Driving conditions greatly affect the energy control and the fuel economy of a hybrid electric vehicle (HEV). In this paper, an automated feature extraction scheme based on convolution neural networks (CNNs) and Kernel PCA (KPCA) for real time driving pattern recognition (RTDPR) is proposed in order to achieve consistent performance of the energy management. Firstly, a dimension expanding strategy is performed to transform one-dimensional speed sequences to generate a two-dimensional dataset. Then, the transformed data is sent to the CNN and KPCA based feature extractor. Finally, the feature extractor automatically selects the most representative features for classification. To improve the generalization of CNN to a small sample dataset, the structure of the typical CNN is adjusted by adding the KPCA layer in order to reduce model parameters. The model is well trained and evaluated in simulation, and it is tested for RTDPR in the real world. Simulation and experimental results show that the proposed automated feature extraction strategy outperforms the conventional driving pattern recognition algorithms based on manually feature extraction, which has achieved the state-of-the-art recognition accuracy.
Bibliographical noteFunding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61603337, in part by the Zhejiang Province Natural Science Fund under Grant LY19F030009, and in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (ICT1900362). The work of H. Zhou was supported in part by the U.K. EPSRC under Grant EP/N011074/1, in part by the Royal Society-Newton Advanced Fellowship under Grant NA160342, and in part by the European Union’s Horizon 2020 Research and Innovation Program through the Marie-Sklodowska-Curie under Grant 720325.
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Copyright 2020 Elsevier B.V., All rights reserved.
- Convolution neural network
- driving pattern recognition
- feature selection
- kernel principle component analysis
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)