Electric heavy truck cooperative balance control system and method based on large-scale charging group

Abstract

The invention discloses an electric heavy truck cooperative balance control system and method based on a large-scale charging group, and the method comprises the steps: constructing a power grid load prediction model, and obtaining a power grid load prediction value; establishing a multi-objective optimization model, wherein the objective function is to minimize the deviation between the planned charging power and the actual demand power of each electric heavy truck; setting a constraint function, and constraining the planned charging power and charging time of each electric heavy truck and the power grid load of each time period; collecting multi-source data and extracting multi-source features; constructing a demand prediction model based on the multi-source features to obtain a charging demand prediction result, and performing charging priority dynamic evaluation on the electric heavy truck to obtain a charging sequence of the electric heavy truck; and based on the charging sequence and the planned charging power, power distribution is carried out on the to-be-charged electric heavy truck, and an optimal charging station and an optimal charging path are recommended. According to the actual conditions of the vehicle and the power grid, the charging power can be dynamically adjusted, the charging time is shortened, the charging efficiency is improved, and the power resource of the charging pile is fully utilized.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric heavy truck charging, and particularly relates to a collaborative balancing control system and method for electric heavy trucks based on a large-scale charging group. Background Art

[0002] An electric heavy truck is a heavy-duty truck that uses an electric drive system to replace a traditional diesel engine, covering types such as tractors, dump trucks, and freight trucks, with a total mass usually exceeding 14 tons; due to its advantages of zero emissions, low usage cost, high torque output, etc., it is widely used in fields such as ports, mining areas, and urban logistics; the charging piles supporting electric heavy trucks are mainly divided into DC fast charging piles and battery swapping stations; the DC fast charging piles directly charge the vehicle battery through a high-power current, with a power usually between 120kW and 1200kW, which can meet the rapid energy replenishment needs of electric heavy trucks; the battery swapping station can quickly put the vehicle into operation by replacing the fully charged battery pack, and the battery swapping can be completed in 3 - 5 minutes.

[0003] In ports, electric heavy trucks are mainly used for short-distance transportation of containers, with a driving mileage usually within 100 kilometers; relying on their high torque characteristics, the vehicles can easily tow heavy containers, and there is no noise and exhaust emissions during operation, improving the port operation environment; in mining areas, electric dump trucks are responsible for ore transportation tasks, and their fixed transportation routes make them suitable for the battery swapping mode to ensure long-term continuous operation; in the field of urban logistics, electric heavy trucks are responsible for medium and short-distance cargo distribution and charge at urban charging stations during the night or distribution intervals.

[0004] However, large-scale centralized charging of electric heavy trucks will cause a large impact on the power grid; when a large number of electric heavy trucks are simultaneously connected to the charging piles for charging, the instantaneous high-power demand may cause local grid load overload, affecting the stability and reliability of the power grid; if the grid infrastructure cannot meet the demand, upgrades and renovations are required, increasing the construction and operation costs, and at the same time, it will also seriously reduce the charging efficiency of electric heavy trucks.

[0005] For example, in the patent applications with publication numbers CN119313096A and CN120046935A, these methods can optimize the charging process of electric heavy trucks through an integrated energy system and a charging scheduling model, but do not consider the real-time energy optimization and grid load management during the charging process. At the same time, these methods also do not consider the energy balance in the charging station and the collaborative scheduling problem across stations. Under large-scale charging demands, they cannot make full use of the bidirectional energy transfer between the energy storage system and the power grid, resulting in unstable grid loads and insufficient utilization of the energy storage system. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a collaborative balancing control system and method for electric heavy-duty trucks based on large-scale charging stations. The system collects historical grid load data, real-time status of vehicles to be charged, and grid operating parameters, and performs normalization processing. A grid load prediction model is constructed to obtain predicted grid load values. A multi-objective optimization model is established, with the objective function being to minimize the deviation between the planned charging power and actual demand power for each electric heavy-duty truck. Constraint functions are set to constrain the planned charging power, charging time, and grid load for each time period for each electric heavy-duty truck. Multi-source data is collected and multi-source features are extracted. The multi-source data includes historical charging data of electric heavy-duty trucks, transportation task plans, weather, and road condition data. Based on the multi-source features, a demand prediction model is constructed to obtain charging demand prediction results. Combining the charging demand prediction results, an evaluation formula is established to dynamically evaluate the charging priority of electric heavy-duty trucks and arrange their charging sequence. Based on the charging sequence, and considering the priority and planned charging power of each electric heavy-duty truck, power allocation is performed on the electric heavy-duty trucks to be charged. After power allocation, the optimal charging station and optimal charging route are recommended for each electric heavy-duty truck. This invention can dynamically adjust the charging power according to the actual conditions of the vehicle and the power grid, reduce charging time, and improve charging efficiency; it can make full use of the power resources of the charging pile, avoid resource idleness, and improve the utilization rate of the charging pile.

[0007] The present invention adopts the following technical solution.

