Open AccessEffect of vehicle mass changes on the accuracy of Kalman filter estimation of electric vehicle speed
Author(s) -
Hodgson David,
Mecrow Barrie Charles,
Gadoue Shady M.,
Slater Howard J.,
Barrass Peter G.,
Giaouris Damian
Publication year - 2013
Publication title -
iet electrical systems in transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.588
H-Index - 26
ISSN - 2042-9746
DOI - 10.1049/iet-est.2012.0027
Subject(s) - control theory (sociology) , kalman filter , electric vehicle , drivetrain , estimator , noise (video) , automotive engineering , controller (irrigation) , engineering , extended kalman filter , torque , computer science , mathematics , power (physics) , physics , statistics , agronomy , control (management) , quantum mechanics , artificial intelligence , biology , image (mathematics) , thermodynamics
The mechanical drivetrain dynamics of electric vehicles can have a detrimental effect on the performance of the vehicle speed controller. It is common for the speed measurement from the motor encoder to be used for the vehicle speed feedback, after taking into account the gear ratio, but it is not valid to assume that motor and vehicle speeds are equal during transient conditions. In this study it is shown how the vehicle driveability can be greatly improved if estimates of vehicle speed and mass are obtained. Estimates of vehicle speed and mass have been realised using a Kalman filter (KF) and a recursive least‐squares estimator, and validated with experimental results. The study also shows the importance of finding the most optimal process noise matrix Q for the KF, this has been carried out using a genetic algorithm, with the estimation accuracy then compared with varying vehicle mass.