A Convolutional Neural Network-Based Driving Cycle Prediction Method for Plug-in Hybrid Electric Vehicles With Bus Route

Abstract

Driving cycle prediction plays a key role in energy management strategy (EMS) for hybrid electric vehicles (HEVs). This paper studies a driving cycle prediction method based on convolutional neural network (CNN). Firstly, the k-shape clustering method is used to group the driving cycle data into six different types. Moreover, this method is compared with the k-means algorithm which is often used for clustering driving cycles. Secondly, CNN is adopted to predict the different types of the driving cycles based on the results of k-Shape clustering. Some basic features are selected to construct the input of the networks with no assistance of human experience. In the process of training neural networks, some high-level features which can describe the information of a driving cycle more accurately are extracted, and the deep neural networks are built, which are different from traditional experience-based driving cycle prediction methods. And then, the better performance of the proposed method is illustrated by making a comparison with the traditional machine learning method. Finally, an adaptive energy management strategy for plug-in hybrid electric buses (PHEB) based on deep learning is given, and simulation results prove the effectiveness of the proposed method.

Keywords

• Plug-in hybrid electric bus
• driving cycle prediction
• energy management strategy
• deep learning