Multi-objective Energy Management Strategy for PHEVs Based on Working Condition Information Prediction and Time-Varying Equivalence Factor ECMS

In order to improve the poor discharge problem that may be caused by the unreasonable power distribution relationship of the battery pack in hybrid vehicles due to the improvement of fuel economy, this paper carries out the research of energy management strategy based on multi-objective optimization for a parallel plug-in hybrid vehicle. The optimization objectives are the optimal fuel economy and the minimum temperature rise of the battery. A vehicle power system model is established to provide a simulation platform for the subsequent verification of the control strategy. A short-term operating condition prediction model is constructed based on the Markov process, ensuring the energy management strategy meets the power balance demand in the local time domain in the future. A multi-objective optimization algorithm is used to enhance the improvement of the traditional equivalent fuel consumption minimization strategy by means of the prediction of the operating conditions, and a real-time search for the optimization is employed by selecting the equivalent factor as the control variable. By selecting the equivalent factor as the control variable for real-time optimization, the optimal time-varying equivalent factor sequence based on multi-objective optimization is obtained, which improves the power distribution between the engine and the motor drive. The results show that the improved control strategy can well trade-off the engine fuel economy and battery temperature rise index, and has excellent battery SOC maintenance capability. While confirming the effectiveness of the strategy, it is verified that it has strong robustness and multi-case generalization capability.