An Optimal Method for Energy Management in a Parallel Hybrid Electric Vehicle

Abstract—In this paper, an optimal energy management tool based on neuro-fuzzy network is proposed for controlling a Hybrid Electric Vehicle (HEV) with parallel structure. In the proposed method, the desired torque for driving vehicle and also the State of Charge (SOC) of batteries are used as inputs, and the throttle in the combustion engine is regulated by the output of the proposed controller. The power flow controller for both mechanical and electrical path is the key point for an efficient driving. The aim of this study is to reduce the fuel consumption and exhaust emissions by optimum splitting the power between above mentioned dual sources in order to have a high efficiency operation mode. Vehicle performance was analyzed by ADVISOR simulator. The results obtained from the proposed controller are compared with the Parallel Electric Assistant (PEA) and Fuel-Minimizing (Fuel-Mode) control strategy. Based on the simulation results, it is shown that remarkable reduction in fuel consumption and also exhaust emissions was achieved.

Keywords:

Parallel Hybrid Electric Vehicle (PHEV) , Adaptive Neural-Fuzzy Inference System (ANFIS) , improved fuzzy logic controller , optimizing fuel consumption , reducing exhaust emission