A novel perspective of energy management strategies on multistack fuel cell hybrid electric vehicles: Trends and challenges

Ghaderi, R., Kandidayeni, M., Boulon, L. et Trovão, J. P. (2024). A novel perspective of energy management strategies on multistack fuel cell hybrid electric vehicles: Trends and challenges. IEEE Intelligent Transportation Systems Magazine . ISSN 1941-1197 DOI 10.1109/MITS.2024.3479694

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Résumé

Abstract

Multistack fuel cell hybrid electric vehicles (MFCHEVs) are promising for heavy-duty applications due to their increased power, redundancy, and extended lifespan. However, managing their diverse power sources necessitates a robust energy management strategy (EMS). A significant gap exists in the literature concerning the connection of stacks in MFCHEVs, which critically impacts system performance. Designing an EMS for MFCHEVs is challenging due to this research gap. Recently, reinforcement learning (RL) has proven effective for real-time EMSs in multiagent frameworks. This article provides novel insights into developing EMSs for MFCHEVs using a multiagent approach. It reviews existing EMS gaps for MFCHEVs, introduces the multiagent EMS design concept, and examines RL’s role in addressing stack connection issues.

Type de document:	Article
Mots-clés libres:	Energy management Real-time systems Fuel cells Optimization Hybrid electric vehicles Batteries Topology Genetic algorithms Artificial neural networks Reviews
Date de dépôt:	24 mars 2025 15:22
Dernière modification:	24 mars 2025 15:22
Version du document déposé:	Post-print (version corrigée et acceptée)
URI:	https://depot-e.uqtr.ca/id/eprint/11758

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