主办：陕西省汽车工程学会
ISSN 1671-7988  CN 61-1394/TH
创刊：1976年
汽车实用技术

汽车实用技术 ›› 2022, Vol. 48 ›› Issue (5): 56-63.DOI: 10.16638/j.cnki.1671-7988.2023.05.010

• 设计研究 • 上一篇    

基于 RLS 和 BO 算法的重型车载重估算研究

白晓鑫 1，吴春玲*1,2，景晓军 1，杨永真 1   

  1. 1.中汽研汽车检验中心（天津）有限公司2.天津大学 机械工程学院
  • 出版日期:2023-03-15 发布日期:2023-03-15
  • 通讯作者: 吴春玲
  • 作者简介:白晓鑫（1993—），男，硕士，工程师，研究方向为基于远程数据的重型车排放评估，E-mail:baixiaoxin@ catarc.ac.cn。
  • 基金资助:
    国家重点研发计划（2022YFC3703600）

Research on Heavy-duty Vehicle Mass Estimation Based on Recursive Least Square and Bayesian Optimization Algorithm

BAI Xiaoxin1 , WU Chunling*1,2 , JING Xiaojun1 , YANG Yongzhen1   

  1. 1.CATARC Automotive Test Center (Tianjin) Company Limited2.School of Mechanical Engineering, Tianjin University
  • Online:2023-03-15 Published:2023-03-15
  • Contact: WU Chunling

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摘要： 针对重型车实际使用过程中载重估算精度低、成本高等问题，提出基于递归最小二乘 法（RLS）和贝叶斯优化（BO）算法的内燃机重型车辆载重估算方法。该方法提出了基于数 据滤波的车辆加速度和道路坡度计算方法，使用控制器局域网络（CAN）总线和全球定位系 统（GPS）高程数据，基于车辆纵向动力学和 RLS 进行重型车载重估算。采用 BO 算法对 12 组训练数据建模，对车辆载重估算模型中的多变量滤波参数进行寻优配置，并利用测试数据 进行模型估算性能评价。结果表明，该方法具有良好的载重估算精度，估算误差在 6%以内。

关键词: 重型车；载重；估算；递归最小二乘法；贝叶斯优化算法

Abstract: In order to solve the problems of low accuracy and high cost of mass estimation in the actual use of heavy vehicles, a method of load estimation of heavy-duty vehicles with internal combustion engine based on recursive least squares (RLS) and bayesian optimization (BO) algorithm is proposed. In this method, a method for calculating vehicle acceleration and road slope based on data filtering is proposed. Controller area network (CAN) bus and global positioning system (GPS) elevation data are used to estimate heavy vehicle load based on vehicle longitudinal dynamics and RLS. The BO algorithm is used to model the 12 groups of training data, and the multivariable filtering parameters in the vehicle mass estimation model are optimized, and the test data are used to evaluate the model estimation performance. The results show that the method has good accuracy of load estimation, and the estimation error is less than 6%.

Key words: Heavy-duty vehicle; Mass; Estimation; Recursive least square; Bayesian optimization