Introducing predictive Best-Mode controller for minimizing hybrid electric vehicle fuel consumption

Abstract

In this paper, a real-time predictive control strategy is developed to control the energy consumption of hybrid electric vehicles with lower sensitivity to prediction accuracy. A predictive Best-Mode concept is introduced based on the future speed predictions, by which the trend of battery state of charge is estimated. The estimated battery state of charge is used to better management of the battery charge mode. The optimum work zones of the components are then selected according to the best battery charging mode and the vehicle speed and power demand. This controller is less sensitive to the prediction accuracy and enables the system to work at the near-optimal points. The results show that the predictive Best-Mode controller is capable of minimizing the energy consumption in real-time applications, very close to the results of the offline dynamic programming with a 2% error margin. The predictive Best-Mode strategy's performance is better than the finite-horizon dynamic programming, except for accurate prediction with a longer than 20-sec prediction horizon.