Light commercial electric vehicle photovoltaic centralized charging control method and device and medium

Abstract

The invention discloses a photovoltaic centralized charging control method and device for a light commercial electric vehicle and a medium, and belongs to the technical field of new energy charging. The method comprises the following steps: acquiring weather data, a vehicle battery charge state and task scheduling data; photovoltaic module parameters and environment data are processed through an LSTM neural network model, and a photovoltaic power generation power prediction curve is generated; constructing a multi-dimensional evaluation index system including task emergency degree, charging efficiency and power grid interaction factors, and generating a charging priority sequence in combination with an entropy weight TOPSIS algorithm; solving a real-time power distribution scheme based on a mixed integer programming algorithm, and dynamically adjusting the output power of the charging pile; monitoring the operation state of the system, and triggering a multi-stage power compensation mechanism when the photovoltaic power is insufficient or the electric quantity is low; and the battery state and the photovoltaic power are updated in real time, and a charging strategy is corrected through a rolling optimization algorithm. By means of the method, centralized charging control over the light commercial electric vehicle is achieved, and the technical effect of meeting the requirements of a motorcade is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of new energy charging, and in particular to a photovoltaic centralized charging control method, device and medium for light commercial electric vehicles. Background Art

[0002] In the field of new energy charging technology, centralized charging systems for light commercial electric vehicles have become critical infrastructure in scenarios such as logistics parks and bus stations. Existing technologies generally employ an architecture primarily powered by the grid, supplemented by fixed photovoltaic and energy storage devices, with power supply achieved through AC-DC conversion. In a typical solution, photovoltaic arrays and energy storage systems are configured in a fixed ratio, and a centralized inverter is used to convert DC power to AC power before supplying it to charging stations. Energy storage systems often use statically allocated capacity for lithium iron phosphate battery packs, and charging station power is allocated on a first-come, first-served basis, supplemented by a simple time-based electricity pricing strategy.

[0003] Although existing technologies have achieved the utilization of photovoltaic energy to a certain extent, they still have significant defects. First, energy utilization efficiency is limited by the AC-DC multi-stage conversion architecture, and the insufficient proportion of photovoltaic direct supply leads to low overall system efficiency. Second, the energy storage system adopts a rigid configuration mode and cannot dynamically respond to the spatiotemporal changes in the fleet's charging needs, resulting in redundant energy storage capacity. Finally, the existing charging scheduling strategy lacks multi-dimensional decision-making capabilities and only allocates power based on vehicle SOC or simple time rules, resulting in frequent charging interruptions or grid power replenishment delays in scenarios with fluctuating photovoltaic power generation.

[0004] Therefore, how to centrally control the charging of light commercial electric vehicles to meet the needs of the fleet has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The embodiments of the present application provide a method, device, and medium for controlling centralized photovoltaic charging of light commercial electric vehicles to solve the following technical problem: how to centrally control charging of light commercial electric vehicles to meet fleet needs.

[0006] In the first aspect, an embodiment of the present application provides a photovoltaic centralized charging control method for light commercial electric vehicles, which is applied to a photovoltaic charging system. The photovoltaic charging system is used to charge light commercial electric vehicles. The photovoltaic charging system includes a charging device, and the charging device includes multiple charging piles. The charging device is connected to a photovoltaic power generation device, an energy storage device, and an external power grid. The method is characterized in that the method includes: obtaining weather data and vehicle data of at least one light commercial electric vehicle; wherein the vehicle data includes a battery state of charge and a preset task schedule; processing weather data and data of the photovoltaic power generation device based on a preset photovoltaic power generation prediction algorithm to generate a photovoltaic power generation power prediction curve; calculating based on the battery state of charge, the preset task schedule, and the photovoltaic power generation power prediction curve A charging priority sequence for multiple light commercial electric vehicles; determining the real-time photovoltaic power generation power, and processing the charging priority sequence and the real-time photovoltaic power generation power through a preset dynamic power allocation algorithm to generate a real-time power allocation plan and expected results for multiple charging piles; controlling multiple charging piles to perform charging operations according to the real-time power allocation plan, and monitoring the operating status of the photovoltaic charging system. When it is detected that the photovoltaic power generation of the photovoltaic power generation device is insufficient and / or the energy storage capacity of the energy storage device is lower than the power threshold, supplementing power is provided based on the external power grid; updating the battery charge status and real-time photovoltaic power generation power of multiple light commercial electric vehicles in real time during the charging process. If the expected result exceeds the charging threshold time value, the charging priority sequence is recalculated and the real-time power allocation plan is updated.

[0007] In one implementation of the present application, weather data and data of photovoltaic power generation devices are processed based on a preset photovoltaic power generation prediction algorithm to generate a photovoltaic power generation power prediction curve, specifically including: obtaining photovoltaic module parameters and environmental monitoring data of the photovoltaic power generation device; wherein, photovoltaic module parameters include module efficiency attenuation curve and photovoltaic array tilt adjustment range, and environmental monitoring data include module surface temperature and ambient irradiance; performing time series analysis on weather data through a preset LSTM neural network model, and weather data includes cloud cover change data and historical seasonal light intensity distribution data for a certain period of time in the future; inputting photovoltaic module parameters into the LSTM neural network model to generate an initial photovoltaic power generation power prediction curve; based on the module surface temperature and ambient irradiance, the initial prediction curve is corrected by comparing with a preset temperature power reduction coefficient comparison table to generate a photovoltaic power generation power prediction curve.

[0008] In one implementation of the present application, a charging priority sequence of multiple light commercial electric vehicles is calculated based on the battery state of charge, a preset task schedule and a photovoltaic power generation power forecast curve, specifically including: constructing a multidimensional evaluation index system based on the battery state of charge and the preset task schedule; wherein the index system includes a task urgency factor, a charging efficiency factor and a grid interaction factor; processing the multidimensional evaluation index system based on a preset entropy weight TOPSIS algorithm to generate dynamic priority scores for multiple light commercial electric vehicles; performing spatiotemporal matching of the dynamic priority scores with the photovoltaic power generation power forecast curve to determine the photovoltaic priority charging period and the energy storage compensation charging period; generating a charging priority sequence based on the period matching results; wherein the charging priority sequence includes a period allocation table and a power fluctuation tolerance parameter for each charging pile.

[0009] In one implementation of the present application, a multidimensional evaluation index system is constructed based on the battery state of charge and the preset task schedule, specifically including: determining the available charging time based on the planned departure timestamp and current time in the preset task schedule, and deducting the preset safety margin to determine the actual available charging time; obtaining a preset task type weight table; wherein the task type weight table includes at least the emergency coefficients corresponding to cold chain transportation tasks, general freight tasks and spare vehicle tasks; processing the actual available charging time, the battery state of charge and the preset task type weight table based on a preset exponential decay model to generate a task urgency factor; calculating the maximum allowable charging current based on the battery pack temperature distribution data, and determining the charging efficiency factor based on the ratio of the actual output power of the charging pile to the rated power; obtaining the time-of-use electricity price signal and regional load rate of the external power grid to determine the power grid interaction factor; and constructing a multidimensional evaluation index system based on the task urgency factor, the charging efficiency factor and the power grid interaction factor.

[0010] In one implementation of the present application, the real-time photovoltaic power generation power is determined, and the charging priority sequence and the real-time photovoltaic power generation power are processed through a preset dynamic power allocation algorithm to generate a real-time power allocation plan and expected results for multiple charging piles, specifically including: real-time collection of voltage and current data of the photovoltaic power generation device, and determination of the real-time photovoltaic power generation power through a preset sliding average filtering algorithm; establishing a dynamic power allocation matrix; wherein the dynamic power allocation matrix includes the charging pile number, real-time photovoltaic power generation power, charging priority sequence and charging efficiency curve; solving the optimal solution of the dynamic power allocation matrix based on a preset mixed integer programming algorithm; wherein the objective function of the mixed integer programming algorithm is to minimize the total charging completion time, and the constraints include the maximum power limit of the charging pile and the battery safety charging current limit; generating a real-time power allocation plan based on the optimal solution, and calculating the expected results of multiple light commercial electric vehicles based on the real-time power allocation plan.

