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Abstract
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.
Highlights
A driving pattern is typically defined as the driving cycle of a vehicle in a particular environment [1], [2]
Since the current driving pattern has a great impact on the energy management strategy of a hybrid electric vehicle (HEV) [3], [4], it is efficient to use the prior knowledge of the driving cycle to achieve the real time driving pattern recognition (RTDPR) and enhance the control performance of the HEV [5], [6]
Compared with the traditional driving pattern recognition methods, the convolution neural networks (CNNs) + Kernel PCA (KPCA) model has achieved the stateof-the-art correct rate, reaching 100% recognition accuracy on the training set and 97.40% on the testing set, whereas the best testing results from the methods based on feature extraction algorithms is 91.93%, which is a substantial leap
Summary
A driving pattern is typically defined as the driving cycle of a vehicle in a particular environment [1], [2]. The conventional way is to manually extract features from the historical speed data to characterize the driving patterns [2]. The quality of the feature extraction algorithm plays a great impact on the classification accuracy. Those manually extracted features usually include average speeds, average accelerations and other features which are directly calculated using physical models [11], while other complex and high level features are hard to represent. Those low level features are unable to effectively characterize the complex driving patterns.
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