Control strategy optimization of plug-in hybrid electric vehicle based on driving data mining

Abstract

SDP (Stochastic Dynamic Programming) control strategy can mine the travel data of drivers, so that the energy-saving potential of vehicles could be improved. However, there are two problems need to be solved in the current SDP: firstly, the analysis and construction method of driver’s typical driving cycle is not clear; secondly, the adaptability of SDP algorithm to a typical driving cycle is insufficient. To solve the above problems, a driving cycle construction method for off-line SDP solution is proposed, which is based on “analysis, dimension reduction and clustering” process. In addition, a coupled control strategy (ECMS-SDP) based on driving conditions identification is developed. Because it is difficult to predict the driving conditions in real time, the working part of the coupled control strategy is calculated by the method of improved random forest. The simulation results show that ECMS-SDP control strategy can save 8%–15% fuel on average compared with CD-CS control strategy, and can save 4%–7% fuel on average compared with ECMS control strategy. The results prove that the ECMS-SDP coupled control strategy can respond well to the changing driving environment, and the fuel economy of vehicles is enhanced.