Work

Driver-Oriented Adaptive Equivalent Consumption Minimization Strategy for Plug-in Hybrid Electric Buses

Year: 2025

Type: article

Abstract: The adaptability of the supervisory control strategy of plug-in hybrid electric buses (PHEBs) to different driving styles determines the energy-saving performance. This paper proposes a driver-oriented adaptive equivalent consumption minimization strategy (ECMS) for PHEBs. The strategy aims to improve the fuel economy of PHEBs as much as possible by adapting to different driving styles while satisfying the physical constraints of the hybrid power system. Firstly, an online driving style recognition algorithm based on the Fuzzy K-means (FKM) algorithm and the random forest (RF) method is devised, in which the FKM algorithm is used to preprocess the feature parameters related to driving styles and the RF method is utilized to identify the driver’s driving style. Secondly, the driving style recognition results are introduced into the ECMS framework to form a driver-oriented energy management strategy. Finally, the proposed control strategy is verified using both Matlab/Simulink and Hardware-in-the-Loop. The verification results demonstrate that the proposed control strategy improves the fuel economy of PHEBs.

Source: Energies

DOI: 10.3390/en18185033

Authors: Xiang Tian, Ma Wan, Xinqiang Chen, Yingfeng Cai, Xiaodong Sun, Zhen Zhu

Institutions: Jiangsu University, Chery Automobile (China)

Language: English

Topic: Electric and Hybrid Vehicle Technologies

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $3,137