[0008] This invention proposes a collaborative balance control method for electric heavy-duty trucks based on large-scale charging stations, characterized by comprising:

[0009] S1: Collect historical power grid load data, real-time status of vehicles to be charged, and power grid operating parameters, and perform normalization processing; use the normalized data to construct a power grid load prediction model and obtain the predicted power grid load value;

[0010] S2. Establish a multi-objective optimization model with the objective function of minimizing the deviation between the planned charging power and the actual demand power of each electric heavy truck; set constraint functions to constrain the planned charging power, charging time and grid load of each electric heavy truck in each time period.

[0011] S3, Collect multi-source data and extract multi-source features; the multi-source data includes historical charging data of electric heavy trucks, transportation task plans, weather and road condition data; based on the multi-source features, construct a demand prediction model and obtain charging demand prediction results;

[0012] S4. Based on the charging demand forecast results, establish an evaluation formula to dynamically evaluate the charging priority of electric heavy trucks and arrange the charging sequence of electric heavy trucks; based on the charging sequence, combine the priority and planned charging power of each electric heavy truck to allocate power to the electric heavy trucks to be charged.

[0013] After power allocation, S5 recommends the optimal charging station and optimal charging route for electric heavy trucks.

[0014] Furthermore, in S1, the real-time status of the vehicle to be charged includes the initial state of charge (SOC), target charge level, maximum charging power, and allowed charging time window.

[0015] Historical load data are normalized to eliminate the influence of dimensions.

[0016] Furthermore, in S1, a load forecasting model is constructed, taking historical load sequences and the current total charging power of electric heavy trucks as inputs, and outputting load forecast values ​​every 15 minutes for the next 4 hours;

[0017] The actual load data of the power grid is collected every 15 minutes. If the deviation between the actual load data and the predicted load value of the power grid exceeds 10%, an adjustment is triggered.

[0018] When the grid load is higher than the forecast, the power of vehicles that are charging will be smoothly reduced.

[0019] When the load is lower than the forecast value, the charging power is gradually increased to make full use of the remaining capacity of the power grid.

[0020] Furthermore, in S2, the actual power demand is calculated by dividing the charging capacity by the available charging time;

[0021] The planned power of each vehicle shall not be lower than the minimum charging power and shall not exceed the maximum allowable power; the charging start time must be within the time window specified for the vehicle; and the predicted load value of the power grid at each time period shall not exceed the threshold.

[0022] Furthermore, in S3, spatiotemporal feature vectors are extracted from historical charging data, and the daily, weekly, and special time period features in the charging timestamps are analyzed. At the same time, combined with the geographical location of charging stations, the charging density and vehicle flow heat map of different areas are calculated.

[0023] Initial demand forecasts are generated based on transportation task plans. The transportation task is broken down into parameters such as start point, destination, cargo weight, and estimated travel time. The charging demand is estimated by combining the vehicle energy consumption model and mapped to a specific time window to obtain the feature vector of task demand.

[0024] By combining weather factors, a weather impact factor model is established to obtain weather impact factors; by combining road condition factors, a congestion index is obtained through traffic big data; and the weather impact factors and congestion index are used as environmental correction feature vectors.

[0025] Spatiotemporal characteristics, task requirements, and environmental modifications are used as multi-source features.

[0026] Furthermore, in S4, the evaluation formula is: Priority Score = λ1 × SOC Urgency + λ2 × Task Criticality + λ3 × Electricity Price Sensitivity + λ4 × Battery Health + λ5 × User Level; where SOC Urgency = 1 - SOC, λ1, λ2, λ3, λ4 and λ5 are weighting coefficients, which are set according to the actual situation, and λ1 + λ2 + λ3 + λ4 + λ5 = 1.

[0027] Furthermore, in S4, during the priority evaluation process, combined with the time window constraint, the charging demand of the electric heavy truck is obtained based on the predicted charging demand of the electric heavy truck in the time window, and the priority score of the electric heavy truck is reduced or increased according to the charging demand.

[0028] Furthermore, in S4, the total available charging power is preferentially allocated to vehicles with higher priority; according to the vehicle priority order, a "fair share" of the ratio of the current remaining power to the number of remaining vehicles is allocated to each vehicle, while ensuring that the allocated power does not exceed the vehicle's maximum allowable charging power;

[0029] When the allocated power is lower than the planned charging power of electric heavy trucks, its priority is automatically increased to ensure that vehicles with critical missions have their charging needs met first.

[0030] Furthermore, in S5, all available depots near electric heavy-duty trucks are evaluated, considering four factors:

[0031] Site availability is assessed by evaluating the number of available charging stations and estimated waiting times; the calculation formula is:

[0032]

[0033] Where, N available Number of available charging stations, N total S represents the total number of charging stations, waiting_time represents the estimated waiting time, and S represents the total number of charging stations. avail T is the site availability score. max Maximum waiting time;

[0034] Path efficiency is calculated by combining real-time traffic conditions to determine the travel time and distance of vehicles to the depot; the calculation formula is:

[0035]

[0036] Among them, T drive Travel time is measured in minutes or hours; D drive The distance traveled is expressed in kilometers or meters; T max1 In this embodiment, the maximum permissible travel time is set to 60 minutes; D max1In this embodiment, the maximum permissible driving distance is set to 100 kilometers; S path The path efficiency score is calculated; α and β are weighting coefficients that control the degree of influence of time and distance on path efficiency, respectively.

