, Battery Status Monitoring: Real-time monitoring of electric vehicle battery status information, including battery charge, battery health status, and charging requirements; S62, Load Forecasting: Based on battery status information and charging demand, the load forecasting model is used to predict the real-time load demand of the charging pile. S63, dynamically adjusts the output power of charging piles: based on predicted load demand and real-time monitored battery status, calculates the optimal output power of each charging pile and adjusts the output power of the charging piles in real time. S64, balancing the load among charging piles: By monitoring the load status of charging piles in real time, when the load of a charging pile is too high, the load is automatically distributed, and the load balancing algorithm is used to optimize the load distribution.

5. The control method for a mobile charging pile according to claim 4, characterized in that, The load prediction model in S62 employs a generative adversarial network (GAN) model, which includes: S621, Data Acquisition and Feature Extraction: Acquire electric vehicle battery status information and charging demand, and extract time series features, including battery charge change rate, historical charging records and environmental factors; S622, Initializing the Generator and Discriminator: Initializing the Generator and discriminator Weights and biases; S623, Conditional Input: Use the electric vehicle battery status and charging demand as conditional inputs. Combined with random noise Input generator; S624, Loss function definition: Discriminator loss function: ; in, It is the loss function of the discriminator. This is the actual load demand. It is a conditional input. It is the actual data distribution. The discriminator is under given conditions Below the actual load requirements The output, It is a random noise vector. It is the distribution of the noise vector. Is the generator in a given noise vector and conditions The generated load requirements The discriminator is under given conditions The following are the requirements for generating load. The output; Generator loss function: ; in, It is the loss function of the generator. It is the distribution of conditional inputs; S625, Adaptive Learning Rate Adjustment: The learning rate is dynamically adjusted based on changes in loss during training. The calculation formula is as follows: ; in, For the first The learning rate for the next iteration. To adjust the coefficient, The rate of change of the loss function; S626, Training the producer and discriminator: Repeat the training process until the loss function converges. The training process is as follows: Update the discriminator: ; Update generator: ; in, These are the parameters of the discriminator. It is the learning rate of the discriminator. It is the discriminator loss function For parameters gradient, These are the parameters of the generator. It is the learning rate of the generator. It is the generator loss function For parameters The gradient; S627, Load Prediction: Using a Trained Generator Receive condition input and random noise Generate predicted load demand .

6. The control method for a mobile charging pile according to claim 5, characterized in that, The dynamic adjustment of the charging pile output power in S63 includes: S631, Initial Output Power Calculation: Obtain the predicted load demand and real-time monitoring of battery status Based on the current battery level and charging needs Calculate the initial output power of each charging station. The calculation formula is: ； in, To meet the battery's charging needs, This is the current battery level. For the expected charging time; S632, Optimal Output Power Calculation: Based on the predicted load demand and real-time monitored battery status, the output power is adjusted using a PID control algorithm. The calculation formula is as follows: ； in, For the first Time of the first The output power of each charging station For error, For proportional gain, For integral gain, This is the differential gain; S633, Real-time adjustment of charging pile output power: Based on the output power calculated by the PID control algorithm, the output power of each charging pile is adjusted in real time. The calculation formula is as follows: ； in, The first calculation for the PID control algorithm Time of the first The output power of each charging station This is for adjusting the coefficient.

7. The control method for a mobile charging pile according to claim 6, characterized in that, The load between the balanced charging piles in S64 includes: S641, real-time monitoring of the load status of charging piles: real-time monitoring of the load information of each charging pile, including the current output power and load status, and recording the load status of each charging pile to form historical load data; S642, Detecting overload: Analyzes the load information monitored in real time, and triggers the load distribution mechanism when the load of the charging pile exceeds the preset threshold. S643, Automatic load distribution: After detecting an excessively high load, it automatically distributes the predetermined load to a charging station with a low load. S644, Optimize load distribution using a load balancing algorithm: The load balancing algorithm optimizes the load distribution among charging stations. The calculation formula is as follows: ； in, For the redistributed number The load of each charging station For the load before distribution, The total load to be allocated. The number of charging stations with low load.

8. A control system for a mobile charging pile, used to implement the control method for a mobile charging pile as described in any one of claims 1-7, characterized in that, Includes the following modules: Charging pile positioning and user demand receiving module: acquires the real-time location information of the mobile charging pile, sends the location information to the charging pile control center, and receives the user's charging demand; Path planning and charging pile mobility module: Based on the user's charging needs and the real-time location information of the charging pile, calculate the optimal driving route of the mobile charging pile and send the driving route to the charging pile control center. According to the optimal driving route, drive the charging pile to the user's designated location by controlling the mobile device. Charging preparation module: After the mobile charging station arrives at the designated location, it performs charging preparation work and checks the status of the charging equipment; Electromagnetic interference suppression module: processes the communication and control signals of the charging pile to suppress electromagnetic interference; Charging and Completion Module: After confirming that the charging preparation work is completed, the module starts charging the electric vehicle and monitors the charging status in real time. After charging is completed, the power connection is disconnected and the user is notified of the completion of charging through the user terminal. Dynamic load balancing module: Real-time monitoring and adjustment of the output power of charging piles, dynamic adjustment of charging power based on the status of electric vehicle batteries and charging demand, and balance of load among multiple charging piles; Charging pile return and data recording module: After charging is completed, the mobile charging pile returns to the initial position or moves to the standby position according to the instructions of the charging pile control center, records the relevant data for each charging, and sends the relevant data to the charging pile control center for analysis.

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