Design of a hybrid energy management system using designedrule-basedcontrol strategy and genetic algorithm for the series-parallel plug-in hybrid electric vehicle

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.

Automated summary

This paper introduces a hybrid EMS system for PHEV, using a rule-based control strategy and genetic algorithm optimization technique to overcome battery limitations. Simulation studies have shown significant improvements in emissions control with the proposed system.