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基于加速度信号的轮胎滚动阻力估计算法

Tire rolling resistance estimation algorithmbased on acceleration signal
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摘要 提出了一种基于智能轮胎的滚动阻力估计算法,利用轮胎加速度信号实现轮胎滚动阻力更高精度的估算。使用ABAQUS软件建立205/55/R16子午线轮胎的稳态滚动阻力模型,通过对不同负载、胎压和车速下滚动阻力的仿真结果分析,验证了有限元模型的一致性。通过提取轮胎内衬中心轴线处的加速度信号,使用Yule-Walker分析法计算加速度信号的功率谱密度。基于汽车行驶参数和轮胎接地离地瞬间的加速度功率谱密度数据,采用偏最小二乘回归法对轮胎的滚动阻力和滚阻系数进行回归预测。结果表明:结合轮胎加速度信号与行驶参数的回归方程拟合效果比单纯使用行驶参数拟合的效果更好,研究结果为开发节能汽车提供了一定的指导作用。 Tire rolling resistance is an important factor affecting vehicle fuel economy,which is mainly due to the energy loss caused by the hysteresis effect of rubber materials.For passenger cars using radial tires,about 3.4%to 6.6%of fuel consumption is used to overcome tire rolling resistance,so the topic of reducing vehicle fuel consumption by reducing tire rolling resistance has received more and more attention from scholars.The purpose of this paper is to establish a more accurate and effective tire rolling resistance prediction model by using acceleration signal combined with intelligent tire technology.In this paper,the 205/55/R16 passenger car radial tire is taken as the research object.Firstly,based on the contribution rate of rolling resistance,the structure of the radial tire is simplified reasonably,and the finite element model of the tire is established by ABAQUS finite element simulation software and material parameterization method.The UAMP subroutine tire is used to control the angular velocity of the tire to obtain the steady-state free rolling angular velocity of the tire and extract the rolling resistance data.Through finite element analysis and control variable method,the rolling resistance of tire finite element model under variable load,vehicle speed and tire pressure is studied.The validity of the finite element model is verified by the stress and strain characteristics of tire joints.Secondly,the acceleration data of the nodes at the central axis of the tire lining under various compound working conditions are extracted,and the acceleration of the nodes is converted from the body coordinate system to the acceleration body coordinate system using the coordinate transformation matrix,and the longitudinal,lateral and vertical acceleration curves are obtained.Comparing the response degree of different signals to rolling resistance,the longitudinal and vertical acceleration signals are selected as the observation signals.The generation mechanism of tire rolling resistance is analyzed.Yule-Walker frequency domain analysis method is used to calculate the power spectral density of acceleration signals.The relationship between signal power and frequency is estimated through the correlation of signals.Combined with tire pressure,vehicle speed and load,a tire rolling resistance estimation model based on partial least squares regression algorithm is built.Finally,the fitting effect of the model can be approximated according to the estimation results of the model under 20 test conditions of variable load,vehicle speed and tire pressure.The mean square error of the tire rolling resistance estimation algorithm based on acceleration signal is 0.3183,and the goodnessof fit is 0.9676.Under the same data set,the mean square error and goodness of fit of the tire rolling resistance estimation algorithm,which only uses vehicle speed,tire pressure and load as input variables,are 0.3524 and 0.9419.The results show that compared with the traditional physical model of rolling resistance,the fitting effect of the regression equation combining the tire acceleration signal and driving parameters is better than that of using only the driving parameters,and the prediction result is more accurate,which may provide some references for the research of rolling resistance.
作者 王子寒 李波 贝绍轶 刘涛 林棻 殷国栋 WANG Zihan;LI Bo;BEI Shaoyi;LIU Tao;LIN Fen;YIN Guodong(College of Automotive and Transportation Engineering,Jiangsu University of Technology,Changzhou 213001,China;Suzhou Automobile Research Institute,Tsinghua University,Suzhou 215200,China;General Equipment Research Institute,Beijing 102202,China;College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;College of Mechanical Engineering,Southeast University,Nanjing 210096,China)
出处 《重庆理工大学学报(自然科学)》 CAS 北大核心 2024年第1期30-40,共11页 Journal of Chongqing University of Technology:Natural Science
基金 国家自然科学基金项目(52172367) 江苏省高校自然科学基金重大项目(21KJA580001)。
关键词 智能轮胎 加速度 滚动阻力 功率谱密度 偏最小二乘回归 intelligent tire acceleration rolling resistance power spectral density partial least squares regression
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