Adaptive real-time energy management control strategy based on fuzzy inference system for plug-in hybrid electric vehicles

Abstract

A novel control strategy for the adaptive real-time energy management of a commuter pull-in hybrid vehicle is proposed. The proposed strategy can adapt to various driving conditions so that fuel economy can be improved further in practice. Its main feature is that a fuzzy inference system (FIS) for online estimation of the reference SOC and an adaptive update law with traffic recognition are blended into the main frame of an adaptive-equivalent consumption minimization strategy (A-ECMS). The FIS is established through an adaptive neuro-fuzzy inference system (ANFIS) that is offline trained by the traffic information extracted from historical traffic data and the reference state of charge (SOC) optimized by dynamic programming (DP). The adaptive update law with traffic recognition means that the adaptive equivalent factor (A-EF) of the real-time A-ECMS is adjusted online according to the traffic information in the real route besides the SOC of the vehicle battery. This is because the initial A-EF and the proportional–integral coefficients of the A-EF adjuster are mappings of the SOC and the traffic road segment, and the mappings are optimized by particle swarm optimization (PSO) according to the different initial SOC and the real historical driving cycles of each segment. The proposed strategy is carried out on the simulation test platform integrated GT-Suite simulator and MATLAB/Simulink. The simulation results show that the proposed strategy can reach an optimal energy distribution on a near global optimal level (close to the level of dynamic programming (DP) under the deterministic driving condition). Compared with a rule-based (RB) strategy, the traditional ECMS, an A-ECMS with the linear SOC reference, an A-ECMS with the EF optimized by PSO and an A-ECMS with the A-EF adjusted by a fixed PI feedback controller of the SOC, the fuel consumption is reduced by an average of 22.98% 10.26% 6.52% 2.33% and 5.91% respectively.