A bi-decision model for electric vehicle dispatch and battery swapping station charging schedule problem
Yong Su1, Shishun Tian2, Hao Wu1
1Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, Guangdong, China.
Scientific Reports
|July 8, 2025
Summary
This study introduces a novel bi-decision model for electric vehicle (EV) battery swapping stations (BSSs), optimizing EV driver time and BSS operational costs. The model uses advanced algorithms to manage EV dispatch and charging schedules effectively.
Area of Science:
- Operations Research
- Electrical Engineering
- Computer Science
Background:
- The growing adoption of electric vehicles (EVs) necessitates efficient battery swapping station (BSS) management.
- Existing BSS models often address dispatching or charging schedules in isolation, failing to capture real-world complexities and optimize outcomes for both EV drivers and operators.
Purpose of the Study:
- To develop an integrated bi-decision model for EV dispatch and BSS charging schedule optimization.
- To minimize average extra time for EV drivers while optimizing electricity costs, battery degradation, and power load variance for BSS operators.
Main Methods:
- Proposed a bi-decision model where the EV dispatch solution informs the BSS charging schedule.
- Developed an adaptive tabu search (ATS) algorithm for the EV dispatch problem, considering extra time, battery availability, and queue lengths.
- Implemented a multi-objective particle swarm optimization (MOPSO) algorithm for the complex BSS charging schedule problem to find Pareto-optimal solutions.
Main Results:
- The proposed bi-decision model demonstrated superior performance compared to traditional rule-based strategies like nearest-in-range.
- Experimental results visualized through Gantt charts highlighted reduced waiting times for EVs and efficient scheduling.
- A comprehensive comparison confirmed the effectiveness and competitiveness of both the ATS and MOPSO algorithms.
Conclusions:
- The integrated bi-decision model offers a more realistic and optimal approach to managing EV battery swapping stations.
- The developed evolutionary algorithms (ATS and MOPSO) provide efficient solutions for the NP-hard problems of EV dispatch and BSS charging scheduling.
- This research contributes to improving the efficiency and economic viability of EV charging infrastructure.
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