1 HEV control strategy research based on the computational intelligence
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. We proposed some approaches based on the computational intelligence.
2 An Intelligent Multi-feature Statistical Approach for Discrimination of Driving Conditions of Hybrid Electric Vehicle
We proposed an intelligent multi-feature statistical approach to discriminate the driving condition 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 to select effective features. After that, it applies support vector machine 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 95%. 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 simulation results of control strategy, it turns out that our proposed approach raises the efficiency of controlling the HEV considerably.
3 A Torque Control Strategy with Charge Buffer for Parallel Hybrid Electric Vehicle
We proposed a new torque control strategy with charge buffer (TCSCB) to control the two power sources of the HEV. The TCSCB is based on the control of engine torque which makes the control strategy easily distribute the output power to the engine and motor. In this control strategy, the real time optimization based on the engine efficiency map forces the engine to operate at relatively higher efficiency regions. The charge buffer reduces the dramatic fluctuation of the engine torque to improve the fuel economy. The prediction of the required engine torque based on neural networks improves the control performance greatly by the future information.
We built a light-duty parallel HEV model with different control strategies on the simulation platform ADVISOR. The experimental results showed the TCSCB could reach a higher fuel economy and lower emission compared to the current control strategies. In order to optimize its control performance, the parameters in the TCSCB were also discussed in detail.
4 Forecast of Driving Load of Hybrid Electric Vehicles by using Discrete Cosine Transform and Support Vector Machine
We proposed an efficient approach for forecasting the driving load of the HEV by using Discrete Cosine Transform (DCT) and Support Vector Machine (SVM). The DCT is used to extract features from raw data, and reduce the dimensionality of feature which will result in an efficient SVM classification. The SVM is used to classify the current driving load into one of five presetting levels of the driving load of the HEV. In such way, we can predict the driving load efficiently and accurately, which leads to a reasonable control to the HEV and gives as a high efficiency and low emission level as possible. Finally, a number of experiments are conducted to verify the validity of our proposed approach. Compared to current methods, our proposed approach gives a considerably promising performance through extensive experiments and comparison tests.
