TY - JOUR
T1 - A novel method for identifying inertial parameters of electric vehicles based on the dual H infinity filter
AU - Gong, Xinle
AU - Suh, Jongsang
AU - Lin, Cheng
N1 - Publisher Copyright:
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2020/1/2
Y1 - 2020/1/2
N2 - Accurate identification of vehicle inertial parameters is essential to the design of vehicle dynamics control systems. In this paper, a novel vehicle inertial parameter identification method based on the dual H infinity filter (DHIF) for electric vehicles (EVs) is proposed. The filter algorithm employs a nonlinear longitudinal vehicle model with three vehicle states. A hierarchical framework is engaged by the DHIF to estimate the vehicle states and inertial parameters concurrently. In order to minimise the disturbance of unknown noise, the vehicle states are estimated by using the linear H infinity filter (LHIF), while the nonlinear H infinity filter (NHIF) utilises the observed states to identify the vehicle inertial parameters. Finally, the proposed estimation method is verified and compared through the dSPACE based hardware-in-the-loop (HIL) simulation experiments. The results indicate that the DHIF-based estimation method is effective to identify the vehicle inertial parameters with high precision, remarkable robustness, and quick convergence.
AB - Accurate identification of vehicle inertial parameters is essential to the design of vehicle dynamics control systems. In this paper, a novel vehicle inertial parameter identification method based on the dual H infinity filter (DHIF) for electric vehicles (EVs) is proposed. The filter algorithm employs a nonlinear longitudinal vehicle model with three vehicle states. A hierarchical framework is engaged by the DHIF to estimate the vehicle states and inertial parameters concurrently. In order to minimise the disturbance of unknown noise, the vehicle states are estimated by using the linear H infinity filter (LHIF), while the nonlinear H infinity filter (NHIF) utilises the observed states to identify the vehicle inertial parameters. Finally, the proposed estimation method is verified and compared through the dSPACE based hardware-in-the-loop (HIL) simulation experiments. The results indicate that the DHIF-based estimation method is effective to identify the vehicle inertial parameters with high precision, remarkable robustness, and quick convergence.
KW - Dual H infinity filter (DHIF)
KW - electric vehicles (EVs)
KW - hardware-in-the-loop (HIL)
KW - nonlinear vehicle model
KW - parameter identification
KW - state estimation
UR - http://www.scopus.com/inward/record.url?scp=85059945096&partnerID=8YFLogxK
U2 - 10.1080/00423114.2019.1566559
DO - 10.1080/00423114.2019.1566559
M3 - Article
AN - SCOPUS:85059945096
SN - 0042-3114
VL - 58
SP - 28
EP - 48
JO - Vehicle System Dynamics
JF - Vehicle System Dynamics
IS - 1
ER -