[0037] Electricity price differences are calculated by comparing the charging prices at different charging stations with the regional average electricity price; the calculation formula is as follows:

[0038]

[0039] Among them, P station The charging price for the station; P avg S represents the regional average electricity price. price Score the electricity price difference;

[0040] Battery health compatibility is determined by recommending the most suitable charging parameters based on the vehicle's battery status; the calculation formula is:

[0041]

[0042] Where SOH represents battery health; S SOH Rate the battery health compatibility;

[0043] The above factors are weighted according to the actual situation, the comprehensive score is calculated, and the station with the highest score is finally selected as the optimal charging station.

[0044] Furthermore, in S4, after determining the optimal charging station, a path objective function is established, including minimizing transportation time, minimizing charging cost, and minimizing the impact on grid load. Constraints are set, including:

[0045] The electric heavy-duty truck's charge level should never be lower than 15%.

[0046] Charging time must be within the time window allowed by the vehicle operation plan;

[0047] The load balance of each station should be kept below 30% to avoid local overload;

[0048] The optimal charging path is obtained by solving the problem.

[0049] This invention also proposes a collaborative balance control system for electric heavy-duty trucks based on large-scale charging stations, including a charging infrastructure layer, a vehicle management layer, an energy management subsystem, and an intelligent scheduling subsystem.

[0050] The charging facility layer includes a charging pile control unit and an intelligent charging management unit; the intelligent charging management unit controls the charging process of the charging pile and controls the charging power and charging time parameters.

[0051] The vehicle management layer includes a battery management module and a vehicle control module. The battery management module monitors the battery's voltage, current, temperature, and state of charge parameters, monitors the battery's charging and discharging within a safe operating range, and sends the battery's status information to the charging station to adjust the charging strategy. The vehicle control module determines whether the vehicle is in a suitable charging state and interacts with the charging station.

[0052] The energy management subsystem includes a power grid energy management module and an in-station energy optimization module. The power grid energy management module monitors the voltage, frequency, and load parameters of the charging grid and formulates charging plans based on the grid's operating status and the charging needs of electric heavy trucks.

[0053] The intelligent dispatching subsystem includes a vehicle charging dispatching module and a multi-site collaborative dispatching module. The vehicle charging dispatching module arranges the charging sequence and charging spaces for vehicles based on the arrival time, battery level, charging demand, and charging pile availability of electric heavy trucks, and allocates charging solutions to vehicles. The multi-site collaborative dispatching module guides vehicles to suitable sites for charging based on the load conditions, number of charging piles, and vehicle distribution of different sites.

[0054] Furthermore, the energy management subsystem also includes vehicle-to-grid (V2G) technology, enabling bidirectional energy transfer between the electric heavy-duty truck and the power grid.

[0055] Furthermore, the vehicle charging scheduling module includes the establishment of a charging demand prediction model, dynamic evaluation of charging priority, and dynamic allocation of charging power.

[0056] The evaluation formula for dynamic assessment of charging priority is as follows:

[0057] Priority score = λ1 × SOC urgency + λ2 × task criticality + λ3 × electricity price sensitivity + λ4 × battery health + λ5 × user level; where SOC urgency = 1 - SOC, λ1, λ2, λ3, λ4 and λ5 are weighting coefficients, set according to actual conditions, and λ1 + λ2 + λ3 + λ4 + λ5 = 1; the charging order of heavy trucks is arranged according to the ascending order of priority scores.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] 1. This invention, through intelligent power scheduling and optimized charging strategies, can dynamically adjust the charging power according to the actual conditions of the vehicle and the power grid, reduce charging time, and improve charging efficiency; and can avoid the excessive load impact on the power grid caused by large-scale simultaneous charging of electric heavy trucks, reduce the peak load of the power grid, and ensure the safe and stable operation of the power grid.

[0060] 2. The intelligent power allocation technology and vehicle charging scheduling algorithm of the present invention can make full use of the power resources of charging piles, avoid resource idleness, improve the utilization rate of charging piles, and reduce the construction and operation costs of charging stations. Attached Figure Description

[0061] Figure 1 This is a flowchart of the method for the collaborative balance control of electric heavy-duty trucks based on large-scale charging stations according to the present invention.

[0062] Figure 2 This is a system structure diagram of the electric heavy-duty truck collaborative balance control system based on a large-scale charging group according to the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0064] Example 1

[0065] like Figure 1 As shown, the present invention provides a technical solution for a collaborative balance control method for electric heavy-duty trucks based on large-scale charging stations, comprising:

[0066] S1 collects historical power grid load data, real-time status of vehicles waiting to be charged, and power grid operating parameters, and performs normalization processing; using the normalized data, a power grid load prediction model is constructed to obtain the predicted power grid load value.

[0067] Specifically, the real-time status of the vehicles to be charged includes initial state of charge (SOC), target charge level, maximum charging power, and allowable charging time window. Historical load data is normalized to eliminate the influence of dimensions, while key parameters in vehicle charging demand are selected.

