An Adaptive Adjustment Method of Equivalent Factor Considering Speed Predict Information

Abstract

Although the energy management strategies at present have achieved a good effect, they still have their limitations, so there is still room for further improvement to improve the fuel economy of hybrid electric vehicles (HEV). This paper proposes an adaptive equivalent consumption minimization strategy (ECMS) based on speed prediction, which can distribute power more reasonably and improve the power balance and fuel economy. The driving speed reflects the operation of the road and driver during the driving process. Under the motor assisted energy management control strategy, knowing all working condition information in advance can improve the battery power use planning to a certain extent and reduce the fuel consumption during the whole driving process by adjusting parameters. In this paper, a novel adaptive adjustment method for the equivalent factor (EF) of the ECMS based on future information is proposed. In this paper, a novel speed-prediction method combined with wavelet packet transformation (WPT) and a radial basis function neural network (RBF-NN) is proposed to realize accurate vehicle speed prediction. Then, the optimal equivalent factor under the state of charge (SOC) constraint is calculated by using the predicted speed. Simulation studies are conducted to verify the effectiveness of the proposed adjustment method for the EF compared to a commonly adjustment method from SOC balance and economic viewpoints.

Keywords

• equivalent fuel consumption minimum strategy
• wavelet packet transformation
• radial basis function neural network
• speed predict