Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations

Hybrid electric vehicle technologies emerge mainly because of the instability in fossil fuel prices, resources and the terrible impact of global warming. As most transport systems use fossil fuel and emit greenhouse gases, many researchers have studied the potential of fuel-cell hybrid electric vehicles (FCHEVs). FCHEVs are vehicles with zero greenhouse gas emission because they only depend on hydrogen. Numerous studies have proven that fuel cells with energy storage elements can provide sufficient energy required by FCHEVs. However, end users demand FCHEVs that are not only efficient in delivering the energy required but can also optimize hydrogen consumption and prolong battery lifetime to compete with current internal combustion engine vehicles. Therefore, advanced optimization algorithms for an FCHEV energy management system (EMS) must be developed to improve the performance efficiency of FCHEVs. This paper presents a critical review of the different types of FCHEV EMSs and their optimization algorithms to solve existing limitations and enhance the performance of future FCHEVs. Consequently, a comprehensive review on the major categories of FCHEV EMSs, such as proportional-integral-derivative controller, operational or state mode, rule-based or fuzzy logic, and equivalent consumption minimization strategies, are explained. This paper also describes optimization techniques such as linear programming, dynamic programming, Pontryagin's minimum principle, genetic algorithm, particle swarm optimization and rule-based logic optimization for the EMSs of FCHEVs. Furthermore, it focuses on the various factors and challenges of existing optimization algorithms, hydrogen fuel source, environment and safety, and economical and societal concerns, as well as provides recommendations for designing capable and efficient EMSs for FCHEVs. All the highlighted insights of this review will hopefully lead to increasing efforts toward the development of an advanced optimization algorithm for future FCHEV EMSs.