Reinforcement learning based energy management systems and hydrogen refuelling stations for fuel cell electric vehicles: An overview期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

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

Affiliation:	School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Abstract:	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 management 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.

Keywords:	Hydrogen refuelling station Fuel cell hybrid electric vehicle Energy storage systems Energy management systems Reinforcement learning Deep Q learning BEV"} {"#name":"keyword" "$":{"id":"kwrd0045"} "$$":[{"#name":"text" "_":"Battery Electric Vehicles EMS"} {"#name":"keyword" "$":{"id":"kwrd0055"} "$$":[{"#name":"text" "_":"Energy Management Systems ESS"} {"#name":"keyword" "$":{"id":"kwrd0065"} "$$":[{"#name":"text" "_":"Energy Storage Systems FCEVs"} {"#name":"keyword" "$":{"id":"kwrd0075"} "$$":[{"#name":"text" "_":"Fuel Cell Hybrid Electric Vehicles FC"} {"#name":"keyword" "$":{"id":"kwrd0085"} "$$":[{"#name":"text" "_":"Fuel Cell GG"} {"#name":"keyword" "$":{"id":"kwrd0095"} "$$":[{"#name":"text" "_":"Greenhouse Gases HRS"} {"#name":"keyword" "$":{"id":"kwrd0105"} "$$":[{"#name":"text" "_":"Hydrogen Refuelling Station RB"} {"#name":"keyword" "$":{"id":"kwrd0115"} "$$":[{"#name":"text" "_":"Rule Based RES"} {"#name":"keyword" "$":{"id":"kwrd0125"} "$$":[{"#name":"text" "_":"Renewable Energy Sources SOC"} {"#name":"keyword" "$":{"id":"kwrd0135"} "$$":[{"#name":"text" "_":"State of Charge SC"} {"#name":"keyword" "$":{"id":"kwrd0145"} "$$":[{"#name":"text" "_":"Super Capacitor UC"} {"#name":"keyword" "$":{"id":"kwrd0155"} "$$":[{"#name":"text" "_":"Ultra Capacitor RL"} {"#name":"keyword" "$":{"id":"kwrd0165"} "$$":[{"#name":"text" "_":"Reinforcement Learning
本文献已被 ScienceDirect 等数据库收录！

设为首页 | 免责声明 | 关于勤云 | 加入收藏