An Intelligent Multifeature Statistical Approach for the Discrimination of Driving Conditions of a Hybrid Electric Vehicle

As a new kind of vehicle with low fuel cost and low emissions, the hybrid electric vehicle (HEV) has been paid much attention in recent years. The key technique in the HEV is adopting the optimal control strategy for the best performance. As the premise, correct driving condition discrimination has an extremely important significance. This paper proposes an intelligent multifeature statistical approach to automatically discriminate the driving condition of the HEV. First, this approach periodically samples the driving cycle. Then, it extracts multiple statistical features and tests their significance by statistical analysis to select effective features. Afterward, it applies a support vector machine (SVM) and other machine-learning methods to intelligently and automatically discriminate the driving conditions. Compared with others, the proposed approach can compute fast and discriminate in real time during the whole HEV running mode. In our experiments, it reaches an accuracy value of 95%. As a result, our approach can completely mine the valid information from the data and extract multiple features that have clear meanings and significance. Finally, according to the prediction experiment by a neural network, the fitting experiment by the autoregressive moving average model, and the simulation results of the control strategy, it turns out that our proposed approach raises the efficiency of considerably controlling the HEV.