摘要详情

ID / 提交时间

125 / 2025-05-16 16:59:06

标题

Research on Dynamic Shift Control Strategy for Heavy-Duty Vehicles Based on Driving Intention Recognition

关键字

Heavy-duty vehicles; Driving intention; Shift control strategy; Greedy algorithm

主题及专题

4、System Monitoring, Fault Diagnosis and Prediction

状态

全文待审

作者

Liyong Wang / P. R. China； Beijing；The Ministry of Education Key Laboratory of Modem Measurement and Control Technology； Beijing Information Science &Technology University

Liye Xie / 2949877368@qq.com

Xiaozan Huang / Beijing Information Science and Technology University

Ximing Zhang / China north vehicle research institute

Jianpeng Wu / Beijing Information Science & Technology University

Ao Ding / Beijing Information Science and Technology University

摘要

To address the challenge faced by heavy vehicles in dynamically adjusting shifting strategies in real-time to achieve optimal performance, this study proposes a real-time dynamic shifting control strategy based on driving intention recognition. The static-dynamic and static-economy shift speeds are derived using a vehicle dynamics model. A Long Short-Term Memory (LSTM) neural network is employed to establish a driving intention recognition model for identifying the driver's intentions. The shift speeds, optimized using the greedy strategy and integrated with driving intentions, are compared with those from static-dynamics, static-economy, and greedy strategy optimization. The results show that the driving intention recognition-based shifting control strategy can accurately identify the driver's intentions based on the vehicle's operational state parameters, with an overall recognition accuracy of 93.67%. Compared to the static-dynamics shifting control strategy, the time required to accelerate from 0 to 80 km/h is reduced by 0.81% and compared to the static-economy shifting control strategy, fuel consumption is reduced by 1.07%, improving both dynamics and economy.