Real-time energy management and sensitivity study for hybrid electric vehicles

This paper presents a real-time energy management algorithm for hybrid electrical vehicles (HEV). The proposed approach features a practical structure and manageable computation complexity for real-time implementation. It adopts a Model Predictive Control framework and utilizes the information attainable from Intelligent Transportation Systems (ITS) to establish a prediction based real-time controller structure. Simulations have been conducted with a Matlab/Simulink based vehicle model to assess the optimality of the algorithm, in comparison with existing control approaches. For real-time HEV control algorithms, ITS based driving prediction is an essential component. It is important to investigate the impact of the accuracy of ITS information on HEV energy consumption. In this work, we study the the effect of noises and errors in the velocity profile prediction under different control approaches. The sensitivity of the HEV energy use is investigated based on real driving data. The results provide better understanding of the need in driving profile prediction in real-time HEV control.