LAUSR

Electric vehicles (EVs) are promising alternatives to carbonized propulsion-based vehicles. They are capable of reducing environmental degradation without compromising driving performance. Power management strategies (PMS) are particularly essential for electrified vehicles to ensure optimal power split between on-board energy storage sources and to meet operational requirements of each source. However, optimization concept in PMS, have been constantly addressed in literature to achieve optimal power handling decisions in real-time, particularly under unknown driving conditions. In this contribution, an intelligent rule-based PMS with embedded offline-optimized control parameters and online driving state recognition is proposed to achieve optimal power handling decisions for EVs situatively and adaptively. A set of characteristic variables defining driving states have been extracted from representative segments of several driving cycles, to which optimized control strategies are tuned offline. Three different driving cycles representing urban, highway, and mixed trip conditions have been implemented for comparative investigation of achieved results. The analysis of results reveals the potential of proposed PMS to reduce the energy consumption by 13.6 – 30.9 %.