[0011] In one implementation of the present application, multiple charging piles are controlled to perform charging operations according to a real-time power allocation scheme, and the operating status of the photovoltaic charging system is monitored. When it is detected that the photovoltaic power generation of the photovoltaic power generation device is insufficient and / or the energy storage capacity of the energy storage device is lower than the power threshold, power is supplemented based on the external power grid, specifically including: real-time collection of output power data of the photovoltaic power generation device, and accumulating the power allocation values of all charging piles in the real-time power allocation scheme to determine the current total charging demand; calculating the difference between the photovoltaic power generation power and the total charging demand based on a preset power difference algorithm; when the difference continues to be negative for more than a first preset time, it is determined that the photovoltaic power generation is insufficient, and the energy storage device is called for supplementary power supply; periodically obtaining the remaining power of the energy storage device The percentage of remaining power is calculated and compared with the power threshold; when the remaining power percentage is lower than the power threshold, the grid power replenishment instruction is triggered to enter the power replenishment mode; the time-of-use electricity price data of the external power grid is obtained; the power allocation weight of the external power grid is calculated based on the time-of-use electricity price data, and the grid power replenishment power allocation plan is generated; among them, the grid power replenishment power allocation plan includes the grid power supply ratio and phase synchronization parameters of each charging pile; the charging pile is controlled to switch to the grid power supply mode, and power allocation is executed based on the grid power replenishment power allocation plan, and the grid load rate is monitored in real time to see if it exceeds the preset safety range; when it is detected that the photovoltaic power generation power returns to the total charging demand or the power of the energy storage device returns to the safety threshold, the grid power supply ratio is gradually reduced until the power replenishment mode is exited.

[0012] In one implementation of the present application, the battery state of charge and real-time photovoltaic power generation power of multiple light commercial electric vehicles are updated in real time during the charging process. If the expected result exceeds the charging threshold, the charging priority sequence is recalculated and the real-time power allocation plan is updated, specifically including: collecting incremental battery state of charge data and actual output power data of charging piles of multiple light commercial electric vehicles at preset intervals; inputting the incremental battery state of charge data into a preset charging efficiency evaluation model to generate an actual charging rate curve; comparing the actual charging rate curve with the expected result, and calculating the time integral difference between the two; when the time integral difference exceeds the charging threshold, reconstructing a multi-dimensional evaluation index system based on the latest collected battery state of charge and the revised photovoltaic power generation power prediction curve; reordering the dynamic priority scores based on a preset rolling optimization algorithm to generate an updated charging priority sequence; and updating the real-time power allocation plan according to the updated charging priority sequence and constraint conditions.

[0013] In one implementation of the present application, the method also includes: collecting historical operation data of a complete charging cycle to construct a charging efficiency evaluation data set; processing the charging efficiency evaluation data set through a preset feature extraction algorithm to extract a set of key performance indicators; constructing a random forest regression model based on the key performance indicator set to analyze the impact of parameters in the key performance indicator set on the photovoltaic charging system and generate charging strategy optimization recommendations.

[0014] In a second aspect, an embodiment of the present application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, any of the above-mentioned methods for controlling photovoltaic centralized charging of light commercial electric vehicles is implemented.

[0015] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, it implements any of the above-mentioned methods for controlling photovoltaic centralized charging of light commercial electric vehicles.

[0016] The embodiments of the present application provide a photovoltaic centralized charging control method, device, and medium for light commercial electric vehicles. Through the photovoltaic power generation prediction model of the LSTM neural network, combined with the dynamic correction mechanism of photovoltaic module parameters and environmental monitoring data, it effectively enhances the spatiotemporal accuracy of power generation prediction and provides a reliable basis for energy scheduling. By constructing a multi-dimensional evaluation index system including task urgency, charging efficiency, and grid interaction, and using the entropy-weighted TOPSIS algorithm to dynamically generate a charging priority sequence, it achieves a precise match between vehicle task scheduling, battery status, and photovoltaic power generation characteristics, improving the rationality of resource allocation and the charging guarantee capability of high-priority vehicles. The dynamic power allocation algorithm is optimized and solved through a mixed integer programming model. Under the premise of meeting the power constraints of the charging pile and battery safety, it shortens the total charging completion time and improves the utilization rate of charging facilities. The real-time monitoring and feedback mechanism dynamically adjusts the charging strategy through sliding average filtering, power difference calculation, and rolling optimization algorithm to ensure seamless switching to grid power replenishment mode when photovoltaic fluctuations or insufficient energy storage are encountered, thereby enhancing the stability of system operation and environmental adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A flow chart of a photovoltaic centralized charging control method for a light commercial electric vehicle provided in an embodiment of the present application;

[0019] Figure 2 A schematic diagram of the internal structure of a photovoltaic centralized charging control device for a light commercial electric vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] The embodiments of the present application provide a method, device, and medium for controlling centralized photovoltaic charging of light commercial electric vehicles to solve the following technical problem: how to centrally control charging of light commercial electric vehicles to meet fleet needs.

[0022] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0023] Figure 1 This is a flow chart of photovoltaic centralized charging control for light commercial electric vehicles provided in the embodiment of this application. Figure 1 As shown, the embodiment of the present application provides a method for controlling photovoltaic centralized charging of a light commercial electric vehicle, which specifically includes the following steps:

[0024] Step 1: Obtain weather data and vehicle data of at least one light commercial electric vehicle; wherein the vehicle data includes a battery state of charge and a preset task schedule.

[0025] Weather data: refers to real-time and predicted meteorological information obtained through meteorological service interfaces or environmental sensors deployed at charging stations, including parameters such as light intensity, cloud coverage, ambient temperature and humidity.

[0026] Vehicle data: refers to vehicle operation data obtained through on-board terminals (such as TBOX) or logistics management systems, including battery state of charge (SOC, i.e., the percentage of remaining power) and preset task schedules (such as planned departure time, task type, and priority tag).

[0027] In a specific case, a photovoltaic charging system deployed in a logistics park needs to provide charging services for 30 light commercial electric vehicles.

[0028] Call the third-party weather service API to obtain cloud cover change data (such as cloud cover fluctuations between 20% and 60%), ambient temperature (28°C-32°C), and light intensity (peak 800W / m 2 ).

[0029] The battery SOC of each vehicle is collected in real time through the on-board TBOX (e.g., the SOC of vehicle A is 40%, and that of vehicle B is 65%).

[0030] Synchronize the task scheduling data of the logistics management system and extract key parameters (such as vehicle C needs to perform the cold chain transportation task in 2 hours, and the priority is marked as "urgent").

[0031] Step 2: Process weather data and photovoltaic power generation device data based on a preset photovoltaic power generation prediction algorithm to generate a photovoltaic power generation power prediction curve.

[0032] Step 2.1, obtaining photovoltaic module parameters and environmental monitoring data of the photovoltaic power generation device; wherein the photovoltaic module parameters include the module efficiency attenuation curve and the photovoltaic array tilt adjustment range, and the environmental monitoring data include the module surface temperature and the ambient irradiance.

[0033] PV module parameters: The inherent property parameters of PV panels, including the module efficiency attenuation curve (describing the attenuation pattern of PV panel output power with age) and the PV array tilt adjustment range (the range of tilt angles allowed by the PV panel mechanical structure).

[0034] Environmental monitoring data: physical environmental parameters collected in real time, including component surface temperature (real-time temperature value of photovoltaic panel surface) and environmental irradiance (solar radiation intensity received per unit area).

[0035] In one example of this application:

[0036] A temperature sensor (such as an infrared temperature measurement module) and an irradiance sensor (such as a photodiode array) are installed in the photovoltaic array, and the sampling frequency is set to once per minute.

[0037] The module efficiency attenuation curve is read from the photovoltaic system monitoring terminal in the form of a year-attenuation percentage comparison table (e.g., 2% attenuation in the first year, 3.5% attenuation in the second year).

[0038] Obtain the tilt adjustment range parameters from the photovoltaic support control system (e.g., the mechanical structure supports tilt adjustment from 15° to 60°).

