Energy-aware E-taxi Fleet Coordination under Power Rationing via Dynamic Charging Rate

Yukun Yuan, Zilong Zeng, Xiaonan Zhang, Shan Lin

Abstract: Existing electric taxi (e-taxi) services rely on charging infrastructure to maintain their daily operations. Unfortunately, severe power system disruptions, such as power rationing, can impose harsh constraints on e-taxi charging activities and significantly affect the service quality of e-taxis fleets. To address this issue, we design a framework for Energy-Aware e-taxi fleet coordination via dynamic charging Rate (EAR), to provide a satisfactory service quality while meeting energy conservation requirements. In this framework, an e-taxi fleet coordination algorithm is designed to provide sustainable service quality during pre-rationing, rationing and post-rationing phases. The coordination problem across the three phases is modeled as separate multi-objective mixed-integer linear problems due to the distinct objectives of each phase. The proposed solution is evaluated with a comprehensive dataset for an existing e-taxi system and charging infrastructures including nearly 800 e-taxis. Our data-driven evaluation shows that EAR improves the ratio of served passengers by 37.0% during the power rationing phase compared with the state-of-the-art method, which does not consider disruptions in charging infrastructure when coordinating e-taxis.