Acknowledgement
The authors are thankful for the financial support through The Ministry of Higher Education under Universiti Teknologi Malaysia for UTM Encouragement Grant with vote number of Q.J130000.3851.20J63.
References
- Farmann, A., Waag, W., Marongiu, A., Sauer, D.U.: Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles. J. Power Sources 281, 114-130 (2015). https://doi.org/10.1016/j.jpowsour.2015.01.129
- Yang, B., et al.: Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: a critical comprehensive survey. J. Energy Storage 39, 1072 (2021). https://doi.org/10.1016/j.est.2021.102572
- Xu, W., Xu, J., Yan, X.: Lithium-ion battery state of charge and parameters joint estimation using cubature Kalman filter and particle filter. J. Power Electron. 20(1), 292-307 (2020). https://doi.org/10.1007/s43236-019-00023-4
- Li, Y., Wei, Z., Xiong, B., Vilathgamuwa, D.M.: Adaptive ensemble-based electrochemical-thermal degradation state estimation of lithium-ion batteries. IEEE Trans. Ind. Electron. 69(7), 6984-6996 (2022). https://doi.org/10.1109/TIE.2021.3095815
- Li, Y., Xiong, B., Vilathgamuwa, D.M., Wei, Z., Xie, C., Zou, C.: Constrained ensemble Kalman filter for distributed electrochemical state estimation of lithium-ion batteries. IEEE Trans. Ind. Informatics 17(1), 240-250 (2021). https://doi.org/10.1109/TII.2020.2974907
- Plett, G.L.: Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: part 1. Background. J. Power Sources 134(2), 252-261 (2004). https://doi.org/10.1016/j.jpowsour.2004.02.031
- Plett, G.L.: Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: part 2. Modeling and identification. J. Power Sources 134(2), 262-276 (2004). https://doi.org/10.1016/j.jpowsour.2004.02.032
- Plett, G.L.: Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. state and parameter estimation. J. Power Sources 134(2), 277-292 (2004). https://doi.org/10.1016/j.jpowsour.2004.02.033
- Zhang, F., Yin, L., Kang, J.: Enhancing stability and robustness of state-of-charge estimation for lithium-ion batteries by using improved adaptive Kalman filter algorithms. Energies (2021). https://doi.org/10.3390/en14196284
- Xiong, R., He, H., Sun, F., Zhao, K.: Evaluation on state of charge estimation of batteries with adaptive extended Kalman filter by experiment approach. IEEE Trans. Veh. Technol. 62(1), 108-117 (2013). https://doi.org/10.1109/TVT.2012.2222684
- Charkhgard, M., Farrokhi, M.: State-of-charge estimation for lithium-ion batteries using neural networks and EKF. IEEE Trans. Ind. Electron. 57(12), 4178-4187 (2010). https://doi.org/10.1109/TIE.2010.2043035
- Xiong, R., Gong, X., Mi, C.C., Sun, F.: A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter. J. Power Sources 243, 805-816 (2013). https://doi.org/10.1016/j.jpowsour.2013.06.076
- Plett, G.L.: Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: part 1: introduction and state estimation. J. Power Sources 161(2), 1356-1368 (2006). https://doi.org/10.1016/j.jpowsour.2006.06.003
- Plett, G.L.: Sigma-point kalman filtering for battery management systems of lipb-based hev battery packs: part 2: simultaneous state and parameter estimation. J. Power Sources 161(2), 1369-1384 (2006). https://doi.org/10.1016/j.jpowsour.2006.06.004
- Yao, L.W., Aziz, J.A., Idris, N.R.N.: State-of-charge estimation for lithium-ion battery using Busse's adaptive unscented Kalman filter. IEE Conf. Energy Convers. (2016). https://doi.org/10.1109/CENCON.2015.7409544
- Sun, F., Hu, X., Zou, Y., Li, S.: Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles. Energy 36(5), 3531-3540 (2011). https://doi.org/10.1016/j.energy.2011.03.059
- Ouyang, Q., Ma, R., Wu, Z., Xu, G., Wang, Z.: Adaptive square-root unscented Kalman filter-based state-of-charge estimation for lithium-ion batteries with model parameter online identification. Energies (2020). https://doi.org/10.3390/en13184968
- Zhang, C., Yan, F., Du, C., Rizzoni, G.: An improved model-based self-adaptive filter for online state-of-charge estimation of Li-Ion batteries. Appl. Sci. (2018). https://doi.org/10.3390/app8112084
- Zhou, W., Hou, J.: A new adaptive robust unscented Kalman filter for improving the accuracy of target tracking. IEEE Access 7, 77476-77489 (2019). https://doi.org/10.1109/ACCESS.2019.2921794
- Song, Q.: An adaptive UKF algorithm for the state parameter estimations of a mobile robot. Acta Autom. Sin. (2008). https://doi.org/10.3724/SP.J.1004.2008.00072
- Shi, Y., Han, C., Liang, Y.: Adaptive UKF for target tracking with unknown process noise statistics. Inf. Fusion 2009(1), 1815-1820 (2009)
- Mohamed, A., Schwarz, K.: Adaptive Kalman filtering for INS/GPS. J. Geod. 73, 193-203 (1999). https://doi.org/10.1007/s001900050236
- Sun, F., Xiong, R., He, H.: A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique. Appl. Energy 162, 1399-1409 (2016). https://doi.org/10.1016/j.apenergy.2014.12.021
- Busse, F., How, J., Simpson, J.: Demonstration of adaptive extended kalman filter for low earth orbit formation estimation using CDGPS. Navigation 50(2), 1-12 (2003). https://doi.org/10.1002/j.2161-4296.2003.tb00320.x/abstract
- Plett, G.: Battery management systems, volume II: equivalent-circuit methods. Artech 111-112 (2015)