Adaptive real-time advisory system for fuel economy improvement in a hybrid electric vehicle

In this paper, we present a fuzzy logic based adaptive algorithm with a learning mechanism that estimates driver's long term and short term preferences. The algorithm represents a significant advancement to the capability of our previous non-adaptive real-time fuel economy advisory system that was implemented in a Ford Escape Hybrid [8][9]. This real-time advisory system proposed in [8][9]achieved improved fuel economy by providing visual and haptic feedbacks to the driver to change his or her driving style or behavior for a given vehicle condition. It was tuned to maximize fuel economy without significantly impacting the performance of the vehicle. Some drivers may perceive it's feedback to be intrusive on one extreme while some other drivers may feel it ineffective on another extreme, depending on the driver's driving styles. The new adaptive algorithm learns driver's intentions by monitoring their driving styles and behaviors, and addresses the issues of intrusiveness of the advisory feedback. This proposed adaptive algorithm balances the competing requirements for improved fuel economy and drivability by maintaining vehicle performance that is acceptable to the current driver's driving style and behavior while providing mechanism to improve fuel economy. This system was developed and validated on the Ford Escape Hybrid vehicle. Experimental results show that the proposed adaptive algorithm is capable of improving driver's behavior and style without being perceived as ineffective or intrusive and achieves fuel economy improvements.