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基于AEKF的车辆质量与道路坡度实时估计
Real Time Estimation of Vehicle Quality and Road Slope Based on Adaptive Extended Kalman Filter
  
DOI:10.16450/j.cnki.issn.1004-6801.2020.04.019
中文关键词:  纵向动力学模型  自适应扩展卡尔曼滤波  汽车质量  道路坡度  遗忘因子
英文关键词:longitudinal dynamics model  adaptive extended Kalman filter(AEKF)  vehicle quality  road gradient  forgetting factor
基金项目:(国家自然科学基金资助项目(51805086);福建省自然科学基金资助项目(2019J01210);河南科技大学国家地方联合工程试验室开放基金资助项目(201802)
作者单位
任志英1,沈亮量1,黄伟2,刘星星1 (1.福州大学机械工程及自动化学院 福州350116)(2.福建省特种设备检验研究院 福州350008) 
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中文摘要:
      针对车辆在实际行驶过程中外界噪声的统计特性无法已知的问题,以车辆纵向动力学模型为基础,提出了自适应扩展卡尔曼滤波(adaptive extended Kalman filter,简称AEKF)的车辆质量及道路坡度估计算法。以动态估计车辆系统中的质量与坡度为研究对象,引入了旋转质量换算系数,建立车辆纵向动力学系统的状态空间模型,考虑了不同时刻的档位匹配与行驶特殊工况的处理。对系统状态方程进行离散化处理,得到系统状态方程与系统测量方程,在扩展卡尔曼滤波(extended Kalman filter,简称EKF)的基础上引入带遗忘因子的噪声统计估计器,通过AEKF对状态方程与测量方程实时更新,进行在线估计和校正噪声统计值,从而解决系统的噪声时变问题。本研究算法与EKF算法估计及实测结果的对比分析表明,本研究算法能够很好地对车辆质量和坡度信号进行有效滤波和估计,在短时间内逐渐收敛并逼近实测值,从而能够合理有效地检测车辆在行驶过程中的状态信息。
英文摘要:
      Aiming at the problem that the statistical characteristics of the external noise in the actual driving process of the vehicle cannot be known, based on the longitudinal dynamics model of the vehicle, the adaptive extended Kalman filter (AEKF) vehicle quality and road gradient estimate algorithm is proposed. Taking the dynamic estimation of the mass and slope of the vehicle system as the research object, the rotation mass conversion coefficient is introduced, the state space model of the vehicle longitudinal dynamic system is established, and the gear matching at different times and the handling of special driving conditions are considered. The system state equation is discretized to obtain the system state equation and the system measurement equation. Then, the noise statistical estimator with forgetting factor is introduced on the basis of the extended Kalman filter (EKF).The on-line estimation and correction of noise statistics are performed based on the real-time updating of the state equation and the measurement equation by adaptive extended Kalman filter, so as to solve the problem of time-varying noise of the system. The comparative analysis of the estimated and measured results of this algorithm and the EKF algorithm shows that the proposed algorithm can effectively filter and estimate the vehicle mass and gradient signals in the vehicle longitudinal dynamics model, and gradually converge and approach the measured value in a short time, so that it can be reasonably and effectively detect the status information of the vehicle during driving.
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