基于汽车动力学与加速度传感信息的纵向坡度实时识别算法
Real-time Recognition Algorithm of Longitudinal Road Slope Based on Vehicle Dynamics and Acceleration Sensor Information
摘要 纵向坡度信息是汽车底盘及传动电控系统中的常用参量,其识别精确度及平滑度直接影响控制策略实施的准确性及驾乘舒适性,然而纵向坡度识别的难点在于:车辆高频动态特性及行驶工况复杂性直接影响加速度传感器的信号质量,由此也降低了直接使用该信息估算坡度值的精确度,而基于复杂非线性模型的观测方法则难以实时运算。同时一种识别方法若要扩展应用到多个电控系统中,也要求尽可能采用基本相同的变量作为输入。采用加速度坡度法与汽车动力学坡度法相融合的方式,根据其各自高精确度范围,先计算置信因子,得出坡度原始值,再利用广义迟滞滤波方法进行平滑处理,得到精确、平稳的坡度值。通过实车试验,验证了坡度识别算法的有效性。该方法具有运算量小、成本低、易实现的特点,可应用在多种实时电控系统中。
Abstract:
Longitudinal slope is a usual parameter in the electronic control system of vehicle chassis and transmission. The work veracity and drive comfort of control strategy are directly affected by the accuracy and smoothness of the slope recognition. The problem of recognition is that the signal quality of acceleration sensor is affected by the vehicle dynamic behavior with high frequency and the complexities of drive cycle, which make the estimated road slope with this method have a low accuracy. And the observational method based on a complicated nonlinearity model can’t realize a real-time operation. Meanwhile if a kind of recognition method can be used by multi-system application, the basic and usual input variables are required. In order to solve these problems, an integration method with the acceleration slope method and vehicle dynamic slope method is presented in this paper. According to their high precision area sufficiently, the confidence factor is calculated. And then the raw slope can be obtained. Eventually the raw slope is processed by a generalized hysteresis filter to get the accurate and smooth slope. The test verifies that this algorithm is effective. This recognition algorithm with small calculating amount, low cost and easy realization can be adopted in multiple real-time electronic control systems.
Author: RAN Xu LI Liang ZHAO Xun SONG Jian YANG Cai CAO Minglun
作者单位: 清华大学汽车安全与节能国家重点实验室北京 100084 长安汽车股份有限公司汽车工程研究总院重庆 401120
刊 名 机械工程学报 ISTICEIPKU
年,卷(期): 2016, 52(18)
分类号: U461
机标分类号: TP3 U41
在线出版日期: 2016年11月2日
基金项目: 国家自然科学基金,国家科技支撑计划(2013BAG14B01)资助项目。