Real-Time Electric Taxi Guidance for Battery Swapping Stations Under Dynamic Demand

Yu Feng (), Xiaochun Lu, Xiaohui Huang and Jie Ma
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Yu Feng: School of Economics and Management, Beijing Polytechnic University, Beijing 100176, China
Xiaochun Lu: School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
Xiaohui Huang: School of Economics and Management, Beijing Polytechnic University, Beijing 100176, China
Jie Ma: School of Economics and Management, Beijing Polytechnic University, Beijing 100176, China

Energies, 2025, vol. 18, issue 9, 1-16

Abstract: High battery swapping demand from electric taxis and drivers’ subjective station selection often leads to congestion and the uneven utilization of battery swapping stations (BSSs). Efficient vehicle guidance is essential for improving the operational performance of electric taxis. In this study, we have developed a vehicle-to-station guidance model that considers dynamic demand and diverse driver response-time preferences. We have proposed two decision-making strategies for BSS recommendations. The first is a real-time optimization method that uses a greedy algorithm to provide immediate guidance. The second is a delayed optimization framework that performs batch scheduling under high demand. It integrates a genetic algorithm with KD-tree search to handle dynamic demand insertion. A case study based on Beijing’s Fourth Ring Road network was conducted to evaluate the strategies under four driver preference scenarios. The results show clear differences in vehicle waiting times. A balanced consideration of travel distance, waiting time, and cost can effectively reduce delays for drivers and improve station utilization. This research provides a practical optimization approach for real-time vehicle guidance in battery swapping systems.

Keywords: battery swapping stations; vehicle guidance; real-time optimization; delayed optimization; dynamic demand (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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