Fuzzy Rule-Based Driver Advisory System for Fuel Economy Improvement in a Hybrid Electric Vehicle

Abstract

Environmental awareness has resulted in greater emphasis on developing more environmentally friendly and fuel efficient vehicles in automotive field. Hybrid electric vehicles (HEVs) are a viable option towards achieving these goals. Ford Motor Company developed a full HEV with an e-CVT (electronically controlled continuously variable transmission), which is a power-split hybrid system power train with an integrated motor and generator. This power train exhibits great potential to improve fuel economy. Achievement of high fuel economy, however, significantly depends on the driver behavior which plays a crucial role in the full utilization of the advantages of the HEV technology. In this paper we discuss an intelligent fuzzy advisory system called the "Fuzzy Rule-Based Driver Advisory System" for Fuel Economy Improvement that automatically identifies driver's style, intentions, and preferences and provides guidance to the driver for selecting the optimal driving strategy that results in maximal fuel economy. The proposed advisory system consists of two fuzzy logic controllers (FLCs) that determine the maximal driver demand corresponding to a desired fuel economy level under current operating conditions. The output of the controller is the dynamically calculated upper bounds of the driver demand that is continually conveyed to the driver. The system serves as an automatic advisor guiding the driver to a performance that maximizes the fuel economy without significantly reducing the vehicle's speed. This Fuzzy Rule-Based Driver Advisory System for Fuel Economy Improvement was tested in a simulation environment for a Ford Escape Hybrid. Simulations results demonstrated that the proposed driver advisory system improves the overall fuel economy of the HEV by up to 3.5% without significantly compromising vehicle performance.