指数和线性参数化的轮胎-路面纵向附着条件实时估计方法

Real-time Tire-road Longitudinal Adhesion Estimation Based on Exponential Sum Linear Parameterization

利用动力学建模和遗忘因子递推最小二乘算法,对电动汽车纵向制动工况中轮胎-路面附着特性的峰值点估计方法进行研究。以匹配电子液压制动系统的前驱电动汽车为建模对象,提出了一种最大化能量回收的制动分配策略;针对再生制动轮优先趋于抱死这一现象,设计了一种附着条件实时估计算法,以降低利用附着系数进入非线性区域的风险。利用指数和线性模型对非线性Burckhardt摩擦模型进行拟合,并进行了在线辨识的仿真和硬件在环试验,结果表明该算法满足实时性要求,且相较于Kiencke线性化方法,具有更佳的估计精度和收敛速度。

The wheel dynamics modeling and FFRLS(forgetting factor recursive least-square)were utilized to develop a method of tire-road adhesion estimations in longitudinal braking conditions.The modeling object was an FWD(front wheel drive)EV(electric vehicle)equipped with an EHB(elec-tro-hydraulic brake).Firstly,a maximized regenerative brake distribution strategy was introduced.The regenerative brake wheels would tend to be locked before other wheels in heavy brake conditions.Sec-ondly,a real-time adhesion estimation method was designed to lowering the risks of wheel locks.The linear parameterization of ES(exponential sum)was introduced to fit non-linear Burckhardt friction mod-el,the on-line simulations and HiL(hardware-in-loop)tests were conducted.Results show that the pro-posed linear method may run in real-time,and has better estimation accuracy and convergence rate com-pared with linear Kiencke method.