An intelligent multi-feature statistical approach for discrimination of driving conditions of hybrid electric vehicle

As a new kind of vehicles with low fuel cost and low emission, hybrid electric vehicle (HEV) has been given more and more attentions in recent years. The key technique in the HEV is adopting the optimal control strategy for the best performance. As the premise, a correct driving condition discrimination has an extremely important significance. This paper proposes an intelligent multi-feature statistical approach to discriminate the driving conditions of the HEV automatically. First of all, this approach samples the driving cycle periodically. Then it extracts multiple statistical features and tests their significance by statistical analysis. After that, it applies SVM and other machine learning methods to discriminate the driving conditions intelligently and automatically. Compared to the others, the proposed approach can compute fast and discriminate in real time during the whole HEV running. In our experiments, it reaches an accuracy of 97%. As a result, our approach can mine the valid information in the data completely and extract multiple features which have clear meanings and significance. Finally, according to the prediction experiment by a neural network and the fitting experiment by the ARMA model, it turns out that our proposed approach raises the efficiency of controlling the HEV considerably.