基于自适应FFRLS的汽车前后轴侧偏刚度估计

Estimation of Automobile Front/rear Axle Cornering Stiffness Based on the Adaptive FFRLS

汽车前、后轴侧偏刚度与汽车的操纵特性紧密联系,是汽车动力学控制中的重要参数。受轮胎特性变化的影响,在汽车使用中线性侧偏刚度发生变化。针对传统估计方法不利于在量产汽车上应用的问题,在车载传感器配置的基础上,提出应用自适应遗忘因子递推最小二乘方法(Forgetting-factor recursive least square method,FFRLS)的前、后轴线性侧偏刚度估计方法。设计出在制动工况下对轮胎特性变化具有鲁棒特性的侧向速度观测方法,由此计算估计模型中的前、后轴侧偏角。提出并应用最大侧向力计算估计模型中的前、后轴侧向力。通过设定合适的估计触发条件实现基于工况的在线参数估计。基于veDYNA的仿真结果表明在轮胎侧偏刚度突变时自适应FFRLS方法相比普通FFRLS方法参数能够更快地收敛。最后在驾驶员在环混合仿真平台环境下,针对触发条件受限的工况通过实时仿真验证了估计算法的有效性。

Front/rear axle cornering stiffness coefficient is an important parameter in automobile dynamic control system, which is linked closely with automobile maneuverability. In the real world front/rear axle linear cornering stiffness often changes with the tire properties. To overcome the deficiencies of the traditional method in the estimation of linear cornering stiffness for wide-spread application, a method based on adaptive forgetting-factor recursive least square（FFRLS） is proposed to estimate this parameter in which only common sensors in modern automobiles are used. A lateral velocity estimation method with the robustness of tire properties is given, which is further used to calculate axle sideslip angle. Maximum lateral force is proposed and applied in the computation of axle lateral force. Some adequate toggle conditions are set to identify front/rear axle linear cornering stiffness. Simulation results based on veDYNA show that adaptive FFRLS has better parameter convergence performance than normal FFRLS when tire properties suddenly change. Finally the estimation method is validated when the toggle condition is limited via real-time simulation in the human-in-the-loop test bench.