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Spatial-Temporal Distribution Prediction of Charging Load of Electric Truck Cluster Considering Battery Loss Under Path Planning
CUI Jinghao, ZHANG Yi, ZHANG Zhichao, YU Yang
Electric Power Construction ›› 2025, Vol. 46 ›› Issue (6) : 192-204.
PDF(4185 KB)
PDF(4185 KB)
Spatial-Temporal Distribution Prediction of Charging Load of Electric Truck Cluster Considering Battery Loss Under Path Planning
[Objective] This study addresses the problems of the impact of the high frequency and high-power charging of electric trucks on the stable operation of the power system and the insufficient number of charging stations. [Methods] First， the power consumption characteristics of electric trucks are modeled by considering the weather temperature， traffic flow， loaded cargo volume， terrain， and other factors. Second， a path planning model is constructed by using the charging cost of electric trucks and minimum cost of battery loss as the objective function， and a genetic algorithm is used to solve the path planning model according to the logistic order information to obtain dynamic paths. Finally， the Monte Carlo method is used to sample electric trucks randomly and obtain the spatial distribution of electric trucks， judge the charging strategy according to the time window and remaining state of charge of the point of arrival at the customer， and add up the electric truck charging loads in the region to determine the spatial and temporal distributions of the charging loads. The actual traffic network in Tangshan City was used to carry out the simulation validation. [Results] The results showed that， compared with the shortest path algorithm， the peak charging load of electric trucks decreased by 6%， the overall charging load decreased by 2%， and the travel cost decreased by 20，410 yuan overall after adopting the proposed path planning method， which reduced the impact on the grid and the user driving cost. In addition， the charging load was affected by seasonal temperatures， and the peak charging load in winter was 6.3% higher than that in summer. [Conclusion] The proposed load forecasting method has a certain degree of authenticity and rationality， and aligns with the real distribution paths of electric trucks.
electric truck / logistics characteristics / path planning / Monte Carlo simulation / load forecasting
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