Rostami Seyed Mehdi Rakhtala, Al-Shibaany Zeyad, Kay Peter, Karimi Hamid Reza
School of Engineering, University of the West of England Bristol, Bristol, UK.
Computer Engineering Department, University of Technology, Baghdad, Iraq.
Sci Rep. 2024 Dec 28;14(1):30917. doi: 10.1038/s41598-024-81769-1.
Hydrogen-based electric vehicles such as Fuel Cell Electric Vehicles (FCHEVs) play an important role in producing zero carbon emissions and in reducing the pressure from the fuel economy crisis, simultaneously. This paper aims to address the energy management design for various performance metrics, such as power tracking and system accuracy, fuel cell lifetime, battery lifetime, and reduction of transient and peak current on Polymer Electrolyte Membrane Fuel Cell (PEMFC) and Li-ion batteries. The proposed algorithm includes a combination of reinforcement learning algorithms in low-level control loops and high-level supervisory control based on fuzzy logic load sharing, which is implemented in the system under consideration. More specifically, this research paper establishes a power system model with three DC-DC converters, which includes a hierarchical energy management framework employed in a two-layer control strategy. Three loop control strategies for hybrid electric vehicles based on reinforcement learning are designed in the low-level layer control strategy. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) is used with a network. Three DRL controllers are designed using the hierarchical energy optimization control architecture. The comparative results between the two strategies, Deep Reinforcement Learning and Fuzzy logic supervisory control (DRL-F) and Super-Twisting algorithm and Fuzzy logic supervisory control (STW-F) under the EUDC driving cycle indicate that the proposed model DRL-F can ensure the Root Mean Square Error (RMSE) reduction for 21.05% compared to the STW-F and the Mean Error reduction for 8.31% compared to the STW-F method. The results demonstrate a more robust, accurate and precise system alongside uncertainties and disturbances in the Energy Management System (EMS) of FCHEV based on an advanced learning method.
诸如燃料电池电动汽车（FCHEV）之类的氢基电动汽车在实现零碳排放以及同时减轻燃油经济性危机带来的压力方面发挥着重要作用。本文旨在针对各种性能指标进行能量管理设计，例如功率跟踪和系统精度、燃料电池寿命、电池寿命，以及降低聚合物电解质膜燃料电池（PEMFC）和锂离子电池上的瞬态和峰值电流。所提出的算法包括在低级控制回路中结合强化学习算法以及基于模糊逻辑负载共享的高级监督控制，该算法在所考虑的系统中得以实现。更具体地说，本研究论文建立了一个带有三个DC-DC转换器的电力系统模型，其中包括采用两层控制策略的分层能量管理框架。在低级层控制策略中设计了基于强化学习的混合动力电动汽车的三种回路控制策略。采用带有网络的双延迟深度确定性策略梯度（DDPG TD3）。使用分层能量优化控制架构设计了三个深度强化学习（DRL）控制器。在欧盟城市驾驶循环（EUDC）下，深度强化学习与模糊逻辑监督控制（DRL-F）以及超扭曲算法与模糊逻辑监督控制（STW-F）这两种策略之间的对比结果表明，所提出的DRL-F模型与STW-F相比，可确保均方根误差（RMSE）降低21.0％，与STW-F方法相比，平均误差降低8.31％。结果表明，基于先进学习方法的FCHEV能量管理系统（EMS）在存在不确定性和干扰的情况下，具有更强大、准确和精确的系统。
