An energy active regulation management strategy based on driving mode recognition for electro-hydraulic hybrid vehicles

Abstract

Electro-hydraulic hybrid vehicles(EHHVs) have shown astonishing advantages in energy utilization and recovery. However, the key factor affecting the energy efficiency lies in the selection and switching conditions of driving modes. This paper introduces the structure and working principle of an electro-hydraulic hybrid vehicle that combines planetary gear mechanism. And an energy active regulation management strategy combined with driving mode recognition(DMR) and fuzzy control strategy is proposed. Firstly, a driving mode feature parameter evaluation is presented based on the collected speed and the principal component analysis(PCA). Secondly, K-means clustering algorithm is applied to identify offline driving modes through the extraction of driving short cycles. Then, combined with the PCA results, a Learning Vector Quantization neural network(LVQ) is constructed to recognize driving modes. Simultaneously, a fuzzy controller is added to allocate torque ratio. Finally, the DMR and fuzzy controller are combined to realize the energy active regulation of motors and hydraulic pump/motor. Experimental results indicate that energy management strategies based on DMR and fuzzy controller can realize optimal switching between modes according to condition recognition. It improves the utilization rate of the accumulator, and reduces battery energy consumption compared with traditional strategies.