[0068] A load forecasting model is constructed using a Long Short-Term Memory (LSTM) network: Inputting historical load sequences (e.g., data from the past 24 hours) and the current total charging power of electric heavy-duty trucks, the model is processed through multiple layers of neurons to output load forecasts every 15 minutes for the next 4 hours. This model can capture the temporal characteristics and periodic patterns of power grid load, such as load fluctuations during weekday morning and evening peak hours, thus providing a forward-looking basis for charging plans. During model training, the mean square error between the actual load and the load forecast is used as the optimization objective. The network parameters are adjusted through a backpropagation algorithm to ensure that the prediction error does not exceed 10%. Using a Long Short-Term Memory (LSTM) network is a preferred embodiment of this invention; other neural networks can also be used to achieve similar results.

[0069] Furthermore, actual grid load data is collected every 15 minutes. If the deviation between the actual load data and the grid's predicted load value exceeds 10%, an adjustment is triggered.

[0070] When the grid load is higher than the predicted value, the power of vehicles that are charging is smoothly reduced to avoid damage to the battery from sudden power outages;

[0071] When the load is lower than the forecast value, the charging power is gradually increased to make full use of the remaining capacity of the power grid and improve charging efficiency.

[0072] S2. Establish a multi-objective optimization model with the objective function of minimizing the deviation between the planned charging power and the actual demand power of each electric heavy-duty truck. Set constraint functions to constrain the planned charging power, charging time, and grid load of each electric heavy-duty truck in each time period.

[0073] Specifically, the actual power demand is calculated by dividing the charging capacity by the available charging time.

[0074] Furthermore, the planned power of each vehicle shall not be lower than the minimum charging power (ensuring charging is completed within the allowed time) and shall not exceed the maximum allowable power; the charging start time must be within the time window specified for the vehicle; and the predicted load value of the power grid at each time period shall not exceed the threshold.

[0075] The above model is solved using a mixed-integer programming algorithm to determine the optimal charging power and start time for each vehicle. While mixed-integer programming is a preferred method in this embodiment, other methods such as particle swarm optimization and genetic algorithms can also achieve similar results.

[0076] For charging stations equipped with energy storage systems, the grid energy management module coordinates the energy flow between the energy storage system and the charging piles. During off-peak charging periods, it uses the surplus electricity of the grid to charge the energy storage system. During peak charging periods, it uses the energy storage system to discharge to supplement the power demand of the charging piles, thereby achieving energy balance and optimized utilization within the station and reducing dependence on the grid.

[0077] S3. Collect multi-source data and extract multi-source features; the multi-source data includes historical charging data of electric heavy trucks, transportation task plans, weather and road condition data; based on the multi-source features, construct a demand prediction model and obtain charging demand prediction results.

[0078] Specifically, spatiotemporal feature vectors are extracted from historical charging data to analyze the daily cycle (e.g., morning and evening peak hours), weekly cycle (difference between weekdays and weekends), and special time periods (holidays) features in the charging timestamps. Simultaneously, combined with the geographical location of charging stations, charging density and vehicle flow heatmaps for different areas are calculated. During data preprocessing, wavelet transform is used to eliminate abnormal pulses, linear interpolation is used to fill missing values, and standardization is performed to unify data dimensions.

[0079] Furthermore, based on the transportation task plan, an initial demand forecast is generated. The transportation task is broken down into parameters such as origin, destination, cargo weight, and estimated travel time. This is combined with a vehicle energy consumption model to estimate the charging demand, which is then mapped to a specific time window to obtain a feature vector of the task demand. For example, a logistics company's transportation plan can specify the vehicle return time, thereby predicting the temporal distribution of its charging demand.

[0080] Among them, the vehicle energy consumption model is existing technology in this field, and will not be elaborated on here.

[0081] Furthermore, weather and road condition data are incorporated to refine the initial demand forecast. Regarding weather factors, real-time temperature, wind speed, and rain / snow conditions affect battery performance and vehicle energy consumption; these are dynamically adjusted by establishing a weather impact factor model. As for road condition factors, congestion indices are obtained through big data traffic analysis to correct for increased travel time and energy consumption caused by changes in road conditions. Weather impact factors and congestion indices are used as environmental correction feature vectors.

[0082] The weather influencing factor model can be obtained through methods not limited to regression analysis and neural networks, which will not be elaborated upon here. The congestion index can be obtained through methods not limited to big data analysis and machine learning, and these existing technologies will not be elaborated upon here.

[0083] Furthermore, a demand forecasting model is constructed, which outputs the charging demand forecast results for the next few hours based on three types of vectors: spatiotemporal characteristics, task requirements, and environmental corrections, providing a data foundation for subsequent priority assessment and power allocation.

[0084] In a preferred embodiment of the present invention, the demand prediction model uses a Long Short-Term Memory (LSTM) network. This network structure comprises two layers of LSTM neurons and one fully connected layer, receiving three types of vectors as input: spatiotemporal features, task requirements, and environmental corrections. During training, a combined loss function is used to balance prediction accuracy, and the Adam optimization algorithm is used to iteratively update the model parameters.

[0085] In addition to the method of the preferred embodiment of the present invention, other neural network models can be used to achieve similar results.

[0086] S4. Based on the charging demand forecast results, an evaluation formula is established to dynamically evaluate the charging priority of electric heavy-duty trucks and arrange the charging sequence of electric heavy-duty trucks. Based on the charging sequence, and combined with the priority and planned charging power of each electric heavy-duty truck, power is allocated to the electric heavy-duty trucks to be charged.

