Browse Publications Technical Papers 2019-01-0161
2019-04-02

Energy Management Optimization for Plug-In Hybrid Electric Vehicles Based on Real-World Driving Data 2019-01-0161

Excellent energy consumption performance of a plug-in hybrid electric vehicle (PHEV) is usually attributed to its hybrid drive mode. However, the factors including vehicle performance, driver behavior and traffic status have been shown to cause unsatisfactory performance. This phenomenon leads to a necessity of study on energy consumption control strategies under real-world driving conditions. This paper proposes a new approach for energy management optimization of plug-in hybrid electric vehicles based on real-world driving data for two purposes. One is for improving the energy consumption of PHEV under real-world driving conditions and the other is for reducing the computational complexity of optimization methods in simulation models. In this process, the paper collected real-world driving record data from 180 drivers within 6 months. Then the principal component analysis (PCA) was employed to extract and define the hidden factors from the initial real-world driving data. The K-means clustering method was employed to evaluate the sensitivity of new factors to the energy consumption of PHEV, in which the sensitivity was defined through the Pearson correlation coefficient and covariance. Moreover, an optimal energy management strategy based on the sensitivity results and constrained optimization functions was used to optimize the speed-curve of PHEV. Furthermore, the 100 simulation tests based on MATLAB/Simulink platform were carried out to validate the feasibility of the proposed approach. The results indicate that 60% of the factors extracted from this paper show a strong sensitivity to the energy consumption of PHEV and the optimization method costs less computational time, leading to a better energy consumption performance. The proposed methodology is valuable for the research on real-time energy management of electric vehicles and speed control of intelligence vehicles under real-world road conditions.

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