用强化学习优化退役电池在充电桩的使用，降低成本。
Deep Reinforcement Learning-Based Optimization of Second-Life Battery Utilization in Electric Vehicles Charging Stations
- 基于深度强化学习构建充电桩调度框架，用退役电池替代新电池。
- 模型训练覆盖全年数据，可应对节假日和季节性用电波动。
- 适合关注新能源车后端资源利用与智能电网管理的读者。
随着电动汽车（EV）普及，大量退役电池带来管理挑战。研究显示，电动车退役电池仍保留显著剩余容量，可有效用于充电站储能系统（BESS），替代新电池并降低整体规划成本。将退役电池（SLBs）集成至充电站（EVCS）是缓解系统过载的可行策略。然而，受电动汽车到离峰时间波动及电网电价变化等不确定性影响，高效运行仍面临难题。本文提出一种基于深度强化学习（DRL）的充电站优化框架，采用先进的软动作-评价者（SAC）算法，在包含工作日与节假日的全年数据上进行训练，以适应季节性变化。通过定制化奖励函数实现离线训练，支持在不确定环境下实时优化充电站运行。
原文摘要 · Abstract (English)
The rapid rise in electric vehicle (EV) adoption presents significant challenges in managing the vast number of retired EV batteries. Research indicates that second-life batteries (SLBs) from EVs typically retain considerable residual capacity, offering extended utility. These batteries can be effectively repurposed for use in EV charging stations (EVCS), providing a cost-effective alternative to new batteries and reducing overall planning costs. Integrating battery energy storage systems (BESS) with SLBs into EVCS is a promising strategy to alleviate system overload. However, efficient operation of EVCS with integrated BESS is hindered by uncertainties such as fluctuating EV arrival and departure times and variable power prices from the grid. This paper presents a deep reinforcement learning-based (DRL) planning framework for EV charging stations with BESS, leveraging SLBs. We employ the advanced soft actor-critic (SAC) approach, training the model on a year's worth of data to account for seasonal variations, including weekdays and holidays. A tailored reward function enables effective offline training, allowing real-time optimization of EVCS operations under uncertainty.
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