基于多传感器的电动汽车ESP关键状态参数估计研究

Wang Cheng, Song Chuan-xue, L. Jianhua
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引用次数: 2

摘要

为了提高估计精度,缩短估计器的响应时间,对电动汽车ESP系统的关键状态参数进行了估计。建立了基于多传感器和改进估计算法的车辆状态估计器。该估计器用于估计车速。介绍了一种速度估计模型。该模型由三自由度车辆模型、轮胎受力计算模型和路面附着系数计算模型组成。介绍了这些模型的数学方程。改进的估计算法是基于改进UKF的2倍sigma点采样。介绍了估计过程的初始化、预测和更新。根据速度估计的要求,建立了电动汽车的状态空间方程。基于Carsim和Simulink软件建立了仿真模型。进行了55公里时速和95公里时速的联合仿真实验。实验结果表明,估计速度能及时、准确地跟踪实际速度。改进后的估计方法适用于电动汽车ESP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Key State Parameters Estimation of Electric Vehicle ESP Based on Multi-sensor
In order to enhance the estimation accuracy and improve the response time of the estimator for the key state parameters estimation of electric vehicle ESP system. A vehicle state estimator was built based on multi-sensor and the improved estimation algorithm. The estimator was used to estimate vehicle speed. A speed estimation model was introduced. The model was made up of three degree of freedom vehicle model, tire force calculation model and road adhesion coefficient calculation model. The mathematical equations of those models were introduced. The improved estimation algorithm was based on two times sigma points sampling of improved UKF. The estimation process was introduced including initialization, forecast and update. The state space equations of electric vehicle were established in accordance with the requirements of speed estimation. A simulation model was built based on Carsim and Simulink software. Combined simulation experiments were made including the speed of 55 kilometers and 95 kilometers per hour simulation experiment. The experimental results shown that the estimated speed could follow the actual speed timely and accurately. The improved estimation method was fit for ESP of electric vehicle.
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