An Adaptive Electric Vehicle Charging Management Strategy for Multi-Level Travel Demands

Shuai Zhang, Dong Guo (), Bin Zhou, Chunyan Zheng, Zhiqin Li and Pengcheng Ma ()
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Shuai Zhang: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Dong Guo: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Bin Zhou: State Key Laboratory of Intelligent Transportation System, Beijing 100088, China
Chunyan Zheng: School of Management, Shandong University of Technology, Zibo 255000, China
Zhiqin Li: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Pengcheng Ma: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China

Sustainability, 2025, vol. 17, issue 6, 1-48

Abstract: As the adoption of electric vehicles (EVs) continues to rise, the pressure on charging station resources has intensified, particularly under high-load conditions, where limited charging infrastructure struggles to meet the growing demand. Issues such as uneven resource allocation, prolonged charging wait times, fairness concerns among different user groups, and inefficient scheduling strategies have significantly impacted the overall operational efficiency of charging infrastructure and the user experience. Against this backdrop, the effective management of charging infrastructure has become increasingly critical, especially in balancing the diverse mobility needs and service expectations of users. Traditional charging scheduling methods often rely on static or rule-based strategies, which lack the flexibility to adapt to dynamic load environments. This rigidity hinders optimal resource allocation, leading to low charging pile utilization and reduced charging efficiency for users. To address this, we propose an Adaptive Charging Priority (ACP) strategy aimed at enhancing charging resource utilization and improving user experience. The key innovations include (1) dynamic adjustment of priority parameters for optimized resource allocation; (2) a dynamic charging station reservation algorithm based on load status and user arrival rates to prioritize high-priority users; (3) a scheduling strategy for low-priority vehicles to minimize waiting times for non-reserved vehicles; and (4) integration of real-time data with the DDPDQN algorithm for dynamic resource allocation and user matching. Simulation results indicate that the ACP strategy outperforms the FIFS and RFWDA strategies under high-load conditions (High-priority vehicle arrival rate: 22 EV/h, random vehicle arrival rate: 13 EV/h, maximum parking duration: 1200 s). Specifically, the ACP strategy reduces charging wait times by 96 s and 28 s, respectively, and charging journey times by 452 s and 73 s. Additionally, charging station utilization increases by 19.5% and 11.3%. For reserved vehicles, the ACP strategy reduces waiting times and journey times by 27 s and 188 s, respectively, while increasing the number of fully charged vehicles by 104. For non-reserved vehicles, waiting and journey times decrease by 213 s and 218 s, respectively, with a 75 s increase in fully charged vehicles. Overall, the ACP strategy outperforms traditional methods across several key metrics, demonstrating its advantages in resource optimization and scheduling.

Keywords: electric vehicles; EV charging; travel demand; charging priority; charging optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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