基于运动学信息融合的四轮驱动汽车纵向车速自适应估计方法

doi:10.3901/JME.2021.08.184

机械工程学报 ›› 2021, Vol. 57 ›› Issue (8): 184-194.doi: 10.3901/JME.2021.08.184

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基于运动学信息融合的四轮驱动汽车纵向车速自适应估计方法

任彦君¹, 殷国栋¹, 沙文瀚^1,2, 沈童¹

1. 东南大学机械工程学院南京 211189;
2. 奇瑞新能源汽车股份有限公司电控与架构部芜湖 241000

收稿日期:2020-05-19 修回日期:2020-10-14 出版日期:2021-04-20 发布日期:2021-06-15
通讯作者: 殷国栋(通信作者)，男，1976年出生，博士，教授，博士研究生导师。主要研究方向为车辆动力学与控制、无人驾驶汽车与智能网联汽车。E-mail：ygd@seu.edu.cn
作者简介:任彦君，男，1994年出生，博士研究生。主要研究方向为车辆系统动力学与控制。E-mail：ryj19940918@163.com
基金资助:
国家重点研发计划（2016YFD0700905）、国家自然科学基金（51975118，52025121）和江苏省成果转化（BA2018023）资助项目。

Longitudinal Velocity Adaptive Estimation for Four-wheel-drive Vehicles Via Kinematic Information Fusion

REN Yanjun¹, YIN Guodong¹, SHA Wenhan^1,2, SHEN Tong¹

1. Department of Mechanical Engineering, Southeast University, Nanjing 211189;
2. Department of Electronic Architecture and Vehicle Control, Chery New Energy Co., Ltd., Wuhu 241000

Received:2020-05-19 Revised:2020-10-14 Online:2021-04-20 Published:2021-06-15

摘要/Abstract

摘要： 针对四轮驱动汽车纵向车速难以直接测量的问题，考虑多源传感器信号置信度动态变化特征，提出一种基于运动学信息融合的纵向车速自适应估计方法。研究行驶环境对车载传感器信息的影响规律，建立基于Kalman滤波框架的运动学信息融合模型，设计面向有色噪声的车速自适应滤波算法，实现对车轮滑移和道路坡度等非随机时变因素的扰动补偿。为兼顾算法稳定性及估计最优性，提出融合衰减记忆因子的强跟踪滤波改进策略，有效避免了极限工况下的滤波发散现象。在Carsim/Simulink联合仿真环境下，采用坡道加速、车轮滑转和双移线等工况验证算法的有效性并与H_∞滤波等方法进行对比。开发了处理器在环试验系统，分析算法在嵌入式控制器中运行的一致性和实时性。研究结果表明，所提出的车速自适应估计方法准确性高、稳定性好，与现有方法相比具有更好的工况适应性，估计结果不依赖先验噪声统计特性的获取，实时性能够满足车载控制器要求，解决了复合工况下四驱汽车高精度纵向车速的统一估计问题。

关键词: 四轮驱动汽车, 纵向车速估计, Kalman滤波, 噪声自适应, 运动学信息融合

Abstract: It is rather difficult to measure the longitudinal velocity of four-wheel drive vehicle directly. Considering the dynamic confidence feature of signals from different sensors, an adaptive longitudinal velocity estimation method based on kinematic information fusion is proposed. The driving environment influence on vehicular sensors is investigated. Then the kinematic model for data fusion is established under Kalman filter framework. The adaptive algorithm regarding with the colored noise is designed to compensate the non-random and time-varying disturbance oriented from the slip ratio and road grade. To make better balance between the stability and the optimization, the strong tracking filter with fading factor is introduced to suppress the divergence under critical conditions. By Carsim/Simulink platform, the proposed method is validated and compared with the baseline method, such as H_∞ filter, under hill acceleration, wheel slip and double lane change conditions. The processor-in-the-loop system is developed to verify its execution time and consistence. Results reveal that the proposed method can accurately and stably estimate the longitudinal velocity with good real-time performance. Compared with the baseline methods, it shows better adaptability and has no requirement for prior statistics knowledge of noise. Thus, the high accurate velocity information for four-wheel drive vehicles can be obtained by this unified method under complex conditions, which is especially significant for vehicle active safety.

Key words: four-wheel-drive vehicle, longitudinal velocity estimation, Kalman filter, noise adaptability, kinematic information fusion

中图分类号:

U461
TP274

任彦君, 殷国栋, 沙文瀚, 沈童. 基于运动学信息融合的四轮驱动汽车纵向车速自适应估计方法[J]. 机械工程学报, 2021, 57(8): 184-194.

REN Yanjun, YIN Guodong, SHA Wenhan, SHEN Tong. Longitudinal Velocity Adaptive Estimation for Four-wheel-drive Vehicles Via Kinematic Information Fusion[J]. Journal of Mechanical Engineering, 2021, 57(8): 184-194.

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基于运动学信息融合的四轮驱动汽车纵向车速自适应估计方法

Longitudinal Velocity Adaptive Estimation for Four-wheel-drive Vehicles Via Kinematic Information Fusion

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