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.
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References
Boada, B. L., Garcia-Pozuelo, D., Boada, M. J. L. and Diaz, V. (2016). A constrained dual Kalman filter based on pdf truncation for estimation of vehicle parameters and road bank angle: Analysis and experimental validation. IEEE Trans. Intelligent Transportation Systems 18, 4, 1006–1016.
Callejo, A., Garcia de Jalon, J. Luque, P. and Mantaras, D. A. (2015). Sensitivity-based, multi-objective design of vehicle suspension systems. J. Computational and Nonlinear Dynamics 10, 3, 031008.
De Bruyne, S., Van der Auweraer, H., Diglio, P. and Anthonis, J. (2011). Online estimation of vehicle inertial parameters for improving chassis control systems. IFAC Proc. Volumes 44, 1, 1814–1819.
Goodarzi, A. and Esmailzadeh, E. (2007). Design of a VDC system for all-wheel independent drive vehicles. IEEE-ASME Trans. Mechatronics 12, 6, 632–639.
Guo, J., Luo, Y., Hu, C., Tao, C. and Li, K. (2019). Robust combined lane keeping and direct yaw moment control for intelligent electric vehicles with time delay. Int. J. Automotive Technology 20, 2, 289–296.
Huang, X. and Wang, J. (2013). Longitudinal motion based lightweight vehicle payload parameter real-time estimations. J. Dynamic Systems, Measurement, and Control 135, 1, 011013.
Jin, X., Wang, J., He, X., Yan, Z., Xu, L., Wei, C. and Yin, G. (2023a). Improving vibration performance of electric vehicles based on in-wheel motor-active suspension system via robust finite frequency control. IEEE Trans. Intelligent Transportation Systems 24, 2, 1631–1643.
Jin, X., Wang, J., Yan, Z., Xu, L., Yin, G. and Chen, N. (2022). Robust vibration control for active suspension system of in-wheel-motor-driven electric vehicle via μ-synthesis methodology. J. Dynamic Systems, Measurement, and Control 144, 5, 051007.
Jin, X., Wang, Q., Yan, Z. and Yang, H. (2023b). Nonlinear robust control of trajectory-following for autonomous ground electric vehicles with active front steering system. AIMS Math 8, 5, 11151–11179.
Jin, X., Yin, G. and Chen, N. (2019). Advanced estimation techniques for vehicle system dynamic state: A survey. Sensors, 19, 4289.
Krishna Teja Mantripragada, V. and Krishna Kumar, R. (2020). Sensitivity analysis of tyre characteristic parameters on ABS performance. Vehicle System Dynamics 60, 1, 47–72.
Lian, J., Wang, X. R., Li, L. H., Zhou, Y. F., Yu, S. Z. and Liu, X. J. (2020). Plug-in HEV energy management strategy based on SOC trajectory. Int. J. Vehicle Design 82, 1–4, 1–17.
Lin, C., Gong, X., Xiong, R. and Cheng, X. (2017). A novel H∞ and EKF joint estimation method for determining the center of gravity position of electric vehicles. Applied Energy, 194, 609–616.
Liu, W., Xia, X., Xiong, L., Lu, Y., Gao, L. and Yu, Z. (2021). Automated vehicle sideslip angle estimation considering signal measurement characteristic. IEEE Sensors J. 21, 19, 21675–21687.
Mousavi Bideleh, S. M. and Berbyuk, V. (2016). Global sensitivity analysis of bogie dynamics with respect to suspension components. Multibody System Dynamics, 37, 145–174.
Qi, H., Zhang, N., Chen, Y. and Tan, B. (2021). A comprehensive tune of coupled roll and lateral dynamics and parameter sensitivity study for a vehicle fitted with hydraulically interconnected suspension system. Proc. Institution of Mechamical Engineers, Part D: J. Automoblie Engineering 235, 1, 143–161.
Sun, Y., Li, L., Yan, B., Yang, C. and Tang, G. (2016). A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle’ mass for a hybrid electric bus. Mechanical Systems and Signal Processing, 68, 416–430.
Wang, J. and Hsieh, M. F. (2009). Vehicle yaw-inertia-and mass-independent adaptive steering control. Proc. Institution of Mechamical Engineers, Part D: J. Automoblie Engineering 223, 9, 1101–1108.
Wenzel, T. A., Burnham, K. J., Blundell, M. V. and Williams, R. A. (2006). Dual extended Kalman filter for vehicle state and parameter estimation. Vehicle System Dynamics 44, 2, 153–171.
Xia, X., Hang, P., Xu, N., Huang, Y., Xiong, L. and Yu, Z. (2021a). Advancing estimation accuracy of sideslip angle by fusing vehicle kinematics and dynamics information with fuzzy logic. IEEE Trans. Vehicular Technology 70, 7, 6577–6590.
Xia, X., Hashemi, E., Xiong, L. and Khajepour, A. (2022). Autonomous vehicle kinematics and dynamics synthesis for sideslip angle estimation based on consensus kalman filter. IEEE Trans. Control Systems Technology 31, 1, 179–192.
Xia, X., Hashemi, E., Xiong, L., Khajepour, A. and Xu, N. (2021b). Autonomous vehicles sideslip angle estimation: Single antenna GNSS/IMU fusion with observability analysis. IEEE Internet of Things J. 8, 19, 14845–14859.
Xia, X., Xiong, L., Lu, Y., Gao, L. and Yu, Z. (2021c). Vehicle sideslip angle estimation by fusing inertial measurement unit and global navigation satellite system with heading alignment. Mechanical Systems and Signal Processing, 150, 107290.
Xiong, L., Xia, X., Lu, Y., Liu, W., Gao, L., Song, S. and Yu, Z. (2020). IMU-based automated vehicle body sideslip angle and attitude estimation aided by GNSS using parallel adaptive Kalman filters. IEEE Trans. Vehocular Technology 69, 10, 10668–10680.
Zhao, J., Wong, P. K., Ma, X. and Xie, Z. (2017). Chassis integrated control for active suspension, active front steering and direct yaw moment systems using hierarchical strategy. Vehicle System Dynamics 55, 1, 72–103.
Acknowledgement
This work is supported by the National Science Foundation of China (51905329), Foundation of State Key Laboratory of Automotive Simulation and Control (20181112).
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Jin, X., Wang, Z., Yang, J. et al. Novel Payload Parameter Sensitivity Analysis on Observation Accuracy of Lightweight Electric Vehicles. Int.J Automot. Technol. 24, 1313–1324 (2023). https://doi.org/10.1007/s12239-023-0106-6
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DOI: https://doi.org/10.1007/s12239-023-0106-6