[0087] Specifically, a priority score is calculated for each heavy truck based on multiple key indicators to determine the charging order. As a preferred embodiment of the present invention, the evaluation formula is: Priority Score = 0.3 × SOC Urgency + 0.2 × Task Criticality + 0.25 × Electricity Price Sensitivity + 0.15 × Battery Health + 0.1 × User Level.

[0088] Among these metrics, State of Charge (SOC) is the core indicator for measuring the urgency of vehicle charging; the lower the SOC value, the higher the urgency of charging. SOC urgency = 1 - SOC. Task criticality is determined by the urgency of the transportation task's deadline; tasks nearing their deadline have higher priority. Electricity price sensitivity is related to the ratio of the current electricity price to the historical highest electricity price; when the electricity price is low, the vehicle's charging priority will be appropriately reduced. Battery health is represented by State of Health (SOH); batteries with poor health require priority charging. Additionally, user level is also a reference factor; vehicles from higher-level users have higher charging priority. These indicators are weighted according to preset weights (urgency 30%, task criticality 20%, electricity price sensitivity 25%, battery health 15%, user level 10%) to calculate a priority score for each electric heavy-duty truck. Finally, charging is arranged according to the scores from highest to lowest, ensuring that vehicles with urgent tasks and those in poor condition receive priority charging resources.

[0089] In the priority assessment process, priority scores are dynamically adjusted based on the predicted charging demand of electric heavy-duty trucks. For example, if the charging demand prediction shows that a certain electric heavy-duty truck has particularly high demand during a specific period, then even if the truck's SOC is high, it can still be prioritized for charging to ensure that its demand is met.

[0090] Furthermore, during the power allocation process, the total available charging power is preferentially allocated to vehicles with higher priority; according to the vehicle priority order, a "fair share" of the ratio of the current remaining power to the number of remaining vehicles is allocated to each vehicle, while ensuring that the allocated power does not exceed the vehicle's maximum allowable charging power.

[0091] Specifically, when the allocated power is lower than the planned charging power of electric heavy trucks, its priority is automatically increased to ensure that vehicles with critical missions have their charging needs met first.

[0092] After power allocation, S5 recommends the optimal charging station and optimal charging route for electric heavy trucks.

[0093] Specifically, all available depots near electric heavy-duty trucks were evaluated, with a focus on four factors:

[0094] I. Site Availability: Assess the number of available charging stations and estimated waiting time; the calculation formula is:

[0095]

[0096] Where, N available Number of available charging stations, N total S represents the total number of charging stations, waiting_time represents the estimated waiting time, and S represents the total number of charging stations. avail T is the site availability score. max Maximum waiting time;

[0097] II. Path efficiency, calculated by combining real-time traffic conditions to determine the vehicle's travel time and distance to the depot; the calculation formula is as follows:

[0098]

[0099] Among them, T drive Travel time is measured in minutes or hours; D drive The distance traveled is expressed in kilometers or meters; T max1 In this embodiment, the maximum permissible travel time is set to 60 minutes; D max1 In this embodiment, the maximum permissible driving distance is set to 100 kilometers; S path The path efficiency score is calculated; α and β are weighting coefficients that control the degree of influence of time and distance on path efficiency, respectively.

[0100] III. Electricity Price Differences: Compare charging prices at different charging stations with the regional average electricity price; the calculation formula is as follows:

[0101]

[0102] Among them, P station The charging price for the station; Pavg S represents the regional average electricity price. price Score the electricity price difference;

[0103] IV. Battery health compatibility: Recommending the most suitable charging parameter configuration based on the vehicle's battery status; the calculation formula is:

[0104]

[0105] Where SOH represents battery health; S SOH Rate the battery health compatibility;

[0106] In a preferred embodiment of the present invention, the above factors are weighted at 40%, 30%, 20% and 10% respectively to calculate a comprehensive score, and the station with the highest score is selected as the optimal charging station to ensure that vehicles can be charged quickly and economically.

[0107] Furthermore, a cross-site charging resource allocation mechanism is established, with each site allocating 20% ​​of its charging capacity to a shared resource pool. Blockchain technology is used to achieve real-time registration of capacity and cross-site settlement. Shared capacity information includes site ID, available time period, shareable power, and internal settlement price. The system will dynamically update the capacity map of each site. When a site experiences a charging peak, it can draw on the idle capacity of other sites from the shared pool to achieve balanced utilization of charging resources within the region.

[0108] Furthermore, after determining the optimal charging station, a path objective function is established, including minimizing transportation time, minimizing charging cost, and minimizing the impact on grid load. Constraints are set, including:

[0109] 1. The electric heavy-duty truck must always maintain a charge level of no less than 15% to ensure driving safety;

[0110] Second, charging time must be within the time window allowed by the vehicle operation plan;

[0111] 3. The load balance of each station should be kept below 30% to avoid local overload.

[0112] As a preferred embodiment of the present invention, a multi-objective optimization strategy is used to obtain the optimal charging path. Besides the method in the preferred embodiment of the present invention, similar results can also be achieved using methods not limited to simulated annealing and ant colony optimization.