[0039] Step 2.2: Use the preset LSTM neural network model to perform time series analysis on weather data. The weather data includes cloud cover change data for a certain period of time in the future and historical seasonal light intensity distribution data.

[0040] LSTM neural network model: A deep learning model suitable for time series forecasting that can capture long-term dependencies.

[0041] Weather data: including cloud cover change data for the next period of time (usually set to 6 hours) (cloud cover forecast every 30 minutes) and historical seasonal light intensity distribution data (average hourly irradiance for the same period in the past three years).

[0042] In one example of this application:

[0043] Obtain cloud cover data for the next 6 hours from the meteorological service interface in the format of time-cloud cover percentage sequence (e.g., 12:00-30%, 12:30-50%).

[0044] Load the historical light intensity database and extract the typical irradiance curve of the current season (e.g. average irradiance 200W / m2 in winter and 600W / m2 in summer).

[0045] A pre-trained LSTM model is used with the input dimension being the time step (6 hours × 2 data points / hour) and the output dimension being the predicted power sequence.

[0046] Weather data is input into the model, which automatically analyzes the relationship between cloud cover mutation points and light intensity, and outputs the initial power generation power forecast value every 30 minutes for the next 6 hours.

[0047] Step 2.3: Input the photovoltaic module parameters into the LSTM neural network model to generate the initial photovoltaic power generation prediction curve.

[0048] Initial photovoltaic power generation prediction curve: The theoretical prediction value without correction for environmental factors, reflecting the power generation capacity under ideal operating conditions.

[0049] In one example of this application:

[0050] Convert the component efficiency attenuation coefficient into the attenuation percentage corresponding to the current year (e.g., in the third year of installation, the attenuation rate is 5%).

[0051] Normalize the real-time tilt angle value (such as 35°) to a value in the range of 0-1 (35 / 60≈0.58).

[0052] The encoded component parameters are combined with weather data to form a multi-dimensional input vector (e.g., cloud cover, historical irradiance, efficiency decay, and inclination angle).

[0053] The model outputs the initial prediction curve in the form of time-power key-value pairs (e.g., 12:00-50kW, 12:30-45kW).

[0054] Step 2.4: Based on the component surface temperature and the ambient irradiance, the initial prediction curve is corrected by comparing it with the preset temperature power reduction coefficient comparison table to generate a photovoltaic power generation prediction curve.

[0055] Temperature power reduction coefficient comparison table: A reference table that describes the output power loss of photovoltaic panels at different temperatures. For example, for every 1°C increase in temperature, the power decreases by 0.5%.

[0056] In one example of this application:

[0057] Query the discount rate corresponding to the current component surface temperature (such as 45°C): (45-25)×0.5%=10% (based on the standard temperature of 25°C).

[0058] Adjust the correction range according to the actual irradiance: if the actual irradiance is 20% lower than the predicted value, add a 5% compensation factor.

[0059] The initial predicted value is corrected point by point: for example, the initial predicted power at 12:00 is 50kW, which is 45kW after the temperature is reduced by 10%; after the irradiance is compensated by 5%, the final value is 47.25kW.

[0060] Output the corrected time-power series and mark the correction reason (such as "temperature correction -10%" or "irradiance compensation +5%").

[0061] In a specific example, the photovoltaic charging system of a logistics park needs to generate a next-day power generation forecast curve:

[0062] The efficiency decay curve shows a 3% decay rate in the second year, with the current tilt angle set to 40°.

[0063] The component surface temperature is 38°C and the ambient irradiance is 480W / m2.

[0064] The weather forecast shows that the cloud cover will increase from 20% to 60% from 10:00 to 14:00 the next day, and the historical average light intensity during the same period is 550W / m2.

[0065] The LSTM model outputs initial prediction values: 10:00-120kW, 12:00-100kW, 14:00-80kW.

[0066] Temperature correction: At 38°C, the power reduction rate is 6.5%.

[0067] Irradiance compensation: The actual irradiance is 12.7% lower than the historical average, so a 3% compensation factor is added.

[0068] Corrected power: original 100kW at 12:00 to 100×(1-6.5%+3%)=96.5kW.

[0069] Step 3: Calculate the charging priority sequence of multiple light commercial electric vehicles based on the battery state of charge, the preset task schedule, and the photovoltaic power generation power forecast curve.

[0070] Step 3.1: Construct a multi-dimensional evaluation index system based on the battery state of charge and the preset task schedule; wherein the index system includes a task urgency factor, a charging efficiency factor, and a grid interaction factor.

[0071] Step 3.1.1: Based on the planned departure timestamp in the preset task schedule and the current time, determine the available charging time, and deduct the preset safety margin to determine the actual available charging time.

[0072] Available charging time: the difference between the vehicle's planned departure time and the current time.

[0073] Safety margin: Buffer time (e.g. 30 minutes) reserved to prevent sudden delays.

[0074] In one example of this application:

[0075] Parse the planned departure time (e.g., 14:00) in the vehicle task schedule and obtain the current system time (e.g., 11:30).

[0076] Calculate the initial available time: 14:00-11:30=2.5 hours.

[0077] Deduct safety margin: 2.5 hours - 0.5 hours = 2 hours.

[0078] Step 3.1.2: Obtain a preset task type weight table; wherein the task type weight table at least includes emergency coefficients corresponding to cold chain transport tasks, general freight tasks, and spare vehicle tasks.

[0079] Task type weight table: defines the urgency of different task types. The higher the weight, the higher the charging priority.

[0080] In one example of this application:

[0081] Load a preset weight table:

[0082] Cold chain transportation task: weight 1.5 (need to maintain a low temperature environment)

[0083] General freight tasks: weight 1.0 (standard time requirement)

[0084] Backup vehicle mission: weight 0.7 (no urgent need)

[0085] Match the weight value according to the vehicle mission type (e.g., the weight of cold chain transportation mission is 1.5).

[0086] Step 3.1.3: Process the actual available charging time, battery state of charge, and preset task type weight table based on a preset exponential decay model to generate a task urgency factor.

[0087] Exponential decay model: A mathematical model in which urgency decreases exponentially over time.

[0088] In one example of this application:

[0089] Enter the actual available time (2 hours), current SOC (such as 30%), and task weight (1.5).

[0090] Calculate the urgency factor: urgency = weight × exponential decay function (available time) × (1-SOC / 100);

[0091] For example, when the attenuation coefficient k=0.2 and the SOC=30%, 1.5×e^(-0.2×2)×0.7≈0.94.

[0092] Step 3.1.4: Determine the maximum allowable charging current based on the battery data of multiple light commercial electric vehicles, and determine the charging efficiency factor based on the ratio of the actual output power of the charging pile to the rated power.

[0093] Maximum allowable charge current: The safe current limit determined by the battery temperature and state of health (SOH).

[0094] In one example of this application:

[0095] Get the battery temperature (e.g., 35°C) and SOH (e.g., 90%) from the BMS.

[0096] Check the safe charging curve provided by the battery manufacturer to determine the maximum allowable current (e.g. 100A).

[0097] Calculate the power utilization: actual output power (45kW) / rated power (50kW) = 0.9.

[0098] Comprehensive efficiency factor: 0.6×(actual current / maximum current)+0.4×power utilization

[0099] For example, the actual current 90A is 0.6×0.9+0.4×0.9=0.9.

[0100] Step 3.1.5: Obtain the time-of-use electricity price signal and regional load rate of the external power grid to determine the power grid interaction factor.

[0101] Time-of-use electricity price: electricity price strategy divided by time period (such as 1.2 yuan / kWh during peak hours and 0.5 yuan / kWh during off-peak hours).

[0102] In one example of this application:

[0103] Access the power grid system to obtain the current electricity price (such as 0.8 yuan / kWh in the flat period) and the regional load rate (such as 70%).

[0104] Calculate the interaction factor:

[0105] Basic cost = electricity price × estimated charging capacity (e.g. 50kWh to 0.8 × 50 = 40 yuan)

[0106] Interaction factor = 1 / (1 + cost coefficient) × environmental benefit bonus

[0107] For example, the load factor penalty value is 0.07, so the interaction factor = 1 / (1+40.07) = 0.024.

