Xia, Jiaqi (Automotive Engineering Research Institute, Jiangsu University), Wang, Feng (Jiangsu University), Xu, Xing (Automotive Engineering Research Institute, Jiangsu University)

A Predictive Energy Management Strategy for Multi-Mode Plug-In Hybrid Electric Vehicle Based on Long Short-Term Memory Neural Network

Scheduled for presentation during the Regular session "Optimization and control for electrified vehicles" (MoBT2), Monday, August 23, 2021, 17:10−17:30, 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 25, 2024

Abstract

The plug-in hybrid electric vehicles (PHEV) provide a promising solution to increasingly severe tailpipe pollution and make possible the high-efficiency utilization of energy. To strengthen the overall fuel economy of a multi-mode PHEV, this paper proposes a real-time predictive energy management strategy (EMS) based on the Long Short-term Memory (LSTM) neural network. In order to forecast the short-term vehicle velocity with speed of previous time steps, a LSTM network is built and trained using speed profile of multiple representative driving cycles; a model predictive control architecture solved by dynamic programming (DP) in prediction horizon is also structured to realize successive online optimization of power allocation between the internal combustion engine and electric motors. The simulation of proposed strategy, convention adaptive equivalent consumption minimization strategy (A-ECMS) and the offline global optimization are carried out with UDDS and HWFET cycles to investigate the effectiveness and adaptivity of the proposed strategy, the result shows that the near-optimality of fuel economy is realized, and the fuel economy is elevated compared with the A-ECMS, therefor the potential for practical application of the proposed strategy is proved.