Adhesion Force Estimation of Railway Vehicles using Intelligent Square Root Cubature Kalman Filter

[1] B.Chen; Z.Huang, W.Liu, R.Zhang,F.Zhou, J.Peng, “A Novel Adhesion Force Estimation for Railway Vehicles Using an Extended State Observer”, IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society,2019.

[2] S.Shrestha, Q.Wu, & M.Spiryagin, “Review of adhesion estimation approaches for rail vehicles,” International Journal of Rail Transportation, vol.7,no.2, 79-102,2019.

[3] A.Onat, & P.Voltr, “Velocity measurement-based friction estimation for railway vehicles running on adhesion limit: swarm intelligence-based multiple models approach,” Journal of Intelligent Transportation Systems, 24(1), 93-107,2020.

[4] C.Schwarz,& A.Keck, “ Simultaneous Estimation of Wheel-Rail Adhesion and Brake Friction Behaviour,” IFAC-PapersOnLine, 53(2), 8470-8475,2020.

[5] S.Shrestha, M.Spiryagin, & Q.Wu, “ Friction condition characterization for rail vehicle advanced braking system,” Mechanical Systems and Signal Processing, 134, 106324,2019.

[6] Y. Liu et al., “Research on a lightweight rail surface condition identification method,” Applied Sciences, vol. 15, no. 6, p. 3391, 2025.

[7] K. Zhao, P. Li, C. Zhang, J. He, Y. Li, and T. Yin, “Online accurate estimation of the wheel-rail adhesion coefficient and optimal adhesion antiskid control of heavy-haul electric locomotives based on asymmetric barrier lyapunov function,” Journal of Sensors, vol. 2018, 2018.

[8] C. V. van de Merwe,J. D. Le Roux, “Estimation of Locomotive Adhesion Coefficients and Slip Ratios” , American Control Conference (ACC),2023.

[9] B.Leng,C.Tian,X.Hou,L.Xiong,W.Zhao,Z.Yu, “TireRoad Peak Adhesion Coefficient Estimation Based on Multisource Information Assessment”, IEEE Transactions on Intelligent Vehicles,vol.8,no.7,2023.

[10] Jing, H., Guangwei, L., Changfan, Z., et al.: “Maximum likelihood identification method for adhesion performance parameters of heavy duty locomotives”, J. Electron. Meas. Instrum., 2017, vol.31, no.2, pp. 170–177.

[11] P. Pichlik and J. Zdenek, “Locomotive wheel slip control method based on an unscented kalman filter,” IEEE Transactions on Vehicular Technology, vol. 67, no. 7, pp. 5730–5739, 2018.

[12] Hussain, I., Mei, T.X., and Jones, A.H., “Modeling and Estimation of Nonlinear Wheel-Rail Contact Mechanics”,Proceedings of Twentieth International Conference on System Engineering, pp. 219-223,Coventry, UK, 2009.

[13] T.X. Mei, and I. Hussain, “Detection of Wheel-Rail Contact Conditions for Improved Traction Control”, Proceedings of 4th International Conference on Railway Traction Systems, Birmingham, UK, 2010.

[14] I. Hussain, and T.X. Mei, “Multi Kalman Filtering Approach for Estimation of Wheel-Rail Contact Conditions”, Proceedings of the United Kingdom Automatic Control Conference, Coventry, UK, 2010.

[15] M. Wu and J. Zhou, "Wheel-Rail Adhesion Test Based on Full Scale Roller Rig," in Resilience and Sustainable Transportation Systems: Proceedings of the 13th Asia Pacific Transportation Development Conference, Shanghai, China, Jun. 2020, pp. 564–572.

[16] C. Wang a, L.B. Shi a, H.H. Ding a, W.J. Wang a, R. Gal as b, J. Guo a, Q.Y. Liu a, Z.R. Zhou a, M. Omasta b, “Adhesion and damage characteristics of wheel/rail using different mineral particles as adhesion enhancers”, Wear vol.477, 2021.

[17] I. Hussain, T. X. Mei, &, R. T. Ritchings , “Estimation of wheel-rail contact conditions and adhesion using the multiple model approach”, Vehicle System Dynamics,2013, vol.51,no.1,pp. 32–53, https://doi.org/10.1080/00423114.2012.708759

[18] J.Zhou, M.Wu, C.Tian, Z.Yuan, C.Chen , “Experimental investigation on wheel–rail adhesion characteristics under water and large sliding conditions”, Industrial Lubrication and Tribology, vol.73,no.2,2020.

[19] I. Hussain, “ Multiple Model Based Real Time Estimation of Wheel-Rail Contact Conditions”, PhD thesis, University of Salford,2012, http://usir.salford.ac.uk/id/eprint/38094.

[20] Y. Zhao,, B. Liang, & S.Iwnicki, “ Friction coefficient estimation using an unscented Kalman filter”, International Journal of Vehicle Mechanics and Mobility, vol.52,no. 1, pp.220–234,2014. https://doi.org/10.1080/00423114.2014.891757.

[21] R. Havangi and M. Moradi, “PSO-based EKF wheel–rail adhesion estimation,” International Journal of Industrial Electronics, Control and Optimization (IECO), vol. 6, no. 1, pp. 49–62, 202.

[22] S. Sahl, E. Song, and D. Niu, "Robust Cubature Kalman Filter for Moving-Target Tracking with Missing Measurements," Sensors, vol. 24, no. 2, p. 392, Jan. 2024.

[23] Q. Chen, C. Yin, J. Zhou, Y. Wang, X. Wang, and, C. Chen, "Hybrid consensus-based cubature Kalman filtering for distributed state estimation in sensor networks, " IEEE Sensors J., vol. 18, pp. 4561–4569, Jun. 2018.

[24] B. Gao, G. Hu, L. Zhang, Y. Zhong, and X. Zhu, "Cubature Kalman Filter with Closed-Loop Covariance Feedback Control for Integrated INS/GNSS Navigation," Chinese Journal of Aeronautics, vol. 36, no. 5, pp. 363–376, May 2023

[25] Q. Chen, J. Gong, X. Ge, S. Chen, and K. Wang, “Estimation of wheel-rail forces based on the STF-SCKFNE algorithm,” Measurement, vol. 236, p. 114974, 2024.

[26] L. Quan, R. Chang, and C. Guo, “Vehicle state and road adhesion coefficient joint estimation based on high-order cubature Kalman algorithm,” Applied Sciences, vol. 13, no. 19, p. 10734, 2023.

[27] W. Wang, J. Fu, S. Bao, and X. Liu, “Vehicle state estimation using interacting multiple model based on square root cubature Kalman filter,” Applied Sciences, vol. 11, no. 22, p. 10772, 2021.