准确、实时地获取路面附着信息是汽车主动安全控制系统正常工作的前提． 路面及其粗糙度、干湿状态对侧偏刚度有很大影响． 基于此，文中在车辆稳态转向下进行轮胎侧偏刚度估计，从而进行路面附着条件辨识． 首先由二自由度车辆模型得到前后轴的侧向力及侧偏角，并考虑载荷转移得到各个轮胎的垂向力． 通过前后轴之间的差值，消去较难得到的质心侧偏角，而后通过递推最小二乘法估计得到归一化的轮胎侧偏刚度，并比较不同路面附着条件下的估计结果． 与之前的侧偏刚度估计方法不同，所提出的方法不需要测量或估计质心侧偏角，因此不需要昂贵的额外传感器，并且考虑了载荷转移对侧偏刚度估计的影响． 文中最后通过仿真及电控模型车辆道路试验对所提出的算法进行了验证． 仿真及试验结果表明: 在考虑载荷转移的条件下，文中提出的辨识算法可以进行路面附着条件辨识．

Accurate and real-time access to road adhesion information is the premise of the operation of the vehicle active safety control system． The roughness and wet condition of road have great influence on lateral stiffness． Based on this，road adhesion condition can be identified by estimating the lateral stiffness of tire under the steady- state cornering． First，the lateral force and slip angles of front and rear axle were obtained from the two-degree-of- freedom vehicle model． Considering the load transfer，the vertical tire vertical force was obtained． With the diffe- rence between the front and back axes，the sideslip angle which is more difficult to get was eliminated． Finally，the normalized tire lateral stiffness was estimated by the recursive least square method，and the estimated results under different road adhesion conditions were compared． Different from previous lateral stiffness estimation methods，the proposed method does not need to measure or estimate the sideslip angle，so it does not need expensive extra sen- sors． Moreover，the influence of load transfer on the estimation of cornering stiffness was taken into account． The proposed algorithm was verified through simulation and electric model vehicle road test． The simulation and experi- mental results show that the proposed identification algorithm can identify the road adhesion conditions when consi- dering load transfer．