Leveraging Battery Electric-Bus Charging Networks for Resilient Shared EV Charging via Deep Reinforcement Learning
Journal article, 2025

The rapid electrification of urban transportation has increased dependence on public electric-vehicle (EV) charging infrastructure, making it more vulnerable to frequent and severe disruptions. To address this issue, this study proposes utilizing underused battery electric-bus (BEB) charging networks by dynamically reallocating surplus depot chargers for public EV charging. We introduce an adaptive shared-charging coordination framework to increase the resilience of public charging services. This coordination problem is formulated as a Markov decision process (MDP) that jointly optimizes BEB charging schedules and shared charger allocation under uncertainty. To enable real-time decision-making without requiring precise forecasts of future system states, an on-policy deep reinforcement-learning (DRL) approach based on the asynchronous advantage actor-critic (A3C) algorithm is developed. A case study using real-world data from Beijing during a major urban flood demonstrates the effectiveness of the proposed adaptive shared-charging coordination framework. The results reveal that our approach significantly mitigates degradation in public charging service performance, accelerates recovery to normal operating levels, enhances user accessibility, and supports grid stability. Under an extreme scenario with only 25% of public chargers operational, the proposed strategy limits revenue losses to just 3.49%, compared with losses of 53.34% under conventional operations. Additionally, the A3C-based approach demonstrates notable training efficiency and achieves a favorable balance between short-term responsiveness and long-term system performance when benchmarked against a perfect-information optimization model, proximal policy optimization (PPO), and a greedy heuristic. These findings highlight the substantial potential of BEB charging networks as critical resilience resources for urban public EV charging infrastructure during extreme disruption events.

Deep reinforcement learning

Adaptive shared charging

Battery electric-bus charging networks

Resilience enhancement

Disruptive events

Author

Zhengke Liu

Beihang University

Yunpeng Wang

State Key Laboratory of Intelligent Transportation System

Beihang University

Sonia Yeh

Chalmers, Space, Earth and Environment, Physical Resource Theory

Patrick Plötz

Fraunhofer Society

Bin Yu

Ministry of Education China

Beihang University

Xiaolei Ma

Ministry of Education China

Beihang University

Engineering

2095-8099 (ISSN)

Vol. In Press

Driving Forces

Sustainable development

Areas of Advance

Transport

Subject Categories (SSIF 2025)

Transport Systems and Logistics

DOI

10.1016/j.eng.2025.09.011

More information

Latest update

11/17/2025