[0108] Step 3.1.6: Construct a multi-dimensional evaluation index system based on the task urgency factor, charging efficiency factor, and grid interaction factor.

[0109] In one example of this application:

[0110] Integrate each factor to generate an evaluation matrix:

[0111] Vehicle; Urgency factor; Efficiency factor; Interaction factor;

[0112] V001;0.94;0.9;0.024;

[0113] V002;0.41;0.85;0.015.

[0114] Step 3.2: Process the multidimensional evaluation index system based on the preset entropy weight TOPSIS algorithm to generate dynamic priority scores for multiple light commercial electric vehicles.

[0115] Entropy-weighted TOPSIS: A decision-making algorithm that objectively assigns weights through information entropy and combines it with ideal solution distance sorting.

[0116] In one example of this application:

[0117] Data normalization: Normalize the urgency, efficiency, and interaction factors to the range of [0,1].

[0118] Entropy weight calculation: Calculate the information entropy of each factor. The smaller the entropy value, the higher the discrimination and the greater the weight.

[0119] For example, weight distribution: urgency 0.5, efficiency 0.3, interaction 0.2.

[0120] TOPSIS ranking: Determine the positive ideal solution (0.94, 0.9, 0.024) and the negative ideal solution (0.41, 0.85, 0.015).

[0121] Calculate the Euclidean distance between each vehicle and the positive / negative ideal solution and generate a priority score:

[0122] Score = negative ideal solution distance / positive ideal solution distance + negative ideal solution distance;

[0123] For example, V001 scores 0.82 (high priority) and V002 scores 0.65 (medium priority).

[0124] Step 3.3: Match the dynamic priority score with the photovoltaic power generation power forecast curve in time and space to determine the photovoltaic priority charging period and the energy storage compensation charging period.

[0125] Spatiotemporal matching: Dynamically aligning vehicle charging demand with the spatiotemporal distribution characteristics of photovoltaic power generation.

[0126] In one example of this application:

[0127] PV power segmentation: discretize the prediction curve into 30-minute intervals (e.g. 50kW is predicted from 12:00 to 12:30).

[0128] Create matching rules:

[0129] High-priority vehicles are allocated to the period with sufficient photovoltaic power (such as 12:00-13:30).

[0130] Medium and low priority vehicles are assigned to the photovoltaic attenuation period or the energy storage power supply period.

[0131] Capacity verification: total demand during the period ≤ PV forecast value × 110% (allowing 10% fluctuation redundancy).

[0132] For example, the total demand from 12:00 to 12:30 is 45kW≤50kW×110%=55kW, and the matching is successful.

[0133] Step 3.4: Generate a charging priority sequence based on the time period matching result; wherein the charging priority sequence includes a time period allocation table and power fluctuation tolerance parameters for each charging pile.

[0134] In one example of this application:

[0135] Generate a scheduling matrix:

[0136] Charging pile; time period; vehicle; power; tolerance;

[0137] P01;12:00-13:30;V001;50kW;±10%;

[0138] P02; 13:30-14:30; V002; 30kW; ±15%.

[0139] In a specific example, a logistics park needs to dispatch charging for two light electric vehicles:

[0140] V001: Cold chain transport mission, SOC=30%, departure time 14:00;

[0141] V002: General cargo mission, SOC=45%, departure time 16:30;

[0142] Photovoltaic prediction curve: 12:00-13:00 power 60kW, 13:00-14:00 power 40kW;

[0143] V001 urgency factor = 0.94, efficiency factor = 0.9, interaction factor = 0.024;

[0144] V002 urgency factor = 0.41, efficiency factor = 0.85, interaction factor = 0.015;

[0145] Entropy weighted TOPSIS scores V001 = 0.82, V002 = 0.65;

[0146] V001 is allocated to 12:00-13:30 (PV 60kW to 40kW);

[0147] V002 is allocated to 13:30-14:30 (PV 40kW + energy storage 10kW);

[0148] Generate scheduling instructions, V001 charges at 50kW±10% at P01, and V002 charges at 30kW±15% at P02.

[0149] Step 4: Determine the real-time photovoltaic power generation power, and process the charging priority sequence and the real-time photovoltaic power generation power through a preset dynamic power allocation algorithm to generate a real-time power allocation plan and expected results for multiple charging piles.

[0150] Step 4.1: Collect voltage and current data of the photovoltaic power generation device in real time, and determine the real-time photovoltaic power generation power through a preset sliding average filtering algorithm.

[0151] Sliding average filtering algorithm: A data processing method that eliminates instantaneous noise by calculating the average value of data in a continuous time window.

[0152] In one example of this application:

[0153] Voltage and current sensors are deployed on the PV DC bus to collect raw data once per second (for example: voltage 600V, current 80A).

[0154] The data format is a timestamp-voltage-current triplet (e.g. 12:00:00, 600V, 80A).

[0155] Set a sliding window of 5 minutes (300 data points) and calculate the rolling average voltage and current:

[0156] Average voltage = the arithmetic mean of the latest 300 voltage data;

[0157] Average current = the arithmetic mean of the latest 300 current data;

[0158] Real-time power = average voltage × average current × efficiency coefficient (taken as 0.95);

[0159] For example, if the average voltage is 600 V and the average current is 80 A, the real-time power = 600 × 80 × 0.95 = 45.6 kW.

[0160] Step 4.2: Establish a dynamic power allocation matrix; wherein the dynamic power allocation matrix includes the charging pile number, real-time photovoltaic power generation power, charging priority sequence and charging efficiency curve.

[0161] Dynamic power allocation matrix: A multi-dimensional data structure that integrates charging station status, available power, vehicle priority, and charging efficiency.

[0162] In one example of this application, the matrix dimensions are defined as:

[0163] Row dimension: charging pile number (e.g. P01, P02);

[0164] Column dimension: real-time photovoltaic power generation (e.g. 45.6kW);

[0165] Charging priority sequence (e.g. V001 priority 0.9, V002 priority 0.7);

[0166] Charging efficiency curve (dynamic efficiency table based on SOC and temperature, for example, efficiency is 90% when SOC = 30%);

[0167] Data filling: Load the charging pile rated power (such as P01 rated power 50kW) and vehicle battery parameters (such as V001 maximum allowable current 100A) from the database.

[0168] For example: charging pile; available power; priority sequence; efficiency curve;

[0169] P01; 45.6kW; V001 (0.9); SOC-efficiency mapping table.

[0170] Step 4.3: Solve the optimal solution of the dynamic power allocation matrix based on a preset mixed integer programming algorithm; wherein the objective function of the mixed integer programming algorithm is to minimize the total charging completion time, and the constraints include the maximum power limit of the charging pile and the battery safe charging current limit.

[0171] Mixed integer programming: A mathematical optimization method that handles both continuous variables (power values) and discrete variables (charging station allocation).

[0172] In one example of this application:

[0173] Objective function setting: Minimize the maximum value of the charging completion time of all vehicles (i.e. the shortest total time).

[0174] Constraint definition: Charging pile power limit: The output power of each charging pile shall not exceed its rated power (e.g. P01 ≤ 50kW).

[0175] Battery safety current limit: The vehicle charging current must not exceed its maximum allowable value (e.g. V001 ≤ 100A).

[0176] Power distribution continuity: Charging tasks for the same charging pile must be allocated in order of priority.

[0177] The branch and bound method is used to traverse the feasible solution space and select the power allocation scheme that meets all constraints.

[0178] For example, solution: P01 allocates 45kW to V001, and P02 allocates 30kW to V002, with a total completion time of 2 hours.

[0179] Step 4.4: Generate a real-time power allocation plan based on the optimal solution, and calculate expected results of multiple light commercial electric vehicles based on the real-time power allocation plan.

