48V重型商用车坡度传感器滤波算法研究

Research on Filtering Algorithm of Slope Sensor for 48V Heavy Commercial Vehicle

本文针对重型商用车48V混合动力系统坡度传感器信号易被干扰、随机噪声复杂的问题,对坡度传感器信号的随机漂移模型及自适应Kalman滤波算法进行研究,通过采集数据信息,利用赤池信息量准则(AIC)确定自回归AR模型阶数,考虑所建立的模型具有模型参数和噪声统计特性存在误差的特点,研究一种含有强跟踪滤波渐消因子的Sagu-Husa自适应Kalman滤波算法。经与标准Kalman滤波算法进行对比仿真,表明改进后的滤波算法对模型参数和噪声统计特性不敏感,故该滤波算法能够有效提高48V混动坡度传感器信号精度。

Aiming at the problems that slope sensor signal of 48V hybrid power system of heavy commercial vehicle is easy to be disturbed and random noise is complex,random drift model of slope sensor signal and adaptive Kalman filtering algorithm are studied.Akaike information quantity criterion(AIC)was used to determine the order of AR model.Considering that the model parameters and statistical characteristics of noise have errors,a Sagu-Husa adaptive Kalman filtering algorithm with strong tracking filter fading factor is studied.The comparison and simulation with the standard Kalman filtering algorithm show that the improved filtering algorithm is insensitive to the model parameters and the statistical characteristics of noise.This filtering algorithm can effectively improve the signal accuracy of 48V hybrid slope sensor.