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针对汽车主动安全系统的需求,提出了一种包括纵向、侧向车速与附着系数的汽车主动安全参数的联合估计方法。基于3自由度车辆动力学模型和刷子轮胎模型,建立不同道路附着系数条件下的扩展卡尔曼滤波模型,利用交互多模型算法实现纵向、侧向车速的自适应估计,并根据计算出的各模型概率实现道路附着系数的实时估计。计算结果表明:该方法能在不同道路附着系数条件下进行车速的准确估计,纵向车速估计误差小于1%,侧向车速估计误差小于5%,与扩展卡尔曼方法相比误差减小了50%以上,且能够实时给出道路附着系数估计值,估计误差小于0.1,对路面突变的响应时间低于2s。
Aiming at the demand of vehicle active safety system, a joint estimation method of vehicle active safety parameters including longitudinal, lateral speed and adhesion coefficient is proposed. Based on the 3-DOF vehicle dynamics model and the brush tire model, an extended Kalman filter model under different road adhesion coefficients is established, and an adaptive multi-model algorithm is used to estimate longitudinal and lateral vehicle speed. Based on the calculated model Probabilistic real-time estimation of road adhesion coefficient. The calculation results show that the method can accurately estimate the vehicle speed under different road adhesion coefficients. The estimation error of longitudinal vehicle speed is less than 1% and the error of lateral vehicle speed estimation is less than 5%. Compared with Extended Kalman method, the error is reduced by 50% , And can be given real-time road adhesion coefficient estimates, the estimated error is less than 0.1, the response time of the road surface mutation is less than 2s.