Zhengke Liu, Yunpeng Wang, Sonia Yeh, Patrick Plötz, Bin Yu, Xiaolei Ma
{"title":"基于深度强化学习的电动汽车充电网络弹性共享充电","authors":"Zhengke Liu, Yunpeng Wang, Sonia Yeh, Patrick Plötz, Bin Yu, Xiaolei Ma","doi":"10.1016/j.eng.2025.09.011","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"64 1","pages":""},"PeriodicalIF":11.6000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Leveraging Battery Electric-Bus Charging Networks for Resilient Shared EV Charging via Deep Reinforcement Learning\",\"authors\":\"Zhengke Liu, Yunpeng Wang, Sonia Yeh, Patrick Plötz, Bin Yu, Xiaolei Ma\",\"doi\":\"10.1016/j.eng.2025.09.011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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. 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Leveraging Battery Electric-Bus Charging Networks for Resilient Shared EV Charging via Deep Reinforcement Learning
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.
期刊介绍:
Engineering, an international open-access journal initiated by the Chinese Academy of Engineering (CAE) in 2015, serves as a distinguished platform for disseminating cutting-edge advancements in engineering R&D, sharing major research outputs, and highlighting key achievements worldwide. The journal's objectives encompass reporting progress in engineering science, fostering discussions on hot topics, addressing areas of interest, challenges, and prospects in engineering development, while considering human and environmental well-being and ethics in engineering. It aims to inspire breakthroughs and innovations with profound economic and social significance, propelling them to advanced international standards and transforming them into a new productive force. Ultimately, this endeavor seeks to bring about positive changes globally, benefit humanity, and shape a new future.