Estimation of sideslip and roll angles of electric vehicles using lateral tire force sensors through RLS and kalman filter approaches

Kanghyun Nam, Sehoon Oh, Hiroshi Fujimoto, Yoichi Hori

Research output: Contribution to journalArticlepeer-review

229 Scopus citations

Abstract

Robust estimation of vehicle states (e.g., vehicle sideslip angle and roll angle) is essential for vehicle stability control applications such as yaw stability control and roll stability control. This paper proposes novel methods for estimating sideslip angle and roll angle using real-time lateral tire force measurements, obtained from the multisensing hub units, for practical applications to vehicle control systems of in-wheel-motor-driven electric vehicles. In vehicle sideslip estimation, a recursive least squares (RLS) algorithm with a forgetting factor is utilized based on a linear vehicle model and sensor measurements. In roll angle estimation, the Kalman filter is designed by integrating available sensor measurements and roll dynamics. The proposed estimation methods, RLS-based sideslip angle estimator, and the Kalman filter are evaluated through field tests on an experimental electric vehicle. The experimental results show that the proposed estimator can accurately estimate the vehicle sideslip angle and roll angle. It is experimentally confirmed that the estimation accuracy is improved by more than 50% comparing to conventional method's one (see rms error shown in Fig.4). Moreover, the feasibility of practical applications of the lateral tire force sensors to vehicle state estimation is verified through various test results.

Original languageEnglish
Article number6157614
Pages (from-to)988-1000
Number of pages13
JournalIEEE Transactions on Industrial Electronics
Volume60
Issue number3
DOIs
StatePublished - 2013

Keywords

  • Electric vehicles
  • Kalman filter
  • multisensing hub (MSHub) unit
  • recursive least squares (RLS)
  • roll angle
  • sideslip angle

Fingerprint

Dive into the research topics of 'Estimation of sideslip and roll angles of electric vehicles using lateral tire force sensors through RLS and kalman filter approaches'. Together they form a unique fingerprint.

Cite this