ABSTRACT

The battery lifetime of Electric vehicles (EVs) is affected by hot temperatures and high charging and discharging effective battery current. Hybrid energy storage systems (HESS) coupling the best attributes of the battery with supercapacitors (SCs) help extend the battery lifetime and improve the EV storage performances. The key to a successful HESS at extending the battery lifetime is to adopt the appropriate Energy Management System (EMS) that ensures the best power sharing between battery and SCs. This paper proposes an innovative real-time optimization-based EMS with low computational costs and high adaptability to variable and commute driving profiles. The proposed EMS is organized in two levels. The lower level implements a rule-based frequency power sharing control. The upper level performs Reinforcement Learning (RL) optimizations to learn and adapt the best power sharing configuration considering real-time information and actual load conditions. An experimental test bench is developed and experimental measurements are conducted. The obtained results confirmed the effectiveness of the proposed EMS to provide the best trade-offs between simple implementation, computation time, solution optimality, real-time performance, and good adaption to variable driving conditions.

摘要

电动汽车（EV）的电池寿命会受到高温以及充放电有效电池电流的影响。混合动力储能系统（HESS）将电池的最佳特性与超级电容器（SC）结合在一起，有助于延长电池寿命并改善电动汽车的存储性能。成功的HESS延长电池寿命的关键是采用适当的能源管理系统（EMS），以确保电池和SC之间的最佳功率共享。本文提出了一种创新的基于实时优化的EMS，它具有较低的计算成本和对可变和通勤驾驶模式的高度适应性。拟议的环境管理体系分为两个层次。下层实现基于规则的频率功率共享控制。上层执行增强学习（RL）优化，以考虑实时信息和实际负载条件来学习和调整最佳功率共享配置。开发了一个实验测试台并进行了实验测量。获得的结果证实了所提出的EMS在简单实施，计算时间，解决方案最优性，实时性能以及对可变驾驶条件的良好适应性之间提供最佳折衷的有效性。