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首页> 外文期刊>Journal of Sound and Vibration >Investigation into on-road vehicle parameter identification based on subspace methods
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Investigation into on-road vehicle parameter identification based on subspace methods

机译：基于子空间方法的公路车辆参数辨识研究

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The randomness of road-tyre excitations can excite the low frequency ride vibrations of bounce, pitch and roll modes of an on-road vehicle. In this paper, modal parameters and mass moments of inertia of an on-road vehicle are estimated with an acceptable accuracy only by measuring accelerations of vehicle sprung mass and unsprung masses, which is based on subspace identification methods. The vehicle bounce, pitch and roll modes are characterized by their large damping (damping ratio 0.2-0.3). Two kinds of subspace identification methods, one that uses input/output data and the other that uses output data only, are compared for the highly damped modes. It is shown that, when the same data length is given, larger error of modal identification results can be clearly observed for the method using output data only; while additional use of input data will significantly reduce estimation variance. Instead of using tyre forces as inputs, which are difficult to be measured or estimated, vertical accelerations of unsprung masses are used as inputs. Theoretical analysis and Monte Carlo experiments show that, when the vehicle speed is not very high, subspace identification method using accelerations of unsprung masses as inputs can give more accurate results compared with the method using road-tyre forces as inputs. After the modal parameters are identified, and if vehicle mass and its center of gravity are pre-determined, roll and pitch moments of inertia of an on-road vehicle can be directly computed using the identified frequencies only, without requiring accurate estimation of mode shape vectors and multi-variable optimization algorithms.
机译：公路轮胎激励的随机性会激发公路车辆的弹跳，俯仰和侧倾模式的低频行驶振动。在本文中，基于子空间识别方法，仅通过测量车辆的簧载质量和非簧载质量的加速度，就能以可接受的精度估算道路车辆的模态参数和质量惯性矩。车辆的弹跳，俯仰和侧倾模式具有较大的阻尼（阻尼比0.2-0.3）。对于高阻尼模式，比较了两种子空间识别方法，一种使用输入/输出数据，另一种仅使用输出数据。结果表明，当给出相同的数据长度时，仅使用输出数据的方法可以清楚地看到模态识别结果的较大误差。同时额外使用输入数据将大大减少估计方差。代替将难以测量或估计的轮胎力用作输入，将未悬挂质量的垂直加速度用作输入。理论分析和蒙特卡罗实验表明，当车速不是很高时，与以道路轮胎力为输入的方法相比，以未悬挂弹簧的加速度作为输入的子空间识别方法可以得到更准确的结果。在识别出模态参数之后，并且如果预先确定了车辆质量及其重心，则仅使用识别出的频率就可以直接计算出道路车辆的侧倾和俯仰惯性矩，而无需精确估计模态形状向量和多变量优化算法。

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