Yanfang, Liu (Beihang university), Jun wei, Zhao (Beihang university), Songlin, Li (Beihang university), Peng, Dong (Beihang university), Shuhan, Wang (Beihang university), Xu, Xiangyang (Beihang University)

Adaptive Energy Management for Plug-In Hybrid Electric Vehicles Considering Real-Time Traffic Information

Scheduled for presentation during the Regular session "Optimization and control for electrified vehicles" (MoBT2), Monday, August 23, 2021, 17:30−17:50, Room T2

6th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, August 23-25, 2021, Tokyo, Japan

This information is tentative and subject to change. Compiled on April 24, 2024

Abstract

In order to achieve comprehensive fuel economy for plug-in hybrid electric vehicles (PHEVs) considering real-time traffic information, this paper proposes an A-ECMS energy management strategy with adaptive equivalent factor (EF) based on ANFIS, which can adaptively adjust to the predicted reference curve of the battery state of charge (SOC). Meanwhile, the Adaptive Network-based Fuzzy Inference System (ANFIS) model was used to train the SOC consumption curve under different driving cycles, so that the vehicle can calculate the SOC reference curve in real time with the traffic information, which ensured that the EF could be adjusted based on the actual driving cycles. Finally, the energy management strategy model proposed was simulated. The results show that the SOC consumption curve with adaptive EF adjustment is basically consistent with the SOC reference curve. The proposed A-ECMS energy management strategy based on ANFIS cannot only effectively use energy, but also take the real-time calculation of traffic information into consideration.