基于速度预测的电动无人车再生制动控制方法

doi:10.19562/j.chinasae.qcgc.2025.11.019

汽车工程 ›› 2025, Vol. 47 ›› Issue (11): 2265-2275.doi: 10.19562/j.chinasae.qcgc.2025.11.019

• • 上一篇

基于速度预测的电动无人车再生制动控制方法

吕学勤¹(),翟鑫睿^1,²,钱沈晨¹,吴涛¹,王佩吟泉¹,顾家威³

^1.上海电力大学自动化工程学院，上海 200090
^2.华电莱州发电有限公司，烟台 261400
^3.上海正仲动力科技有限公司，上海 200092

收稿日期:2024-07-23 修回日期:2025-01-02 出版日期:2025-11-25 发布日期:2025-11-28
通讯作者: 吕学勤 E-mail:lvxueqin@shiep.edu.cn
基金资助:
国家自然科学基金(52075316);上海地方高校能力建设项目(23010501400)

Regenerative Braking Control Strategy for Electric Unmanned Vehicles Based on Speed Prediction

Xueqin Lü¹(),Xinrui Zhai^1,²,Shenchen Qian¹,Tao Wu¹,Peiyinquan Wang¹,Jiawei Gu³

^1.School of Automation Engineering，Shanghai University of Electric Power，Shanghai 200090
^2.Huadian Laizhou Power Generation Co. ，Ltd. ，Yantai 261400
^3.Shanghai Zhengzhong Power Technology Co. ，Ltd. ，Shanghai 200092

Received:2024-07-23 Revised:2025-01-02 Online:2025-11-25 Published:2025-11-28
Contact: Xueqin Lü E-mail:lvxueqin@shiep.edu.cn

摘要/Abstract

摘要：

为了提高电动无人车在运行过程中的能量回收率，确保车辆运行的安全性和经济性，提出了一种基于速度预测的电动无人车再生制动控制策略。采用基于离线训练神经网络模型的路况识别算法和基于改进马尔可夫链的车速预测方法，使预测值更加准确。使用滑动采样窗方法对车辆行驶状态进行在线模式识别，并将预测的速度值转换为车辆运行的功率要求，然后通过模型预测控制器对车辆轮胎施加的转矩进行解算，从而确定电动无人车制动力矩控制的最优解。试验结果表明，使用再生制动控制策略的电动无人车可以有效控制制动过程中的能量回收效率，并扩大续航里程。

关键词: 电动无人车, 再生制动控制策略, 循环神经网络, 速度预测

Abstract:

In order to improve the energy recovery rate of electric unmanned vehicles during operation and to ensure the safety and economy of vehicle operation， a regenerative braking control strategy for electric unmanned vehicles based on speed prediction is proposed. The road condition detection algorithm based on offline training neural network model and the vehicle speed prediction method based on improved Markov chain are used to make the control process more accurate and stable. The sliding sampling window method is used for online pattern recognition of the vehicle driving state， and the predicted speed values are converted into the power demand for vehicle operation， and then the braking torque applied to the vehicle tires is solved by a model prediction controller to determine the optimal solution for the electric unmanned vehicle braking for different braking torques applied to each tire. The experimental results indicate that electric unmanned vehicles employing regenerative braking control strategies can effectively manage the efficiency of energy recovery during braking and extend their cruising range.

Key words: electric unmanned vehicles, regenerative braking control strategy, recurrent neural network, speed prediction

吕学勤,翟鑫睿,钱沈晨,吴涛,王佩吟泉,顾家威. 基于速度预测的电动无人车再生制动控制方法[J]. 汽车工程, 2025, 47(11): 2265-2275.

Xueqin Lü,Xinrui Zhai,Shenchen Qian,Tao Wu,Peiyinquan Wang,Jiawei Gu. Regenerative Braking Control Strategy for Electric Unmanned Vehicles Based on Speed Prediction[J]. Automotive Engineering, 2025, 47(11): 2265-2275.

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预测持续时间/s	1	3	5
准确率/%	99.82	92.15	82.73
误差/%	0.18	7.85	17.27

基于速度预测的电动无人车再生制动控制方法

Regenerative Braking Control Strategy for Electric Unmanned Vehicles Based on Speed Prediction

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