The objective of this work is to develop an optimal management strategy to improve the energetic efficiency of a hybrid electric vehicle. The strategy is built based on an extensive experimental study of mobility in order to allow trips recognition and prediction. For this experimental study, a dedicated autonomous acquisition system was developed. On working days, most trips are constrained and can be predicted with a high level of confidence. The database was built to assess the energy and power needed based on a static model for three types of cars. It was found that most trips could be covered by a 10 kWh battery. Regarding the optimization strategy, a novel real time capable energy management approach based on dynamic vehicle model was created using Energetic Macroscopic Representation. This real time capable energy management strategy is done by a combination of cycle prediction based on results obtained during the experimental study. The optimal control strategy for common cycles based on dynamic programming is available in the database. When a common cycle is detected, the pre-determined optimum strategy is applied to the similar upcoming cycle. If the real cycle differs from the reference cycle, the control strategy is adapted using quadratic programming. To assess the performance of the strategy, its resulting fuel consumption is compared to the global optimum calculated using dynamic programming and used as a reference; its optimality factor is above 98%.
Smart Energy Management for Series Hybrid Electric Vehicles Based on Driver Habits Recognition and Prediction
