Driver Modeling for Heavy Hybrid Vehicle Energy Management

Author

Stoev, Julian ; Hostens, Erik ; Vandenplas, Steve

Author_Institution

Flanders´ Mechatron. Technol. Centre, Leuven, Belgium

fYear

2014

fDate

27-30 Oct. 2014

Firstpage

1

Lastpage

6

Abstract

The paper presents an approach for modeling and predicting the user intentions with application for optimization of the hybrid electrical vehicle. An auto-regressive moving-average model isdesigned to model and predict the driver behavior. The resulting model is converted to a Markov-chain model and used with stochastic dynamic programming, which optimizes the gear-shifting and the power split between the internal combustion engine and the electrical storage of a hybrid electrical vehicle. Verification of resulting energy efficiency is performed using real-life driving data from a heavy-duty industrial vehicle (forklift).

Keywords

Markov processes; autoregressive moving average processes; dynamic programming; energy management systems; hybrid electric vehicles; internal combustion engines; stochastic programming; Markov-chain model; autoregressive moving-average model; driver behavior prediction; driver modeling; electrical storage; forklift; gear-shifting optimization; heavy hybrid vehicle energy management; heavy-duty industrial vehicle; hybrid electrical vehicle optimization; internal combustion engine; power split optimization; stochastic dynamic programming; user intention modeling; user intention prediction; Data models; Dynamic programming; Hybrid electric vehicles; Predictive models; Stochastic processes; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Vehicle Power and Propulsion Conference (VPPC), 2014 IEEE

Conference_Location

Coimbra

Type

conf

DOI

10.1109/VPPC.2014.7007051

Filename

7007051