Driving Pattern Prediction Model for Hybrid Electric Buses Based on Real-World Driving Data

The recognition and prediction of driving pattern is a prerequisite to minimize fuel consumption for plug-in hybrid electric buses with online adaptive energy management strategies. In this work, we present a methodology to develop a driving pattern prediction model from state-of-the-art technologies of cluster, evaluation, and Markov prediction based on the real-world driving speed-time data of a plug-in hybrid electric bus on a fixed route throughout one month. Firstly, we extract the effective driving trips from a large number of irregular data. And by Principal Component Analysis (PCA) for pre-selected 14 feature variables, we select time percent of idling, average velocity and average running velocity composing the optimal feature vectors to partition the driving trips into three significantly different classification with k-means cluster method. The time percent of idling(PTI) of the resulted classification are respectively 0.149, 0.618 and 0.832,and the corresponding average velocity are respectively 17.52km/h,4.50km/h and 31.84km/h, and the corresponding average running velocity are respectively 21.06km/h,6.37km/h and 34.28km/h. Secondly, we segment the driving trips into driving snippets between two successive specific idling and also use PCA to get the optimal feature vectors composed by time percent of acceleration, time percent of deceleration and time percent of cruising to cluster the driving snippets into three clusters. Thirdly, we calculate the occurring probabilities of each driving snippets occurring in a specific class of driving trip and build the Markov prediction model of real-world driving cycle. Finally, we use the Markov model to reconstruct the driving cycle. And the smaller error of feature vectors between the reconstructed driving cycle and real-world driving cycle proves the effectiveness of the resulted Markov model.