李克強++羅禹貢++邊明遠++戴一凡++陳龍

摘 要：汽車狀態(tài)參數(shù)的準確獲得是保證汽車主動安全系統(tǒng)有效性的重要要求。分布式電驅動車輛的新型結構為傳統(tǒng)基于動力學的狀態(tài)參數(shù)估計方法的突破提供了可能。通過分析基于運動學與動力學方法各自不同的誤差特性，該文提出了對兩種估計方法的估計結果進行融合處理的分布式電驅動車輛狀態(tài)參數(shù)估計方法。利用理論推導，證明了該方法將能夠有效的提高不同工況下的估計精度，提高估計方法的工況適應性。為驗證該方法的有效性，開發(fā)了CarSim與Simulink聯(lián)合仿真試驗平臺。仿真結果表明，所提出的誤差加權的融合狀態(tài)觀測方法提高了分布式電驅動車輛狀態(tài)參數(shù)觀測精度和魯棒性。

關鍵詞：分布式電驅動車輛 車輛狀態(tài)估計 多方法融合

Vehicle State Estimation Based on Kinematic Model and Dynamic Model Merging

Li Keqiang Luo Yugong Bian Mingyuan Dai Yifan Chen Long

（Tsinghua University）

Abstract：Vehicle state parameters are essential for active safety control. Distributed electric vehicle with a new structure brings a breakthrough for the traditional dynamics state parameter estimation method. This paper presents a novel estimation method for distributed electric drive vehicle by analyzing different characteristics of the estimation errors of kinematics and dynamics estimation methods. This method merges the results of the two estimation methods with weighting coefficients. With a mathematical deduction， it shows that this method can effectively improve the estimation accuracy and applicability under different conditions. A CarSim and Simulink co-simulation test platform is developed to verify the effectiveness of the method. Simulation results show that the proposed method improves the state estimation accuracy and robustness of distributed electric drive vehicle state parameters.

Key Words：Distributed electric vehicle； Vehicle state estimation； Multi-method merging