Driver Modeling for Heavy Hybrid Vehicle Energy Management

• Published in: 2014 IEEE Vehicle Power and Propulsion Conference (VPPC) pp. 1 - 6
• Main Authors: Stoev, Julian, Hostens, Erik, Vandenplas, Steve
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2014
• Subjects: Data models; Dynamic programming; Hybrid electric vehicles; Predictive models; Stochastic processes; Training
• ISSN: 1938-8756
• DOI: 10.1109/VPPC.2014.7007051

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).