Energy Storage and Conversion of Electric Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electromechanical Energy Conversion Systems".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1229

Special Issue Editors


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Guest Editor

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Guest Editor
Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
Interests: transportation electrification; wireless power transfer; electric machines

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Guest Editor
School of Electrical Engineering, Shandong University, Jinan, China
Interests: control and optimal energy management of microgrid systems incorporating vehicle to grid technology
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Special Issue Information

Dear Colleagues,

The electrification of transportation plays a crucial role in global decarbonization efforts. Electric vehicles (EVs) including hydrogen fuel cell vehicles are a key facilitator of this transition. Advances in energy storage technology and energy conversion technology are central to the performance improvement, reliability, and sustainability of EVs. This Special Issue aims to attract cutting-edge research in the design, modelling, optimisation, and integration of energy storage systems (batteries and fuel cells), and energy conversion interfaces in EVs. The topics of interests include, but are not limited to, the following: battery energy storage systems (BESSs), hydrogen fuel cell stacks, bidirectional converters, battery and fuel cell management systems (BMS and FCMS), charging and refuelling infrastructure, thermal management, and novel hybrid energy conversion architectures.

Contributions exploring vehicle-to-grid (V2G) strategies, hydrogen-to-grid concepts, hybrid energy systems, and comprehensive lifecycle assessments are also welcome. The goal is to provide a platform for academia and industry to share insights, innovations, and challenges in developing next-generation electrified transportation technologies.

Dr. Amin Mahmoudi
Dr. Solmaz Kahourzade
Dr. Jamal Yousuf Alsawalhi
Dr. Arshad Nawaz
Guest Editors

Manuscript Submission Information

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Keywords

  • electric vehicles (EVs)
  • fuel cell electric vehicles (FCEVs)
  • energy storage systems
  • battery management systems (BMSs)
  • fuel cell technologies and management systems (FCMSs)
  • bidirectional converters
  • vehicle-to-grid (V2G) and hydrogen-to-grid (H2G)
  • charging and hydrogen refuelling infrastructure
  • thermal management
  • powertrain optimisation
  • hybrid energy systems

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Published Papers (1 paper)

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Research

30 pages, 11506 KB  
Article
A Health-Aware Fuzzy Logic Controller Optimized by NSGA-II for Real-Time Energy Management of Fuel Cell Electric Commercial Vehicles
by Juan Du, Xuening Zhang, Shanglin Wang and Xiaodong Liu
Machines 2025, 13(11), 1048; https://doi.org/10.3390/machines13111048 - 13 Nov 2025
Cited by 1 | Viewed by 712
Abstract
This study introduces a health-aware fuzzy logic (FL) energy management strategy (EMS) for fuel cell electric commercial vehicles (FCECVs) that aimed to improve energy efficiency and extending fuel cell system (FCS) lifespan. The FL-based EMS was developed using vehicle power demand and battery [...] Read more.
This study introduces a health-aware fuzzy logic (FL) energy management strategy (EMS) for fuel cell electric commercial vehicles (FCECVs) that aimed to improve energy efficiency and extending fuel cell system (FCS) lifespan. The FL-based EMS was developed using vehicle power demand and battery state of charge (SOC) as inputs, with the FCS power change rate as the output, aiming to mitigate degradation induced by abrupt load transitions. A multi-objective optimization framework was established to optimize the fuzzy logic controller (FLC) parameters, achieving a balanced trade-off between fuel economy and FCS longevity. The non-dominated sorting genetic algorithm-II (NSGA-II) was utilized for optimization across various driving cycles, with average Pareto-optimal solutions employed for real-time application. Performance evaluation under standard and stochastic driving cycles benchmarked the proposed strategy against dynamic programming (DP), charge-depletion charge-sustaining (CD-CS), conventional FL strategies, and a non-optimized baseline. Results demonstrated an approximately 38% reduction in hydrogen consumption (HC) relative to CD-CS and over 75% improvement in degradation mitigation, with performance superior to that of DP. Although the strategy exhibits an average 17.39% increase in computation time compared to CD-CS, the average single-step computation time is only 2.1 ms, confirming its practical feasibility for real-time applications. Full article
(This article belongs to the Special Issue Energy Storage and Conversion of Electric Vehicles)
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