A Novel Machine Learning-Based Online Optimal Control Strategy for Fuel Cell in Electrified Transportation System

Proton exchange membrane fuel cells (PEMFCs) have been widely used as clean energy storage devices in electrified transportation system. The key challenges in the existing control approaches of PEMFCs include model dependence, the usage of non-optimal control policy and the reliance on offline-trained neural networks. To address these challenges, this paper proposes a novel machine learning-based optimal control strategy for the PEMFC in electrified transportation system. Furthermore, the proposed method employs a recurrent neural network (RNN) to successfully avoid the problem of slow or even no convergence that may be caused in recursive least square-based neural network weights updating process. It offers excellent control performance with guaranteed convergence and stability. The superiority of the proposed method is validated through Hardware-in-the-Loop (HIL) tests.