Title: Green traffic management strategy for hybrid electric vehicles based on monocular deep velocity estimation algorithm

Authors: Donggen Yang; Jiang Qiu

Addresses: School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan, Hunan, China; School of Mechanical Engineering, Jiangxi Vocational College of Industry & Engineering, Pingxiang, Jiangxi, China ' School of Mechanical Engineering, Jiangxi Vocational College of Industry & Engineering, Pingxiang, Jiangxi, China

Abstract: Energy management strategies can control the energy flow between hybrid vehicle fuel tanks and electrical energy storage by addressing energy allocation issues, but they are influenced by factors such as driving conditions, electric drive system structure, and load characteristics. Based on this, a monocular depth and velocity estimation algorithm was developed, which was analysed, extracted and fused from the perspective of network features combined with a learning framework, and the strategy function was set and the vehicle power results were analysed. The results show that the average extraction accuracy of the vehicle feature information management strategy of this method exceeds 85%, and the error under different working conditions is less than 5%. The vehicle energy management strategy based on environmental information integration can greatly improve the fuel economy and power performance of hybrid vehicles, providing new ideas and tools for green traffic management and design of vehicles.

Keywords: monocular deep velocity estimation algorithm; hybrid electric vehicles; deep learning; energy management strategy; energy conservation.

DOI: 10.1504/IJVICS.2025.149389

International Journal of Vehicle Information and Communication Systems, 2025 Vol.10 No.4, pp.386 - 405

Received: 22 Jan 2024
Accepted: 14 Aug 2024
Published online: 28 Oct 2025 *

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