Extended range electric vehicle control strategy design and muti-objective optimization by genetic algorithm

Extended range electric vehicles (EREVs) provide the power required to drive the vehicle via power battery packs and an engine/generator unit. To make EREVs as efficient as possible, proper control strategy of its´ drive train is essential. This paper proposes an improved thermostat/power tracking switching drive train control strategy, which control parameters are optimized by using genetic algorithm. The objective is to minimize fuel consumption and emissions, as well as reducing battery power volatility. Simulations are performed over two different driving cycles including NEDC and FTP75 by contrasting the performance of the classical thermostat strategy and the proposed strategy. The results show that the proposed strategy not only achieve good driving performance but also reduce the fuel consumption, emissions as well as battery power volatility effectively.