A Methodology for Payload Parameter Sensitivity Analysis of Lightweight Electric Vehicle State Prediction
Year: 2025
Type: article
Abstract: The Light Electric Vehicle (LEV) possesses the advantages of energy efficiency, environmental friendliness, and excellent control in traffic. Nevertheless, a decrease in vehicle mass and size directly affects the active safety system control and state estimation system of LEVs. This paper presents a novel and efficient analysis technique for evaluating the sensitivity of payload parameter variations on the response and state estimation of the LEV system. Firstly, a dynamic model of the LEV is established, taking into account the payload parameters. Next, the accuracy of the payload parameter trajectory sensitivity is calculated using a combination of the perturbation analysis method and the second-order central difference method. An unscented Kalman filter is specifically developed to estimate various vehicle states, including the sideslip angle, longitudinal velocity, and roll angle. The significance of payload parameters on observation accuracy when payload parameters vary during basic state estimation is assessed. The simulation results obtained using Matlab/Simulink-Carsim® 2019 demonstrate the ability of the method to effectively depict the correlation between the payload parameters and the system state estimation. Quantitatively, the sideslip angle is most sensitive to the payload mass (average sensitivity: 2.15 × 10−2), while the longitudinal velocity is predominantly affected by the payload’s longitudinal position. By integrating perturbation analysis with the central difference method, this approach provides a novel and efficient framework for LEV sensitivity analysis, and it is valuable for the design and estimation of the LEV controller.
Source: Electronics
Institution: Shanghai University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416088677