A real-time blended energy management strategy of plug-in hybrid electric vehicles considering driving conditions. (10th April 2020) - Lei, Zhenzhen; Qin, Datong; Zhao, Pan; Li, Jie; Liu, Yonggang; Chen, Zheng
Abstract: In this study, a blended energy management strategy considering influences of driving conditions is proposed to improve the fuel economy of plug-in hybrid electric vehicles. To attain it, dynamic programming is firstly applied to solve and quantify influences of different driving conditions and driving distances. Then, the driving condition is identified by the K-means clustering algorithm in real time with the help of Global Positioning System and Geographical Information System. A blended energy management strategy is proposed to achieve the real-time energy allocation of the powertrain with incorporation of the identified driving conditions and the extracted rules, which includes the engine starting scheme, gear shifting schedule and torque distribution strategy. Simulation results reveal that the proposed strategy can effectively adapt to different driving conditions with the dramatic improvement of fuel economy and the decrement of calculation intensity and highlight the feasibility of real-time implementation. Highlights: Dynamic programming is applied to quantify influences of driving conditions. Driving conditions are identified by the K-means clustering algorithm in real time. A blended real-time strategy is proposed considering driving conditions.