[0180] In one example of this application:

[0181] Convert the optimal solution into executable charging instructions, such as:

[0182] P01 charges V001 at 45kW from 12:00 to 14:00;

[0183] P02 charges V002 at 30kW from 13:00 to 14:30;

[0184] Charging completion time prediction: V001 required power = 70kWh (from 30% to 90%), time = 70 / 45 ≈ 1.56 hours;

[0185] System energy efficiency index: Photovoltaic utilization rate = actual power used / available power = 45 / 45.6 ≈ 98.7%;

[0186] In a specific example, a logistics park needs to dynamically allocate charging power for two light electric vehicles:

[0187] Real-time photovoltaic power: 45.6kW (after filtering)

[0188] Charging priority sequence: V001(0.9)V002(0.7)

[0189] V001: SOC = 30% to 90%, battery capacity 100kWh, maximum current 100A;

[0190] V002: SOC = 45% to 80%, battery capacity 80kWh, maximum current 80A;

[0191] The photovoltaic power was calculated to be 45.6kW by sliding average filtering;

[0192] Create a dynamic matrix including P01 (45.6kW, V001) and P02 (45.6kW, V002);

[0193] The optimal allocation solution is solved by mixed integer programming: P01 allocates 45kW to V001, and P02 allocates 30kW to V002; the charging time of V001 is ≈1.33 hours, and that of V002 is ≈0.93 hours, for a total charging time of 1.33 hours.

[0194] Step 5: Control multiple charging piles to perform charging operations according to the real-time power allocation plan, and monitor the operating status of the photovoltaic charging system. When it is detected that the photovoltaic power generation device is insufficient and / or the energy storage device is lower than the power threshold, supplementary power is provided based on the external power grid.

[0195] Step 5: Collect the output power data of the photovoltaic power generation device in real time, and determine the current total charging demand by accumulating the power allocation values of all charging piles in the real-time power allocation plan.

[0196] Step 5.1: Calculate the difference between the photovoltaic power generation power and the total charging demand based on a preset power difference algorithm.

[0197] Total charging demand: The total power consumption of all charging piles executing the power allocation plan.

[0198] In one example of this application:

[0199] The power distribution value of each charging pile (such as P01 = 42kW, P02 = 3.2kW) is obtained in real time through the charging pile controller.

[0200] The data format is [timestamp, charging pile ID, power value (kW)] and is updated every 5 seconds.

[0201] Add up the allocated power values of all charging piles: total demand = 42 + 3.2 = 45.2kW.

[0202] If the charging pile is detected to be offline or the power fluctuates abnormally (such as ±20% mutation), an alarm is triggered and the abnormal data is eliminated.

[0203] Step 5.2: When the difference value continues to be negative for more than a first preset time, it is determined that the photovoltaic power generation is insufficient, and the energy storage device is called to supplement the power supply.

[0204] Power difference algorithm: Real-time comparison of the difference between photovoltaic power generation and total charging demand.

[0205] In one example of this application:

[0206] The difference ΔP = photovoltaic power (45.2 kW) - total demand (45.2 kW) = 0 kW is calculated every 10 seconds.

[0207] If ΔP is continuously < 0 and lasts for ≥ 5 minutes (e.g., PV power drops to 40kW, total demand is 45.2kW, ΔP = -5.2kW), it is determined that PV power generation is insufficient.

[0208] Send a command to the energy storage controller to supplement the missing power according to the absolute value of the difference (5.2kW).

[0209] For example: the energy storage battery discharges at a power of 5kW, and the remaining 0.2kW gap is automatically adjusted by the system to distribute the power to the charging pile.

[0210] Step 5.3: Periodically obtain the remaining power percentage of the energy storage device and compare it with the power threshold.

[0211] Capacity threshold: The minimum remaining capacity percentage allowed for discharge of the energy storage device (e.g., 30%).

[0212] In one example of the present application: the remaining amount of energy storage (such as the current amount of 35%) is read through the BMS (battery management system) every 5 minutes.

[0213] Compared with the preset threshold: if the remaining power is ≤30%, the grid power replenishment preparation instruction is triggered.

[0214] When the power level approaches a threshold (such as 31%), the energy storage discharge power is reduced to a safe level (such as 3kW) in advance.

[0215] Step 5.4: When the remaining power percentage is lower than the power threshold, the grid power replenishment instruction is triggered to enter the power replenishment mode.

[0216] Power supply mode: The system automatically switches to the operating state of external power grid power supply.

[0217] In one example of this application:

[0218] Command triggering conditions: PV power is insufficient and energy storage capacity is ≤30%.

[0219] For example, when the energy storage capacity drops to 28%, a request for additional power is immediately sent to the grid dispatching system.

[0220] Disconnect the energy storage power supply link and synchronize the charging pile communication protocol (such as ModbusTCP) to the grid interface.

[0221] Step 5.5: Obtain the time-of-use electricity price data of the external power grid.

[0222] Time-of-use electricity price: differentiated electricity price standards set by power companies according to different time periods.

[0223] In one example of this application:

[0224] Obtain 24-hour electricity price data through the power company API (e.g., 1.2 yuan / kWh during peak hours and 0.6 yuan / kWh during off-peak hours).

[0225] The data is stored in the format of [time period, electricity price] (e.g. 13:00-15:00, 1.0 yuan / kWh).

[0226] Mark the current time period type (e.g. 14:30 is a peak period), calculate the electricity price trend for the next 2 hours, and give priority to replenishing electricity during low-price periods.

[0227] Step 5.6: Calculate the power allocation weight of the external power grid based on the time-of-use electricity price data and generate a power allocation plan for the power grid supplement; wherein the power allocation plan for the power grid supplement includes the power supply ratio and phase synchronization parameters of each charging pile.

[0228] Power supply ratio: The ratio of the power supplied by the grid to the total demand for each charging station.

[0229] In one example of this application:

[0230] Weight calculation: Set priority based on electricity prices: assign a weight of 0.9 to off-peak hours and 0.3 to peak hours.

[0231] For example, during peak hours, the grid can only handle 30% of the required power.

[0232] Solution generation: Allocated grid power = total demand × weight = 45.2 × 0.3 ≈ 13.6 kW.

[0233] Allocation by charging pile priority: V001 (high priority) is allocated 10kW, and V002 is allocated 3.6kW.

[0234] Step 5.7: Control the charging pile to switch to the grid power supply mode, perform power allocation based on the grid power supply power allocation plan, and monitor in real time whether the grid load rate exceeds the preset safety range.

[0235] Phase synchronization parameters: The current phase adjustment parameters of each charging pile when the grid is supplying power to avoid three-phase imbalance.

[0236] In one example of this application:

[0237] Control the charging pile relay to switch to the grid power supply line and set the initial power to the allocated value (such as P01 = 10kW).

[0238] The three-phase current phase angle is synchronized (e.g., phase A 0°, phase B 120°, phase C 240°), with the deviation controlled within ±5°. The grid-side current (e.g., 50A) and voltage (220V ±5%) are detected in real time, and the load factor is calculated as actual load / line capacity. If the load factor is ≥85% (e.g., line capacity 60A, actual current 55A), the power of low-priority charging piles is automatically reduced.

[0239] Step 5.8: When it is detected that the photovoltaic power generation power has recovered to the total charging demand or the energy storage device power has returned to the safety threshold, the proportion of grid power supply is gradually reduced until the power replenishment mode is exited.

[0240] Safety threshold: The critical value of energy storage power at which grid replenishment is allowed to be stopped (such as 40%).

[0241] In one example of this application:

[0242] Exit condition detection:

[0243] Periodically (every 2 minutes) check whether the photovoltaic power is restored (such as ΔP ≥ 0) or the energy storage capacity is ≥ 40%.

[0244] If the PV power recovers to 45kW:

[0245] Phase 1: The grid power supply ratio is reduced to 50% (6.8kW), and photovoltaic power generation is responsible for 38.4kW.

[0246] Phase 2 (after 10 minutes): Completely cut off the grid power supply and switch back to the photovoltaic direct supply mode.

[0247] In a specific example, in a logistics park scenario, smart power replenishment operations are performed in rainy weather:

[0248] Photovoltaic power: Continuous fluctuations on rainy days (35kW to 28kW to 32kW).