[0113] Example 2

[0114] like Figure 2 As shown, the present invention provides a technical solution for a collaborative balance control system for electric heavy-duty trucks based on large-scale charging groups, including a charging facility layer, a vehicle management layer, an energy management subsystem, and an intelligent scheduling subsystem;

[0115] Specifically, the charging facility layer includes a charging pile control unit and an intelligent charging management unit;

[0116] The charging station uses a high-power, high-performance DC charger, including a power distributor, a charging status monitoring module, and a local safety protection unit.

[0117] The intelligent charging management unit communicates with the electric heavy-duty truck to obtain the vehicle's battery status and charging demand information, and transmits the information to the upper-level control system. It also receives instructions from the upper-level control system to control the charging process of the charging pile, achieving precise control of charging power and charging time parameters. Through real-time communication and interaction between the intelligent charging management unit, the vehicle's BMS, and the charging pile, it achieves precise control and management of the charging process, including adaptive adjustment of charging power, charging safety protection, charging status monitoring, and fault diagnosis functions, ensuring the safety, reliability, and efficiency of the charging process.

[0118] Furthermore, the vehicle management layer includes the battery management module and the vehicle control module;

[0119] The battery management module is used to monitor the battery's voltage, current, temperature, and state of charge parameters to ensure that the battery is charged and discharged within a safe operating range. The battery management module also communicates with the charging pile's intelligent charging management unit to send the battery's status information to the charging pile so that the charging pile can adjust the charging strategy according to the battery status.

[0120] The vehicle control module (VCU) coordinates the work of various parts of the vehicle. During the charging process, the VCU determines whether the vehicle is in a suitable charging state based on the battery information provided by the battery management module and the driver's operation instructions. It also interacts with the charging pile to complete the charging connection and the start and stop of the charging process.

[0121] Furthermore, the energy management subsystem includes a grid energy management module and an in-station energy optimization module;

[0122] The power grid energy management module monitors the voltage, frequency, and load parameters of the charging grid. Based on the grid's operating status and the charging needs of electric heavy trucks, it formulates reasonable charging plans to avoid large-scale charging impacting the grid. When the grid load is high, it reduces charging power or delays the charging time of some vehicles; when the grid load is low, it increases charging power to improve charging efficiency.

[0123] The in-station energy optimization module, for charging stations equipped with energy storage systems, coordinates the energy flow between the energy storage system and the charging piles through the grid energy management module. During off-peak charging periods, it utilizes the surplus electricity of the grid to charge the energy storage system. During peak charging periods, it supplements the power demand of the charging piles by discharging the energy storage system, thereby achieving energy balance and optimized utilization within the station and reducing dependence on the grid.

[0124] The energy management subsystem also includes vehicle-to-grid (V2G) interaction technology, which allows bidirectional energy transfer between electric heavy-duty trucks and the power grid. When the grid load is too high, the electric heavy-duty truck can feed the power from its battery back to the grid to provide power support. When the grid load is low, the vehicle can use low-cost electricity for charging, realizing the coordinated interaction between the vehicle and the grid, and improving the stability of the grid and energy utilization efficiency.

[0125] Furthermore, the intelligent scheduling subsystem includes a vehicle charging scheduling module and a multi-site collaborative scheduling module;

[0126] The vehicle charging scheduling module rationally arranges the charging sequence and charging spaces of electric heavy trucks based on their arrival time, battery level, charging demand, and the availability of charging piles, thereby reducing vehicle waiting time and improving the utilization rate of charging piles. It adopts a dynamic priority scheduling algorithm to allocate the optimal charging scheme to vehicles based on their urgency and charging time requirements.

[0127] The vehicle charging scheduling module includes the establishment of a charging demand prediction model, dynamic evaluation of charging priority, and dynamic allocation of charging power.

[0128] Charging demand forecasting employs multi-source data fusion and deep learning technologies.

[0129] The evaluation formula for dynamic assessment of charging priority is as follows:

[0130] In a preferred embodiment of the present invention, the priority score is calculated as follows: priority score = 0.3 × SOC urgency + 0.2 × task criticality + 0.25 × electricity price sensitivity + 0.15 × battery health + 0.1 × user level; the priority scores are arranged in ascending order to reasonably arrange the charging sequence of heavy trucks.

[0131] In terms of dynamic charging power allocation, the system achieves intelligent power adjustment based on an improved Water-Filling algorithm;

[0132] For large-scale electric heavy-duty truck charging networks involving multiple charging stations, the multi-station collaborative scheduling module enables the intelligent scheduling subsystem to uniformly manage and coordinate the charging resources of each station. Based on the load conditions, number of charging piles, and vehicle distribution of different stations, it guides vehicles to suitable stations for charging, achieving a balanced distribution of charging resources within the region and avoiding situations where some stations are congested while other stations are idle.

[0133] The multi-site collaborative scheduling module includes a resource matching unit, a site dynamic capacity sharing unit, and a charging path optimization unit.

[0134] The resource matching unit recommends the optimal charging station for the vehicle through a multi-dimensional scoring mechanism. The system evaluates all available charging stations near the vehicle, focusing on four factors: station availability, the number of available charging piles and the estimated waiting time, path efficiency, electricity price differences, and battery health compatibility. Based on the vehicle's battery status, it recommends the most suitable charging parameter configuration. In a preferred embodiment of the invention, the above factors are weighted at 40%, 30%, 20%, and 10% respectively to calculate a comprehensive score. Finally, the charging station with the highest score is selected as the recommendation result, ensuring that the vehicle can complete charging quickly and economically.

