Estimation of Vehicle State and Road Coefficient for Electric Vehicle through Extended Kalman Filter and RLS Approaches
Estimation of vehicle state (e.g.,vehicle velo city and sideslip angl e) and road friction coefficient is essential for electric vehicle stability control.This article proposes a novel real-time model-based vehicle estimator,which can be used for estimation of vehicle state and road friction coefficient for the distributed driven electric vehicle.The estimator is realized us ing the extended Kalman filter (EKF) and the recu rsive least squares (RLS) techniqu e.The proposed estimation algorithm is evaluated through simulation and experimental test.Results to data indicate that the proposed approach is effective and it has the ability to provide with reliable information for vehicle active safety control.
Electric vehicle extended Kalman filter (EKF) estimation of vehicle state and road coefficient recursive least squares (RLS)
LIN Cheng WANG Gang CAO Wan-ke ZHOU Feng-jun
The National Engineering Laboratory for Electric Vehicles,Beijing Institute of Technology,China
国际会议
沈阳
英文
2216-2220
2012-09-26（万方平台首次上网日期，不代表论文的发表时间）