Optimized fuzzy logic control strategy of hybrid vehicles under different driving cycle

This paper focuses on the control strategy of Hybrid Vehicles under different driving cycle. FLC is mainly based on the process knowledge and intuition. In comparison, the Adaptive Neural-Fuzzy Inference System (ANFIS) is a modeling method, which primarily based on data. It is presented here that the membership functions and rules of FLC could be optimized once ANFIS is trained by actual driving cycle data collected from software ADVISOR. Then the FLC controller block in ADVISOR is rewritten by the optimized membership functions according to the ANFIS training. And we choose two different driving cycles for comparison to improve the effectiveness of the method. Some simulation results are compared and discussed: the optimized FLC exhibits better performance in terms of fuel consumption and pollutants emission.