Vehicle mass is an important parameter in vehicle dynamics control systems. Although many algorithms have been developed for the estimation of mass, none of them have yet taken into account the different types of resi...Vehicle mass is an important parameter in vehicle dynamics control systems. Although many algorithms have been developed for the estimation of mass, none of them have yet taken into account the different types of resistance that occur under different conditions. This paper proposes a vehicle mass estimator. The estimator incorporates road gradient information in the longitudinal accelerometer signal, and it removes the road grade from the longitudinal dynamics of the vehicle. Then, two different recursive least square method （RLSM） schemes are proposed to estimate the driving resistance and the mass independently based on the acceleration partition under different conditions. A 6 DOF dynamic model of four In-wheel Motor Vehicle is built to assist in the design of the algorithm and in the setting of the parameters. The acceleration limits are determined to not only reduce the estimated error but also ensure enough data for the resistance estimation and mass estimation in some critical situations. The modification of the algorithm is also discussed to improve the result of the mass estimation. Experiment data on asphalt road, plastic runway, and gravel road and on sloping roads are used to validate the estimation algorithm. The adaptability of the algorithm is improved by using data collected under several critical operating conditions. The experimental results show the error of the estimation process to be within 2.6%, which indicates that the algorithm can estimate mass with great accuracy regardless of the road surface and gradient changes and that it may be valuable in engineering applications. This paper proposes a recursive least square vehicle mass estimation method based on acceleration partition.展开更多
Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matri...Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.展开更多
为了实时获取商用车驾驶室悬置系统主动控制的重要参数,本文以某国产重型商用车作为研究对象,对重型商用车驾驶室质量估计方法进行研究。提出一种通过车架和驾驶室2组加速度信号,实时估计驾驶室质量的方法,在Matlab/Simulink环境下,对...为了实时获取商用车驾驶室悬置系统主动控制的重要参数,本文以某国产重型商用车作为研究对象,对重型商用车驾驶室质量估计方法进行研究。提出一种通过车架和驾驶室2组加速度信号,实时估计驾驶室质量的方法,在Matlab/Simulink环境下,对商用车驾驶室建立三自由度1/4驾驶室激励模型,基于递推最小二乘算法(recursive least squares,RLS)估计质量,并用C级路面仿真验证其正确性,同时,为验证质量估计算法的可行性与准确性,采用Matlab/Simulink进行仿真和实车试验。试验结果表明,在仿真状态下,本文所估计的驾驶室质量,在0.5 s内可迅速趋于参考值,能够准确、实时的估计驾驶室质量,估计误差集中在±20 kg范围内;在车辆起步工况下,最大误差为25.9%,但是随着车辆的平稳运行,误差稳定在10%以内,具有较好的收敛性。该研究为商用车驾驶室质量估计提出了新方法。展开更多
基金Supported by National Basic Research Program of China(Grant No.2011CB711200)
文摘Vehicle mass is an important parameter in vehicle dynamics control systems. Although many algorithms have been developed for the estimation of mass, none of them have yet taken into account the different types of resistance that occur under different conditions. This paper proposes a vehicle mass estimator. The estimator incorporates road gradient information in the longitudinal accelerometer signal, and it removes the road grade from the longitudinal dynamics of the vehicle. Then, two different recursive least square method （RLSM） schemes are proposed to estimate the driving resistance and the mass independently based on the acceleration partition under different conditions. A 6 DOF dynamic model of four In-wheel Motor Vehicle is built to assist in the design of the algorithm and in the setting of the parameters. The acceleration limits are determined to not only reduce the estimated error but also ensure enough data for the resistance estimation and mass estimation in some critical situations. The modification of the algorithm is also discussed to improve the result of the mass estimation. Experiment data on asphalt road, plastic runway, and gravel road and on sloping roads are used to validate the estimation algorithm. The adaptability of the algorithm is improved by using data collected under several critical operating conditions. The experimental results show the error of the estimation process to be within 2.6%, which indicates that the algorithm can estimate mass with great accuracy regardless of the road surface and gradient changes and that it may be valuable in engineering applications. This paper proposes a recursive least square vehicle mass estimation method based on acceleration partition.
基金This work is supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX18_0467)Jiangsu Province,China.During the revision of this paper,the author is supported by China Scholarship Council(No.201906840021)China to continue some research related to data processing.
文摘Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.
文摘为了实时获取商用车驾驶室悬置系统主动控制的重要参数,本文以某国产重型商用车作为研究对象,对重型商用车驾驶室质量估计方法进行研究。提出一种通过车架和驾驶室2组加速度信号,实时估计驾驶室质量的方法,在Matlab/Simulink环境下,对商用车驾驶室建立三自由度1/4驾驶室激励模型,基于递推最小二乘算法(recursive least squares,RLS)估计质量,并用C级路面仿真验证其正确性,同时,为验证质量估计算法的可行性与准确性,采用Matlab/Simulink进行仿真和实车试验。试验结果表明,在仿真状态下,本文所估计的驾驶室质量,在0.5 s内可迅速趋于参考值,能够准确、实时的估计驾驶室质量,估计误差集中在±20 kg范围内;在车辆起步工况下,最大误差为25.9%,但是随着车辆的平稳运行,误差稳定在10%以内,具有较好的收敛性。该研究为商用车驾驶室质量估计提出了新方法。
