Gu, Wen (Loughborough University), Zhao, Dezong (Loughborough University), Mason, Byron (Loughborough University)

A Review of Intelligent Road Preview Methods for Energy Management of Hybrid Vehicles

Scheduled for presentation during the Invited Session "AI / Intelligent Vehicle Control and Calibration" (ThAT3), Thursday, June 27, 2019, 10:50−11:10, Chamerolles

9th IFAC International Symposium on Advances in Automotive Control, June 23-27, 2019, Orléans, France

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

Due to the shortage of fuel resources and concerns of environmental pressure, vehicle electrification is a promising trend. Hybrid vehicles are suitable alternatives to traditional vehicles. Travelling information is essential for hybrid vehicles to design the optimal control strategy for fuel consumption minimization and emissions reduction. In general, there are two ways to provide the information for the energy management strategy (EMS) design. First is extracting terrain information by utilizing global positioning system (GPS) and intelligent transportation system (ITS). However, this method is difficult to be implemented currently due to the computational complexity of extracting information. This leads to the second method which is predicting future vehicle speed and torque demand in a certain time horizon based on current and previous vehicle states. To support optimal EMS development, this paper presents a comprehensive review of prediction methods based on different levels of trip information for the EMS of hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV).