1. INTRODUCTION A hybrid electric vehicle (HEV) has better fuel economy and less emission than a conventional internal combustion engine vehicle due to the existence of electric powertrain. The introduction of additional powertrain components, however, makes the HEV control more challenging and the performance of HEVs is more sensitive to their control strategies. To achieve maximum fuel economy and minimum emissions, researchers in the automotive community have made significant effort to investigate the major factors impacting fuel efficiency and develop optimal power management strategies for hybrid vehicles [ 1,2,3,4,5]. Research results showed that, in addition to vehicle and fuel characteristics, driving patterns have a strong impact on the fuel consumption and exhaust emissions [ 6, 7]. To optimize vehicle performance, multi-mode driving control method has been proposed for the adaptive vehicle control [ 8, 9]. The multi-mode driving control is defined as the control strategy which is able to switch a current control algorithm to the one that is optimized to the recognized driving pattern [ 8]. The ability to dynamically select control algorithms based on identified driving patterns leads to adaptive vehicle control,improved energy efficiency, and reduced green gas emissions. A driving pattern is typically defined as the driving cycle of a vehicle in a particular environment [ 10]. To recognize driving patterns, it is necessary to identify a list of characteristic parameters which can be used to describe driving patterns. Although there is no consensus among researchers about what parameters can be used for driving pattern recognition, several studies have attempted to find such a list of parameters. Ericsson [ 6] investigated the impact of 62 driving pattern parameters on fuel economy and emissions using a large amount of testing driving cycles. The study showed that nine driving pattern parameters (four associated with power demand and acceleration, three with gear changing behavior, and two with speed level) had an important effect on fuel consumption and emissions. Lin et al. [9] selected power demand related parameters and stop time for hybrid electric truck driving pattern recognition. In addition to vehicle parameters, Jeon et al. [8] incorporated road grade parameters in the driving pattern recognition. For pattern classification method, neural network [ 8], support vector machine (SVM) [ 11], and learning vector quantization 2012-01-0742 Published 04/16/2012 Copyright © 2012 SAE International doi:10.4271/2012-01-0742 saealtpow.saejournals.org Driving Pattern Recognition for Adaptive Hybrid Vehicle Control Lei Feng, Wenjia Liu and Bo Chen Michigan Technological Univ ABSTRACT The vehicle driving cycles affect the performance of a hybrid vehicle control strategy, as a result, the overall performance of the vehicle, such as fuel consumption and emission. By identifying the driving cycles of a vehicle, the control system is able to dynamically change the control strategy (or parameters) to the best one to adapt to the changes of vehicle driving patterns. This paper studies the supervised driving cycle recognition using pattern recognition approach. With pattern recognition method, a driving cycle is represented by feature vectors that are formed by a set of parameters to which the driving cycle is sensitive. The on-line driving pattern recognition is achieved by calculating the feature vectors and classifying these feature vectors to one of the driving patterns in the reference database. To establish reference driving cycle database, the representative feature vectors for four federal driving cycles are generated using feature extraction method. The quality of representative feature vectors with different feature extraction methods is evaluated by examining the separation of feature vectors in the feature space and the success rate of the pattern recognition. The performance of the presented adaptive control strat