[0249] Total charging demand: 40kW (P01=30kW, P02=10kW).

[0250] Energy storage initial capacity: 45%, capacity threshold is set to 30%.

[0251] Time-of-use electricity price data: The peak electricity price in the current period (14:00) is 1.1 yuan / kWh, and after 16:00 it enters a trough of 0.7 yuan / kWh.

[0252] The total demand is calculated in real time as 40kW, the photovoltaic power is 32kW, and ΔP = -8kW. ΔP remains negative for 10 minutes, triggering the energy storage to supplement the 8kW shortfall. The energy storage capacity decreases to 32% at a rate of 8kW. Five minutes later, the remaining energy storage capacity is detected at 29% (less than 30%), triggering the grid replenishment command. Electricity price data is obtained, marking the current period as peak, and setting the grid weight to 0.4. A replenishment plan is generated: the grid bears 40×0.4=16kW, allocating P01=12kW and P02=4kW. The system switches to grid power supply, monitoring the load rate to stabilize at 78% (less than the 85% safety threshold). Two hours later, the photovoltaic capacity recovers to 42kW (ΔP=+2kW), gradually reducing the grid power supply ratio to 0.

[0253] Step 6: During the charging process, the battery state of charge and real-time photovoltaic power generation of multiple light commercial electric vehicles are updated in real time. If the expected result exceeds the charging threshold, the charging priority sequence is recalculated and the real-time power allocation plan is updated.

[0254] Step 6.1: Collect incremental state of charge data of multiple light commercial electric vehicles and actual output power data of charging piles at preset intervals.

[0255] Preset cycle: A fixed time interval set by the system (such as 5 minutes) for synchronously updating data.

[0256] In one example of this application:

[0257] The battery SOC increment data (such as V001 SOC from 30% to 35%) is obtained every 5 minutes through the vehicle BMS (battery management system).

[0258] Read the actual output power from the charging pile controller (e.g. P01 nominally allocates 30kW, actual output 28.5kW).

[0259] Data format: [vehicle ID, SOC change value (%), charging pile ID, actual power (kW)].

[0260] If an abnormal SOC increment (such as a ±10% mutation) is detected for three consecutive cycles, the manual review process is triggered.

[0261] For example: If V002 SOC suddenly increases from 45% to 60%, the system will suspend its charging and send a fault alarm.

[0262] Step 6.2: Input the battery state of charge increment data into a preset charging efficiency evaluation model to generate an actual charging rate curve.

[0263] Charging efficiency evaluation model: A charging rate prediction algorithm based on historical data, including a temperature compensation factor.

[0264] In one example of this application:

[0265] Model input processing: Convert SOC increment data into charging efficiency value:

[0266] Actual efficiency = SOC increment corresponding power / charging pile output power × 100%;

[0267] For example: V001 receives 28.5kW×5 minutes=2.375kWh, and the SOC increases by 5% (corresponding to 5kWh), then the efficiency = 5 / 2.375≈210% (abnormal values need to be corrected).

[0268] Curve generation: Fit a curve based on 6 consecutive cycles of data and mark the temperature effect (e.g., efficiency 92% at 25°C, 88% at 35°C).

[0269] Dynamic correction coefficient: When the ambient temperature changes by ≥5°C, the efficiency prediction value is automatically adjusted.

[0270] Step 6.3: Compare the actual charging rate curve with the expected result and calculate the time integral difference between the two.

[0271] Time integral difference: the cumulative deviation between the actual charging progress and the expected plan in the time dimension.

[0272] In one example of this application:

[0273] Difference calculation:

[0274] The area difference between the planned SOC curve and the actual curve for each vehicle is calculated (eg, graphical integration).

[0275] For example: if you plan to charge to 80% SOC in 2 hours, but actually only charge to 75% in 2 hours, the time integral difference = 5% × 2h = 10%·h.

[0276] Threshold determination:

[0277] The preset charging threshold time is 15%·h. If the V001 difference reaches 10%·h, it is marked as a yellow warning; exceeding 15%·h triggers a plan update.

[0278] Step 6.4: When the time integral difference exceeds the charging threshold value, a multi-dimensional evaluation index system is reconstructed based on the latest collected battery state of charge and the revised photovoltaic power generation power prediction curve.

[0279] Multi-dimensional evaluation index system: a set of decision parameters including vehicle urgency, charging cost, and system energy efficiency.

[0280] In one example of this application:

[0281] Indicator expansion: Added real-time electricity price impact factors (such as peak period weight +0.2).

[0282] Added the change rate of vehicle departure time (originally planned to leave at 14:00, but actually delayed to 14:30, increasing the urgency).

[0283] Dynamic weight adjustment: When the predicted photovoltaic power is reduced by 10%, the system energy efficiency weight increases from 0.6 to 0.8.

[0284] For example, the indicator system:

[0285] Indicators: original weight; revised weight;

[0286] Task urgency: 0.7; 0.8;

[0287] Charging cost: 0.5; 0.4;

[0288] Photovoltaic utilization rate: 0.6; 0.7.

[0289] Step 6.5: Reorder the dynamic priority scores based on a preset rolling optimization algorithm to generate an updated charging priority sequence.

[0290] Rolling optimization algorithm: an iterative optimization method that dynamically adjusts priorities based on the latest data.

[0291] In one example of this application:

[0292] Scoring model: Priority score = task urgency × 0.8 + charging cost × 0.4 + photovoltaic utilization rate × 0.7.

[0293] For example: Original score of V001 = 0.9×0.8+0.6×0.4+0.7×0.7=1.69; after correction = 0.95×0.8+0.5×0.4+0.8×0.7=1.86.

[0294] Arranged in descending order by new scores: V003 (2.01) > V001 (1.86) > V002 (1.55).

[0295] Generate a new sequence: V003(0.95), V001(0.90), V002(0.75).

[0296] Step 6.6: Update the real-time power allocation plan according to the updated charging priority sequence and constraints.

[0297] Constraint loading:

[0298] Maximum power limit of the charging pile (e.g. P01≤50kW).

[0299] Minimum vehicle charging current requirement (e.g. V003 ≥ 20A).

[0300] According to the new priority allocation: V003 is allocated 45kW (P01+P02 combined power supply), V001 is adjusted to 30kW (P03), and V002 suspends charging.

[0301] Generate control instructions:

[0302] P01: Switch to V003, power = 30kW;

[0303] P02: Switch to V003, power = 15kW;

[0304] P03: allocate V001, power = 30kW;

[0305] In a specific example, during a peak charging period at noon in a logistics center, the system dynamically adjusted its strategy to cope with the emergency:

[0306] Initial conditions:

[0307] Vehicle: V001 (SOC 40% to 80%), V002 (50% to 85%), V003 (emergency mission, 30% to 95%)

[0308] Photovoltaic power: midday fluctuations (50kW to 42kW to 47kW);

[0309] Original priority sequence: V001 (0.9) > V002 (0.7) > V003 (0.6);

[0310] Data collected in the first cycle: V001's actual charging power was 28kW (expected 30kW), and the SOC only increased by 3%.

[0311] The cumulative difference in the third cycle reached 18%·h, exceeding the threshold of 15%·h.

[0312] Restructured the indicator system: the weight of urgent tasks increased from 0.7 to 0.9, and the V003 priority score jumped.

[0313] New sequence: V003(0.95)>V001(0.85)>V002(0.70).

[0314] Update plan: Allocate 45kW of P01+P02 to V003, and V001 will be supplied with 30kW by P03 instead.

[0315] This application also includes the following methods:

[0316] A1. Collect historical operation data of a complete charging cycle to construct a charging performance evaluation dataset.

[0317] Full charging cycle: The entire process from the start of vehicle charging to reaching the target SOC, including environmental parameters and device status records.

[0318] In one example of this application:

[0319] Data collection scope:

[0320] Time span: Collect the PV charging system operation data for the past 12 months.

[0321] Data Type:

[0322] Environmental parameters: light intensity (W / m2), ambient temperature (°C), photovoltaic panel surface contamination (0-1 coefficient).

