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Design of a hybrid energy management system using designed rule‐based control strategy and genetic algorithm for the series‐parallel plug‐in hybrid electric vehicle
Author(s) -
Ding N.,
Prasad K.,
Lie T. T.
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.5808
Subject(s) - matlab , electric vehicle , battery (electricity) , genetic algorithm , fitness function , energy management , plug in , energy management system , toolbox , engineering , automotive engineering , rule based system , energy (signal processing) , computer science , power (physics) , simulation , algorithm , mechanical engineering , statistics , physics , mathematics , quantum mechanics , machine learning , operating system , programming language
Summary Electric vehicle (EV) is considered as a critical requirement to the future development of transportation. However, the battery performance in terms of power density and energy density limits the use of EVs. An energy management system (EMS) of plug‐in hybrid electric vehicle (PHEV) is very critical to achieve successful transition from the conventional vehicle to the pure electric vehicle (PEV). This paper proposes a hybrid EMS for the series‐parallel PHEV utilising a rule‐based control strategy and genetic algorithm (GA)‐based optimisation technique to overcome the battery limitations. A mathematical model was developed and verified by conducting simulation studies using the vehicle model from ADVISOR database and the GA Optimization Toolbox (GAOT) in the Matlab Simulink environment. The simulation results show that the GA optimization successfully achieved the sub‐targets set in the fitness function. To show the effectiveness of the proposed technique, the results were compared with the simulation results of a single function of the designed rule‐based control strategy—the proposed EMS achieved a significant improvement in the hydrocarbon (HC) emission and NOx emission.