[0135] The station dynamic capacity sharing unit establishes a cross-station charging resource allocation mechanism; each station will include 20% of its charging capacity in the shared resource pool, and use blockchain technology to realize real-time registration of capacity and cross-station settlement; the shared capacity information includes station ID, available time period, shareable power and internal settlement price. The system will dynamically update the capacity map of each station. When a station experiences a charging peak, it can call on the idle capacity of other stations from the shared pool to achieve balanced utilization of charging resources in the region.

[0136] The charging path optimization unit employs a multi-objective optimization strategy to plan the optimal charging path for the vehicle; the system comprehensively balances transportation time, charging cost, and grid impact while satisfying three core constraints.

[0137] 1. The vehicle's charge level must always be no lower than 15% to ensure driving safety;

[0138] Second, charging time must be within the time window allowed by the vehicle operation plan;

[0139] 3. The load balance of each station should be kept below 30% to avoid local overload.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for coordinated balance control of electric heavy-duty trucks based on large-scale charging stations, characterized in that, include: S1 collects historical power grid load data, real-time status of vehicles waiting to be charged, and power grid operating parameters, and performs normalization processing. Using the normalized data, a power grid load forecasting model is constructed to obtain the power grid load forecast value; S2. Establish a multi-objective optimization model with the objective function of minimizing the deviation between the planned charging power and the actual demand power of each electric heavy truck; set constraint functions to constrain the planned charging power, charging time and grid load of each electric heavy truck in each time period. S3, Collect multi-source data and extract multi-source features; the multi-source data includes historical charging data of electric heavy trucks, transportation task plans, weather and road condition data; based on the multi-source features, construct a demand prediction model and obtain charging demand prediction results; S4. Based on the charging demand forecast results, establish an evaluation formula to dynamically evaluate the charging priority of electric heavy trucks and arrange the charging sequence of electric heavy trucks; based on the charging sequence, combine the priority and planned charging power of each electric heavy truck to allocate power to the electric heavy trucks to be charged. After power allocation, S5 recommends the optimal charging station and optimal charging route for electric heavy trucks.

2. The method for coordinated balance control of electric heavy-duty trucks based on large-scale charging stations according to claim 1, characterized in that: In S1, the real-time status of the vehicle to be charged includes the initial state of charge (SOC), target charge level, maximum charging power, and allowed charging time window. Historical load data are normalized to eliminate the influence of dimensions.

3. The method for coordinated balance control of electric heavy-duty trucks based on large-scale charging stations according to claim 1, characterized in that: In S1, a load forecasting model is constructed, taking historical load sequences and the current total charging power of electric heavy trucks as inputs, and outputting load forecast values ​​every 15 minutes for the next 4 hours. The actual load data of the power grid is collected every 15 minutes. If the deviation between the actual load data and the predicted load value of the power grid exceeds 10%, an adjustment is triggered. When the grid load is higher than the forecast, the power of vehicles that are charging will be smoothly reduced. When the load is lower than the forecast value, the charging power is gradually increased to make full use of the remaining capacity of the power grid.

4. The method for coordinated balance control of electric heavy-duty trucks based on large-scale charging stations according to claim 1, characterized in that: In S2, the actual power demand is calculated by dividing the charging capacity by the available charging time; The planned power of each vehicle shall not be lower than the minimum charging power and shall not exceed the maximum allowable power; the charging start time must be within the time window specified for the vehicle; and the predicted load value of the power grid at each time period shall not exceed the threshold.

5. The method for coordinated balance control of electric heavy-duty trucks based on large-scale charging stations according to claim 1, characterized in that: In S3, spatiotemporal feature vectors are extracted from historical charging data, and the daily, weekly, and special time period features in the charging timestamps are analyzed. At the same time, the charging density and vehicle flow heat map of different areas are calculated by combining the geographical location of charging stations. Initial demand forecasts are generated based on transportation task plans. The transportation task is broken down into parameters such as start point, destination, cargo weight, and estimated travel time. The charging demand is estimated by combining the vehicle energy consumption model and mapped to a specific time window to obtain the feature vector of task demand. By combining weather factors, a weather impact factor model is established to obtain weather impact factors; by combining road condition factors, a congestion index is obtained through traffic big data; and the weather impact factors and congestion index are used as environmental correction feature vectors. Spatiotemporal characteristics, task requirements, and environmental modifications are used as multi-source features.

6. The method for coordinated balance control of electric heavy-duty trucks based on large-scale charging stations according to claim 1, characterized in that: In S4, the evaluation formula is: Priority Score = λ1 × SOC Urgency + λ2 × Task Criticality + λ3 × Electricity Price Sensitivity + λ4 × Battery Health + λ5 × User Level; where SOC Urgency = 1 - SOC, λ1, λ2, λ3, λ4 and λ5 are weighting coefficients, which are set according to the actual situation, and λ1 + λ2 + λ3 + λ4 + λ5 = 1.