[0323] Equipment status: charging pile output power deviation rate (actual / nominal value), energy storage battery health (SOH).

[0324] Vehicle data: battery SOC change curve, number of charging interruptions, BMS alarm records.

[0325] Eliminate abnormal charging segments with a duration of less than 10 minutes (such as incorrect insertion and removal of the charging gun).

[0326] Correct sensor drift data (such as temperature data ±3℃ correction).

[0327] [Record ID, timestamp, light intensity, temperature, charging pile power, SOC start value, SOC end value, charging time];

[0328] For example entry: C20241105-0853, 2023-11-0508: 53, 850, 25, 45.2kW, 30%, 80%, 2.3h.

[0329] A2. Process the charging efficiency evaluation dataset using a preset feature extraction algorithm to extract a set of key performance indicators.

[0330] Feature extraction algorithm: Screen parameter combinations that are strongly correlated with charging efficiency based on the Pearson correlation coefficient.

[0331] In one example of this application, the core indicators are:

[0332] PV fluctuation rate = (maximum power - minimum power) / average power × 100% (for example: single-day fluctuation rate is 28%).

[0333] Charging efficiency decay slope = (initial efficiency - final efficiency) / charging time (e.g. -1.2% / h).

[0334] Derivative indicator: Temperature-efficiency coupling coefficient: the change in charging efficiency caused by each 1°C increase in temperature (for example: -0.5% / °C).

[0335] Feature dimensionality reduction: Principal component analysis (PCA) was used to reduce the 32 original parameters to 5 principal components with a cumulative contribution rate ≥ 85%.

[0336] For example, the output is: principal component 1 (photovoltaic stability, weight 0.52), principal component 2 (battery aging effect, weight 0.23).

[0337] A3. Build a random forest regression model based on the key performance indicator set to analyze the impact of the parameters in the key performance indicator set on the photovoltaic charging system and generate charging strategy optimization suggestions.

[0338] Random forest regression model: A machine learning model composed of multiple decision trees, used to analyze multi-parameter nonlinear relationships.

[0339] In one example of this application:

[0340] Dataset division: 70% training set (about 10,000 records), 20% validation set, and 10% test set.

[0341] Number of decision trees = 200, maximum depth = 8, minimum number of leaf samples = 50.

[0342] Example of feature importance output:

[0343] Features: Importance;

[0344] PV volatility: 0.38;

[0345] Ambient temperature: 0.22;

[0346] Battery initial SOC: 0.18.

[0347] Strategy Recommendation Generation:

[0348] Dynamic priority adjustment: When the PV fluctuation rate is greater than 25%, it is recommended to increase the priority of high SOC vehicles by 0.2 levels.

[0349] Energy storage scheduling optimization: When the temperature is greater than 35°C, it is recommended to reduce the upper limit of energy storage discharge power to 80% of the rated value.

[0350] Charging pile collaboration strategy: Identify charging piles with a power deviation rate greater than 15% and recommend reducing their allocation weight by 50%.

[0351] In a specific example, during a project to optimize the efficiency of charging stations in a logistics park, the system implemented the following improvements:

[0352] Historical data set: Contains 8,760 hours of operating data (January 1, 2024 - December 31, 2024)

[0353] Prominent issues: Summer charging efficiency decreased by 12% year-on-year, and photovoltaic utilization rate fluctuated by ±35%.

[0354] Execution process:

[0355] Data from the high temperature period from June to August was collected, and 1,253 charging interruption events due to temperatures greater than 40°C were identified.

[0356] Construct a data set: including 18-dimensional parameters such as temperature, photovoltaic power, and charging efficiency.

[0357] Extract key indicators:

[0358] It was found that the average charging efficiency decreased by 4.7% for every 10°C increase in temperature.

[0359] The PV fluctuation rate at noon (11:00-13:00) reached a peak of 42%.

[0360] Model output suggestions:

[0361] When the temperature is greater than 35°C, the charging power is dynamically reduced by 10%-15% to maintain efficiency.

[0362] During the peak period of photovoltaic fluctuation, the energy storage buffer power is set to 30% of the maximum fluctuation value.

[0363] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a light commercial electric vehicle photovoltaic centralized charging control device, the structure of which is as follows: Figure 2 shown.

[0364] Figure 2 This is a schematic diagram of the internal structure of a photovoltaic centralized charging control device for light commercial electric vehicles provided in an embodiment of the present application. Figure 2 As shown, the equipment includes:

[0365] at least one processor 201;

[0366] and, a memory 202 communicatively coupled to the at least one processor;

[0367] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to:

[0368] Acquiring weather data and vehicle data of at least one light commercial electric vehicle, the vehicle data including battery state of charge, vehicle location information, preset task schedule, and estimated departure time; processing the weather data and data of the photovoltaic power generation device based on a preset photovoltaic power generation prediction algorithm to generate a photovoltaic power generation prediction curve; calculating a charging priority sequence for multiple light commercial electric vehicles based on the battery state of charge, preset task schedule, estimated departure time, and photovoltaic power generation prediction curve; determining real-time photovoltaic power generation, and processing the charging priority sequence and real-time photovoltaic power generation using a preset dynamic power allocation algorithm to generate a real-time power allocation plan and expected results for multiple charging piles; controlling the multiple charging piles to perform charging operations according to the power allocation plan, and monitoring the operating status of the photovoltaic charging system; and providing additional power based on an external power grid when it is detected that the photovoltaic power generation of the photovoltaic power generation device is insufficient and / or the energy storage capacity of the energy storage device is below a power threshold; updating the battery state of charge and real-time photovoltaic power generation of the multiple light commercial electric vehicles in real time during the charging process, and recalculating the charging priority sequence and updating the power allocation plan if the expected result exceeds the charging threshold.

[0369] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for photovoltaic centralized charging control of a light commercial electric vehicle stores computer executable instructions, wherein the computer executable instructions are set to:

[0370] Acquiring weather data and vehicle data of at least one light commercial electric vehicle, the vehicle data including battery state of charge, vehicle location information, preset task schedule, and estimated departure time; processing the weather data and data of the photovoltaic power generation device based on a preset photovoltaic power generation prediction algorithm to generate a photovoltaic power generation prediction curve; calculating a charging priority sequence for multiple light commercial electric vehicles based on the battery state of charge, preset task schedule, estimated departure time, and photovoltaic power generation prediction curve; determining real-time photovoltaic power generation, and processing the charging priority sequence and real-time photovoltaic power generation using a preset dynamic power allocation algorithm to generate a real-time power allocation plan and expected results for multiple charging piles; controlling the multiple charging piles to perform charging operations according to the power allocation plan, and monitoring the operating status of the photovoltaic charging system; and providing additional power based on an external power grid when it is detected that the photovoltaic power generation of the photovoltaic power generation device is insufficient and / or the energy storage capacity of the energy storage device is below a power threshold; updating the battery state of charge and real-time photovoltaic power generation of the multiple light commercial electric vehicles in real time during the charging process, and recalculating the charging priority sequence and updating the power allocation plan if the expected result exceeds the charging threshold.

[0371] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0372] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0373] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0374] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0375] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0376] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0377] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0378] Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0379] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0380] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0381] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A photovoltaic centralized charging control method for light commercial electric vehicles, applied to a photovoltaic charging system, wherein the photovoltaic charging system is used to charge light commercial electric vehicles, the photovoltaic charging system includes a charging device, the charging device includes multiple charging piles, and the charging device is connected to a photovoltaic power generation device, an energy storage device, and an external power grid, characterized in that: The method comprises: Obtaining weather data and vehicle data of at least one of the light commercial electric vehicles; wherein the vehicle data includes a battery state of charge and a preset mission schedule; Processing the weather data and the data of the photovoltaic power generation device based on a preset photovoltaic power generation prediction algorithm to generate a photovoltaic power generation power prediction curve; Calculating a charging priority sequence for a plurality of said light commercial electric vehicles based on said battery state of charge, a preset task schedule and a photovoltaic power generation power prediction curve; Determine the real-time photovoltaic power generation power, and process the charging priority sequence and the real-time photovoltaic power generation power through a preset dynamic power allocation algorithm to generate a real-time power allocation plan and expected results for the plurality of charging piles; Controlling the plurality of charging piles to perform charging operations according to the real-time power allocation scheme, and monitoring the operating status of the photovoltaic charging system, and providing power supplementation based on the external power grid when it is detected that the photovoltaic power generation device is insufficient and / or the energy storage device is below a power threshold; During the charging process, the battery state of charge and real-time photovoltaic power generation power of the plurality of light commercial electric vehicles are updated in real time. If the expected result exceeds a charging threshold, the charging priority sequence is recalculated and the real-time power allocation scheme is updated.

