Driver Modeling for Heavy Hybrid Vehicle Energy Management

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).