Kinematic state adaptive based novel powertrain for the next generation of vehicle electrification: Design optimization and simplification

Xingyu Zhou, Yuekai Guo, Wenmei Hao, Xingyong Zhang and Wenwei Wang

Applied Energy, 2025, vol. 395, issue C, No S030626192500858X

Abstract: Energy efficiency has become one of the major concerns that hinder the penetration of freight electric commercial vehicles (CEV), due to frequent and long-distance transportation tasks. However, how vehicle kinematic states would influence the energy requirement of a given journey and how design modifications might affect the energy conversion efficiency of CEV powertrains have not been comprehensively considered in the development phase of CEVs. This work presents a novel mechanism for saving required energy output by actively adapting differences in wheel speed and explores how modifications to powertrain design would shape the energy utilization within CEV powertrains. Correspondingly, a novel powertrain configuration for improving energy conversion efficiency and exploiting the energy-saving mechanism is proposed. Validation suggests a 54.8 % reduction in energy consumption of CEVs by the synergy of powertrain modifications and the novel energy-saving patterns, compared with the current CEV design. This result also opens the avenue for the next generation of CEVs.

Keywords: Electric vehicles; Powertrain topology; Energy efficiency; Optimization; Deep learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.126128

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