Vehicle mass is one of the most critical parameters in the vehicle control system, based on the discrete vehicle longitudinal dynamic equation after the forward Euler approximation, non-linear particle filter is introduced to estimate the vehicle mass intelligently, and it gains a competitive advantage that statistical characteristics of noise and uncertainties in the system are not necessary to be known or supposed in advance. As a sort of recursive, Bayesian state estimator, vehicle mass is regarded as a constant state variable to constitute the discrete state-space equation, motor torque is selected as input signal, and the measurable vehicle speed is selected to constitute the observation equation, parameters such as rolling resistance coefficient, air drag coefficient and road slop are considered as high-power noise and uncertainties. The performance of the proposed vehicle mass estimator is tested by several groups of load and the results demonstrate that the output of the particle filter based vehicle mass estimator can converge to the real value and keep steady.

中文翻译：

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基于粒子滤波的不确定性电动汽车质量智能估计

车辆质量是车辆控制系统中最关键的参数之一，基于前向欧拉逼近后的离散车辆纵向动态方程，引入非线性粒子滤波来智能地估计车辆质量，并获得了竞争优势无需事先知道或假定系统中噪声和不确定性的统计特性。作为一种递归的贝叶斯状态估计器，将车辆质量视为恒定状态变量以构成离散状态空间方程，选择电动机转矩作为输入信号，并选择可测量的车速构成观察方程，参数诸如滚动阻力系数，空气阻力系数和道路坡度等被认为是大功率噪声和不确定性。