New Energy Vehicle Mechanical Transmission System and Energy Flow Distribution Optimization

The collaborative optimization of the mechanical transmission system and energy flow in new energy vehicles is a key technology for improving vehicle dynamic performance, transmission efficiency and driving range. This paper systematically reviews the energy transfer paths of two typical configurations: pure electric and hybrid electric. It analyzes the configuration of the mechanical transmission system and clarifies that, as the core carrier of energy flow, its structure, speed ratio and efficiency characteristics directly affect the overall vehicle energy consumption. This review compares the technical characteristics and applicable scenarios of three types of energy flow management strategies: rule-based control, global optimal control, and model predictive control. It briefly analyzes the influence mechanism of transmission forms on energy utilization efficiency, and points out key issues including the nonlinear efficiency of transmission systems and parasitic losses. This paper summarizes mainstream collaborative optimization methods such as multi-parameter joint optimization, dynamic efficiency compensation, and real-time collaborative control, as well as the verification system combining simulation and experimentation. According to the review, the integrated collaborative control of the mechanical transmission system and energy flow represents an important development direction for future energy-saving technologies of new energy vehicles, and can provide a theoretical reference for improving overall vehicle energy efficiency.

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Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2026-05-08
DOI
https://doi.org/10.1051/matecconf/202642004014/pdf
Primary Topic
Electric and Hybrid Vehicle Technologies
Type
article
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article

New Energy Vehicle Mechanical Transmission System and Energy Flow Distribution Optimization

Yonghong Guan
Springer Link (Chiba Institute of Technology)
Electric and Hybrid Vehicle Technologies
article

New Energy Vehicle Mechanical Transmission System and Energy Flow Distribution Optimization

Yonghong Guan
article en

Abstract

The collaborative optimization of the mechanical transmission system and energy flow in new energy vehicles is a key technology for improving vehicle dynamic performance, transmission efficiency and driving range. This paper systematically reviews the energy transfer paths of two typical configurations: pure electric and hybrid electric. It analyzes the configuration of the mechanical transmission system and clarifies that, as the core carrier of energy flow, its structure, speed ratio and efficiency characteristics directly affect the overall vehicle energy consumption. This review compares the technical characteristics and applicable scenarios of three types of energy flow management strategies: rule-based control, global optimal control, and model predictive control. It briefly analyzes the influence mechanism of transmission forms on energy utilization efficiency, and points out key issues including the nonlinear efficiency of transmission systems and parasitic losses. This paper summarizes mainstream collaborative optimization methods such as multi-parameter joint optimization, dynamic efficiency compensation, and real-time collaborative control, as well as the verification system combining simulation and experimentation. According to the review, the integrated collaborative control of the mechanical transmission system and energy flow represents an important development direction for future energy-saving technologies of new energy vehicles, and can provide a theoretical reference for improving overall vehicle energy efficiency.

Springer Link (Chiba Institute of Technology)
Beibu Gulf University (CN)
Affordable and clean energy
Openalex Percentile: Top 7%
Electric and Hybrid Vehicle Technologies
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New Energy Vehicle Mechanical Transmission System and Energy Flow Distribution Optimization — Yonghong Guan · Springer Link (Chiba Institute of Technology) (2026) | TGRS Research Map | TGRS