Design and Analysis of an Adaptive Real-Time Advisory System for Improving Real World Fuel Economy in a Hybrid Electric Vehicle 2010-01-0835

Environmental awareness and fuel economy legislation has resulted in greater emphasis on developing more fuel efficient vehicles. As such, achieving fuel economy improvements has become a top priority in the automotive field. Companies are constantly investigating and developing new advanced technologies, such as hybrid electric vehicles, plug-in hybrid electric vehicles, improved turbo-charged gasoline direct injection engines, new efficient powershift transmissions, and lighter weight vehicles. In addition, significant research and development is being performed on energy management control systems that can improve fuel economy of vehicles.

Another area of research for improving fuel economy and environmental awareness is based on improving the customer's driving behavior and style without significantly impacting the driver's expectations and requirements. Ford Motor Company developed an adaptive real-time advisory system for fuel economy improvement in a hybrid electric vehicle that automatically identifies the driver's style, intentions and preferences and provides guidance through haptic and visual mechanisms to the driver for selecting the optimal driving strategy that results in maximum fuel economy. The adaptive real-time advisory system was validated on an experimental Ford Escape Hybrid vehicle using haptic and visual feedback mechanisms in an extensive test study to quantify the fuel economy improvements. In this study, fuel economy data from various types of drives on a pre-determined route was collected without and with the adaptive real-time advisory system to compute the mean fuel economy improvement. In addition, the real world fuel economy improvement using the haptic and visual feedback mechanisms was statistically quantified. The study results demonstrated that the adaptive real-time advisory system achieved a 6% and 3% mean real world fuel economy improvement using haptic and visual feedback mechanisms respectively without significantly compromising the driver's expectations and vehicle performance.