ABSTRACT As battery electric vehicles become increasingly common, the availability of high‐power charging stations for long‐distance travel has become critical. However, the expansion of charging infrastructure has not kept pace with rising demand, resulting in queues and wait times during peak periods. This paper presents a comprehensive analysis of the intention‐aware fixed‐route electric vehicle charging problem. We show that selfish routing algorithms, which optimize decisions at the individual‐vehicle level, often lead to suboptimal system‐wide performance. To address this, we propose an intention‐aware queuing system based on Markov models to improve charging station assignment. The system relies on vehicles submitting their planned charging intentions and, in return, provides wait time predictions that are integrated into a dynamic programming‐based charging strategy optimization framework. We evaluate the effectiveness of the approach through a large‐scale simulation using real‐world electric vehicle mobility data from a major German automotive manufacturer. The simulation includes up to 7500 simultaneously operating vehicles across Germany and is implemented in MATSim. The results show reductions in waiting times of up to 96%, demonstrating substantial improvements in overall system performance and efficiency. These findings highlight the importance of collaborative and coordinated strategies for optimizing electric vehicle charging and routing under high demand conditions.
Widmann et al. (Thu,) studied this question.