Journal
MECHANISM AND MACHINE THEORY
Volume 133, Issue -, Pages 606-624Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.mechmachtheory.2018.12.008
Keywords
In-wheel-motor-drive electric vehicle; State and parameter estimation; Genetic algorithm; Modified particle filter
Categories
Funding
- Ministry of Science and Technology of the People's Republic of China [2017YFB0103600]
Ask authors/readers for more resources
This paper presents a modified particle filter (MPF) to estimate vehicle states and parameter with high precision and robustness under complex noises and sensor fault conditions. To deal with the particle impoverishment issue, the vector particle swarm of the multivariable system is separated into univariate particle swarms, which are diversified with the selection, crossover and mutation operations of the genetic algorithm (GA) while maintaining the mean value and enlarging the standard deviation. The effectiveness of the proposed estimation scheme is verified under the scenarios of the stochastic and needling noises and acceleration sensor faults through the Carmaker-Simulink joint simulations based on typical maneuvers, outperforming the commonly-used vehicle state estimators including the unscented Kalman filter (UKF) and the unscented particle filter (UPF). (C) 2018 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available