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汽车安全与节能学报 ›› 2025, Vol. 16 ›› Issue (5): 707-715.DOI: 10.3969/j.issn.1674-8484.2025.05.005

• 汽车安全 • 上一篇    下一篇

适用绊倒及非绊倒侧翻的改进二次预测型预警算法

田阳1,4(), 李相澎1, 范义红2, 李钊男3, 李亮   

  1. 1.燕山大学 机械工程学院, 秦皇岛 066000,中国
    2.奇瑞汽车股份有限公司,芜湖市 241000,中国
    3.清华大学 车辆与运载学院,北京 100084,中国
    4.清华大学,智能绿色车辆与交通全国重点实验室,北京 100084,中国
  • 收稿日期:2025-05-06 修回日期:2025-06-21 出版日期:2025-10-31 发布日期:2025-11-10
  • 作者简介:田阳(1987—),男(汉),河北,讲师。E-mail:yang.tian@ysu.edu.cn
  • 基金资助:
    智能绿色车辆与交通全国重点实验室开放基金课题(KFY2410);国家自然科学基金(52572463)

Improved secondary prediction algorithm for tripping and non-tripping rollover for rollover warning

TIAN Yang1,4(), LI Xiangpeng1, FAN Yihong2, LI Zhaonan3, LI Liang   

  1. 1. School of Mechanical Engineering, Yanshan University, Qinhuangdao 066000, China
    2. Chery Automobile Co, Ltd, Wuhu 241000, China
    3. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    4. State Key Laboratory of Intelligent Green Vechicle and Mobility, Tsinghua University, Beijing 100084, China
  • Received:2025-05-06 Revised:2025-06-21 Online:2025-10-31 Published:2025-11-10

摘要: 为辨识车辆侧倾状态并准确预警侧翻,设计了基于新侧翻指标(NRI)的改进二次预测型侧翻预警算法。基于6自由度模型,导出适用于绊倒和非绊倒侧翻的新侧翻指标。提出了一次预测型和二次预测型相结合的改进二次预测型侧翻预警算法,将其与NRI相结合,通过Carsim软件与Matlab/Simulink软件的联合仿真及硬件,进行在环试验验证。结果表明:本算法在保留准确性的基础上提高了平稳性;在车辆未发生侧翻时,本算法的预警启停次数较二次预测型侧翻预警减少了39.5%,较一次预测型侧翻预警减小了47.3%;在车辆发生侧翻时,预警时间最大峰值较一次预测型侧翻预警减少了77.2%。从而,该算法能够对路面激励做出准确的响应,增强了适用范围。

关键词: 车辆主动安全, 侧翻预警算法, 新侧翻指标(NRI), 绊倒与非绊倒侧翻

Abstract:

An improved dual-prediction rollover warning algorithm was proposed with an incorporating New Rollover Indicator (NRI) to accurately identify vehicle roll dynamics and predict rollover risks. The NRI applicable to both trip and non-trip rollovers was derived from a 6-degrees of freedom model. A combined primary-secondary prediction strategy was developed and integrated with the NRI, validated through the Carsim software and the Matlab/Simulink software co-simulation and hardware-in-the-loop tests. The results show that the proposed algorithm improves the vehicle stability with maintaining the prediction accuracy, and reduces the warning activations by 39.5% and 47.3% with decreasing the maximum warning peak time by 77.2% in rollover situations, compared to conventional secondary and primary prediction methods respectively in non-rollover scenarios. Therefore, the NRI implementation enhanced the algorithm's responsiveness to road excitations with expanding its applicability.

Key words: vehicle active safety, rollover warning algorithm, new rollover index (NRI), tripping and non-tripping rollover

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