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International Journal of Automotive Technology > Volume 24(5); 2023 > Article
International Journal of Automotive Technology 2023;24(5): 1313-1324.
doi: https://doi.org/10.1007/s12239-023-0106-6
NOVEL PAYLOAD PARAMETER SENSITIVITY ANALYSIS ON OBSERVATION ACCURACY OF LIGHTWEIGHT ELECTRIC VEHICLES
Xianjian Jin 1,2, Zhaoran Wang 1, Junpeng Yang 1, Liwei Xu 3, Guodong Yin 3
1School of Mechatronic Engineering and Automation, Shanghai University
2State Key Laboratory of Automotive Simulation and Control, Jilin University
3School of Mechanical Engineering, Southeast University
PDF Links Corresponding Author.  Xianjian Jin  , Email. xianjianjin@shu.edu.cn
ABSTRACT
Lightweight electric vehicles (LEVs) possess great advantages in the viewpoint of fuel consumption, environment protection and traffic mobility. However, due to the drastic reduction of vehicle weights and body size, the effects of payload parameter variation in LEV control and estimation system become much more pronounced and have to be systematically analysed. This paper proposes a novel payload parameter sensitivity analysis to provide quantitative insight into the sensitivity of payload parameter on the LEV system responses and state estimation. The analysis-oriented LEV dynamic model considering payload parameter variations is developed. Then, the trajectory sensitivity index of the influential parameters is defined and derived with the perturbation approach, the median method is used to improve the calculation accuracy for the trajectory sensitivity of payload parameter. Finally, the extended Kalman filter is designed to show the effect and importance of the sensitive payload parameters on the observation accuracy, the payload parameter variations along with fundamental state estimation such as vehicle sideslip angle, longitudinal velocity and vehicle roll angle are analysed. Simulation results with Matlab/Simulink-Carsim® show that the proposed method can accurately describe the relationship between the vehicle payload parameters and system state estimation, which is helpful to design and evaluate LEV controller and observer performances.
Key Words: Electric vehicles, Payload parameter, Sensitivity analysis, Vehicle dynamics 1. INTRODUCTION Emerging lightweight

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