OPTIMIZING HYBRID ELECTRIC VEHICLE ENERGY UTILIZATION BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM

Abang Ahmad Latif, Abang Sarbini (2023) OPTIMIZING HYBRID ELECTRIC VEHICLE ENERGY UTILIZATION BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM. [Final Year Project Report] (Unpublished)

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Abstract

Hybrid electric technology has been adapted in automobiles for over a few decades with the introduction of the Toyota Prius in 1997. The need to shift from conventional vehicles to hybrid vehicles ever since the declaration of peak oil in the world has driven car manufacturers. There are challenges in designing a vehicle, albeit improving a conventional vehicle to a hybrid electric vehicle (HEV) and achieving the optimal power balance between power sources which are the internal combustion engine (ICE) and the electric motor (EM). The introduction of more recently developed algorithms into energy management strategy (EMS) applications, particularly offline applications such as the one incorporated in this project have accelerated the field of energy management research for HEV. The adaptation of open-sourced simulation was selected for cost-effectiveness and possibilities of modification. The outcome of this project is to maximise the efficiency of a HEV model in MATLAB-Simulink by considering fuel efficiency as the objective function. To evaluate the effectiveness of the proposed EMS, extensive simulations are performed using different drive cycles, considering various input parameters. The findings demonstrate that the proposed controller outperforms the original model in terms of energy utilization. The optimized EMS effectively manages the power flow considering factors such as distance and average engine speed, while ensuring that the battery state does not fall below the minimum threshold.

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