2. A photovoltaic centralized charging control method for light commercial electric vehicles according to claim 1, characterized in that: Processing the weather data and the data of the photovoltaic power generation device based on a preset photovoltaic power generation prediction algorithm to generate a photovoltaic power generation power prediction curve specifically includes: Acquiring photovoltaic module parameters and environmental monitoring data of the photovoltaic power generation device; wherein the photovoltaic module parameters include a module efficiency attenuation curve and a photovoltaic array tilt adjustment range, and the environmental monitoring data includes a module surface temperature and an ambient irradiance; Performing time series analysis on the weather data using a preset LSTM neural network model, where the weather data includes cloud cover change data for a certain period of time in the future and historical seasonal light intensity distribution data; Inputting the photovoltaic module parameters into the LSTM neural network model to generate an initial photovoltaic power generation prediction curve; Based on the component surface temperature and the ambient irradiance, the initial prediction curve is corrected by comparing it with a preset temperature power reduction coefficient comparison table to generate the photovoltaic power prediction curve.

3. A photovoltaic centralized charging control method for light commercial electric vehicles according to claim 1, characterized in that: Calculating a charging priority sequence for a plurality of light commercial electric vehicles based on the battery state of charge, a preset task schedule, and a photovoltaic power generation power prediction curve specifically includes: Constructing a multi-dimensional evaluation index system based on the battery state of charge and the preset task schedule; wherein the index system includes a task urgency factor, a charging efficiency factor, and a grid interaction factor; Processing the multidimensional evaluation index system based on a preset entropy weight TOPSIS algorithm to generate a plurality of dynamic priority scores for the light commercial electric vehicles; Performing spatiotemporal matching of the dynamic priority score with the photovoltaic power generation prediction curve to determine a photovoltaic priority charging period and an energy storage compensation charging period; A charging priority sequence is generated according to the time period matching result; wherein the charging priority sequence includes a time period allocation table and a power fluctuation tolerance parameter for each charging pile.

4. A photovoltaic centralized charging control method for light commercial electric vehicles according to claim 3, characterized in that: A multi-dimensional evaluation index system is constructed based on the battery state of charge and the preset task schedule, specifically including: Determining the available charging time based on the planned departure timestamp in the preset task schedule and the current time, and deducting a preset safety margin to determine the actual available charging time; Obtaining a preset task type weight table; wherein the task type weight table at least includes emergency coefficients corresponding to cold chain transport tasks, general freight tasks, and spare vehicle tasks; Processing the actual available charging time, the battery state of charge, and a preset task type weight table based on a preset exponential decay model to generate a task urgency factor; Determining a maximum allowable charging current based on battery data of the plurality of light commercial electric vehicles, and determining a charging efficiency factor based on a ratio of an actual output power to a rated power of a charging pile; Obtaining a time-of-use electricity price signal and a regional load rate of the external power grid to determine a power grid interaction factor; A multidimensional evaluation index system is constructed based on the task urgency factor, charging efficiency factor and grid interaction factor.

5. A photovoltaic centralized charging control method for light commercial electric vehicles according to claim 4, characterized in that: Determine the real-time photovoltaic power generation power, and process the charging priority sequence and the real-time photovoltaic power generation power through a preset dynamic power allocation algorithm to generate a real-time power allocation plan and expected results for multiple charging piles, specifically including: Collecting voltage and current data of the photovoltaic power generation device in real time, and determining the real-time photovoltaic power generation power through a preset sliding average filtering algorithm; Establishing a dynamic power allocation matrix; wherein the dynamic power allocation matrix includes charging pile numbers, real-time photovoltaic power generation power, charging priority sequence and charging efficiency curve; Solving the optimal solution of the dynamic power allocation matrix based on a preset mixed integer programming algorithm; wherein the objective function of the mixed integer programming algorithm is to minimize the total charging completion time, and the constraints include the maximum power limit of the charging pile and the safe charging current limit of the battery; A real-time power allocation plan is generated based on the optimal solution, and expected results of the plurality of light commercial electric vehicles are calculated based on the real-time power allocation plan.

6. A method for controlling photovoltaic centralized charging of a light commercial electric vehicle according to claim 5, characterized in that: Controlling the plurality of charging piles to perform charging operations according to the real-time power allocation scheme, and monitoring the operating status of the photovoltaic charging system, and performing power replenishment based on the external power grid when it is detected that the photovoltaic power generation device is insufficient and / or the energy storage device's energy storage capacity is lower than a power threshold, specifically comprising: Collecting output power data of the photovoltaic power generation device in real time, and determining the current total charging demand by accumulating the power allocation values of all charging piles in the real-time power allocation plan; Calculating the difference between the photovoltaic power generation power and the total charging demand based on a preset power difference algorithm; When the difference continues to be negative for more than a first preset time, it is determined that photovoltaic power generation is insufficient, and the energy storage device is called upon to provide supplementary power supply; Periodically obtain the remaining power percentage of the energy storage device and compare it with the power threshold; When the remaining power percentage is lower than the power threshold, the grid power replenishment instruction is triggered to enter the power replenishment mode; Obtain time-of-use electricity price data from the external power grid; Calculating the power allocation weight of the external power grid based on the time-of-use electricity price data and generating a power allocation plan for the power grid supplement; wherein the power allocation plan for the power grid supplement includes the power supply ratio and phase synchronization parameters of each charging pile; Controlling the charging pile to switch to the grid power supply mode, performing power allocation based on the grid supplementary power allocation plan, and monitoring in real time whether the grid load rate exceeds the preset safety range; When it is detected that the photovoltaic power generation power has recovered to the total charging demand or the power of the energy storage device has returned to the safety threshold, the proportion of grid power supply will be gradually reduced until the power replenishment mode is exited.

7. A method for controlling photovoltaic centralized charging of a light commercial electric vehicle according to claim 5, characterized in that: During the charging process, the battery state of charge and real-time photovoltaic power generation of the plurality of light commercial electric vehicles are updated in real time. If the expected result exceeds a charging threshold, the charging priority sequence is recalculated and the real-time power allocation scheme is updated, specifically including: Collecting incremental state of charge data of the batteries of the light commercial electric vehicles and actual output power data of the charging piles at preset intervals; Inputting the battery state of charge increment data into a preset charging efficiency evaluation model to generate an actual charging rate curve; Comparing the actual charging rate curve with the expected result, and calculating the time integral difference between the two; When the time integral difference exceeds a charging threshold, a multi-dimensional evaluation index system is reconstructed based on the latest collected battery state of charge and the revised photovoltaic power generation power prediction curve; Reorder the dynamic priority scores based on a preset rolling optimization algorithm to generate an updated charging priority sequence; The real-time power allocation scheme is updated according to the updated charging priority sequence and constraint conditions.

8. The photovoltaic centralized charging control method for light commercial electric vehicles according to claim 1, characterized in that: The method further comprises: Collect historical operation data of a complete charging cycle to build a charging performance evaluation dataset; Processing the charging efficiency evaluation data set using a preset feature extraction algorithm to extract a set of key performance indicators; A random forest regression model is constructed based on the key performance indicator set to analyze the impact of the parameters in the key performance indicator set on the photovoltaic charging system and generate charging strategy optimization suggestions.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and operable on the processor, wherein: When the processor executes the computer program, the photovoltaic centralized charging control method for light commercial electric vehicles according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for controlling photovoltaic centralized charging of a light-duty commercial electric vehicle according to any one of claims 1 to 8 is implemented.

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