Energy Management Optimization Method of Plug-In Hybrid-Electric Bus Based on Incremental Learning

Abstract

The state of charge (SOC) trajectory planning for the power battery is the basis of realizing the global online optimal control of the plug-in hybrid-electric bus (PHEB) energy management system. This article proposed an adaptive rolling planning method for SOC trajectory of PHEBs power battery. First, a mathematical model for simulation research is established. Second, the driving cycles of PHEBs are collected, and the optimal SOC trajectories of driving cycles are obtained using dynamic programming (DP). In order to provide training data for the planning model of the optimal SOC trajectory of a trip segment (TS), the driving cycle and the optimal SOC trajectory of the driving cycle are cut into segments with a time length of 30 s. The incremental learning is used to construct a planning model of the TS’s optimal SOC trajectory and planning the optimal SOC trajectory of the actual TS. The optimal SOC trajectory of the actual TS is applied to proportion–integration-differentiation (PID) control and compared with PID based on mileage-based SOC planning (BMP). The result shows that this study can reduce fuel consumption by 0.8 L/100 km for the same driving cycle. Through incremental learning, the fuel consumption can be further reduced by 0.13 L/100 km.