7. The method for coordinated balance control of electric heavy-duty trucks based on large-scale charging stations according to claim 1, characterized in that: In S4, during the priority evaluation process, the charging demand of electric heavy trucks is obtained in the time window based on the predicted charging demand of electric heavy trucks, taking into account the time window constraints. The priority score of electric heavy trucks is then adjusted based on the charging demand.

8. The method for coordinated balance control of electric heavy-duty trucks based on large-scale charging stations according to claim 1, characterized in that: In S4, the total available charging power is preferentially allocated to vehicles with higher priority; according to the vehicle priority order, a "fair share" of the ratio of the current remaining power to the number of remaining vehicles is allocated to each vehicle, while ensuring that the allocated power does not exceed the vehicle's maximum allowable charging power; When the allocated power is lower than the planned charging power of electric heavy trucks, its priority is automatically increased to ensure that vehicles with critical missions have their charging needs met first.

9. The method for coordinated balance control of electric heavy-duty trucks based on large-scale charging stations according to claim 1, characterized in that: In S5, all available depots near electric heavy-duty trucks are evaluated, taking four factors into account: Site availability, assessing the number of available charging stations and estimated waiting times; The calculation formula is: Where, N available Number of available charging stations, N total S represents the total number of charging stations, waiting_time represents the estimated waiting time, and S represents the total number of charging stations. avail T is the site availability score. max Maximum waiting time; Path efficiency is calculated by combining real-time traffic conditions to determine the travel time and distance of vehicles to the depot; the calculation formula is: Among them, T drive Travel time is measured in minutes or hours; D drive The distance traveled is expressed in kilometers or meters; T max1 In this embodiment, the maximum permissible travel time is set to 60 minutes; D max1 In this embodiment, the maximum permissible driving distance is set to 100 kilometers; S path The path efficiency score is calculated; α and β are weighting coefficients that control the degree of influence of time and distance on path efficiency, respectively. Electricity price differences are calculated by comparing the charging prices at different charging stations with the regional average electricity price; the calculation formula is as follows: Among them, P station The charging price for the station; P avg S represents the regional average electricity price. price Score the electricity price difference; Battery health compatibility is determined by recommending the most suitable charging parameters based on the vehicle's battery status; the calculation formula is: Where SOH represents battery health; S SOH Rate the battery health compatibility; The above factors are weighted according to the actual situation, the comprehensive score is calculated, and the station with the highest score is finally selected as the optimal charging station.

10. The method for coordinated balance control of electric heavy-duty trucks based on large-scale charging stations according to claim 1 or 9, characterized in that: In S4, after determining the optimal charging station, a path objective function is established, including minimizing transportation time, minimizing charging cost, and minimizing the impact on grid load. Constraints are set, including: The electric heavy-duty truck's charge level should never be lower than 15%. Charging time must be within the time window allowed by the vehicle operation plan; The load balance of each station should be kept below 30% to avoid local overload; The optimal charging path is obtained by solving the problem.

11. A collaborative balance control system for electric heavy-duty trucks based on large-scale charging stations, comprising a charging infrastructure layer, a vehicle management layer, an energy management subsystem, and an intelligent scheduling subsystem, characterized in that: The charging facility layer includes a charging pile control unit and an intelligent charging management unit; the intelligent charging management unit controls the charging process of the charging pile and controls the charging power and charging time parameters. The vehicle management layer includes the battery management module and the vehicle control module; The battery management module monitors the battery's voltage, current, temperature, and state of charge parameters, ensuring the battery is charged and discharged within a safe operating range, and sends the battery's status information to the charging station to adjust the charging strategy. The vehicle control module determines whether the vehicle is in a suitable charging state and interacts with the charging station. The energy management subsystem includes a grid energy management module and an in-station energy optimization module; The power grid energy management module monitors the voltage, frequency, and load parameters of the charging grid, and formulates a charging plan based on the grid's operating status and the charging needs of electric heavy trucks. The intelligent dispatch subsystem includes a vehicle charging dispatch module and a multi-site collaborative dispatch module; The vehicle charging scheduling module arranges the charging sequence and charging spaces for electric heavy trucks based on their arrival time, battery level, charging needs, and the availability of charging stations, and assigns charging plans to the vehicles. The multi-site collaborative scheduling module guides vehicles to suitable sites for charging based on the load conditions, number of charging piles, and vehicle distribution of different sites.

12. The electric heavy-duty truck collaborative balance control system based on a large-scale charging group according to claim 11, characterized in that: The energy management subsystem also includes vehicle-to-grid (V2G) technology, enabling bidirectional energy transfer between the electric heavy-duty truck and the power grid.

13. The electric heavy-duty truck collaborative balance control system based on a large-scale charging station according to claim 11, characterized in that: The vehicle charging scheduling module includes the establishment of a charging demand prediction model, dynamic evaluation of charging priority, and dynamic allocation of charging power. The evaluation formula for dynamic assessment of charging priority is as follows: Priority score = λ1 × SOC urgency + λ2 × task criticality + λ3 × electricity price sensitivity + λ4 × battery health + λ5 × user level; where SOC urgency = 1 - SOC, λ1, λ2, λ3, λ4 and λ5 are weighting coefficients, which are set according to the actual situation, and λ1 + λ2 + λ3 + λ4 + λ5 = 1.

Images