Energy Management Strategy for Dual-Motor Coupled PHEVs Based on
Speed Prediction

Vehicle Engine ›› 2025, Vol. 0 ›› Issue (4) : 42-49.

Energy Management Strategy for Dual-Motor Coupled PHEVs Based on Speed Prediction

PAN Mingzhang¹,ZHOU Jingcheng¹,YE Nianye²,QIN Haifeng²,MAN Xingjia²,WANG Guodong²

Author information +

History +

Abstract

As an important direction of China's new energy strategy, optimizing the energy allocation of hybrid vehicles to improve power and fuel economy is a research hotspot. For the energy management strategy of plug-in hybrid electric vehicles (PHEVs), under the framework of stochastic model predictive control (SMPC), a vehicle speed prediction model based on convolutional neural network and long short term memory network (CNN-LSTM) was proposed, and the number of hidden units, maximum training period, and initial learning rate were optimized using honey badger algorithm (HBA). The experiments showed that HBA-CNN-LSTM was superior to the control method in all evaluation indicators. Subsequently, the simulation model of PHEV power system was established. The HBA-CNN-LSTM was combined with SMPC, and dynamic programming (DP) was used to optimize according to the goal of minimizing engine fuel consumption. The simulation results show that under 5 user-defined operating conditions, the proposed strategy reduces fuel consumption by 0.240 8 L (10.491%) compared to the CD-CS strategy, and the difference with the DP strategy is only 0.087 3 L (3.802%).

Key words

hybrid electric vehicle / speed prediction / predictive control / energy management

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

PAN Mingzhang, ZHOU Jingcheng, YE Nianye, QIN Haifeng, MAN Xingjia, WANG Guodong. Energy Management Strategy for Dual-Motor Coupled PHEVs Based on Speed Prediction[J]. Vehicle Engine. 2025, 0(4): 42-49