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, Wenwei Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number126128
JournalApplied Energy
Volume395
DOIs
Publication statusPublished - 1 Oct 2025
Externally publishedYes

Keywords

  • Deep learning
  • Electric vehicles
  • Energy efficiency
  • Optimization
  • Powertrain topology

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