Reinforcement learning based energy management systems and hydrogen refuelling stations for fuel cell electric vehicles: An overview

This paper examines the current state of the art of hydrogen refuelling stations-based production and storage systems for fuel cell hybrid electric vehicles (FCHEV). Nowadays, the emissions are increasing rapidly due to the usage of fossil fuels and the demand for hydrogen refuelling stations (HRS) is emerging to replace the conventional vehicles with FCHEVs. Hence, the availability of HRS and its economic aspects are discussed. In addition, a comprehensive study is presented on the energy storage systems such as batteries, supercapacitors and fuel cells which play a major role in the FCHEVs. An energy man-agement system (EMS) is essential to meet the load requirement with effective utilisation of power sources with various optimizing techniques. A detailed comparative analysis is presented on the merits of Reinforcement learning (RL) for the FCHEVs. The significant challenges are discussed in depth with potential solutions for future work.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper examines the current state of the art of hydrogen refuelling stations-based production and storage systems for fuel cell hybrid electric vehicles (FCHEV). The availability and economic aspects of hydrogen refuelling stations are discussed, along with a comprehensive study on energy storage systems in FCHEVs such as batteries, supercapacitors, and fuel cells. The paper also highlights the importance of energy management systems and reinforcement learning in FCHEVs, and discusses the significant challenges and potential solutions for future work.