Model based state-of-energy estimation for LiFePO4 batteries using unscented particle filter

Lü, X., Qu, Y., Wang, Y., Qin, C., Liu, G.: A comprehensive review on hybrid power system for PEMFC-HEV: issues and strategies. Energy Convers. Manag. 171, 1273–1291 (2018)

Xie, J., Ma, J., Chen, J.: Peukert-equation-based state-of-charge estimation for LiFePO4 batteries considering the battery thermal evolution effect. Energies 11, 1112 (2018)

Feng, F., Hu, X., Hu, L., Hu, F., Li, Y., Zhang, L.: Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs. Renew. Sustain. Energy Rev. 112, 102–113 (2019)

Wei, Z., Zhao, J., Ji, D., Tseng, K.J.: A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model. Appl. Energy 204, 1264–1274 (2017)

Zou, C., Manzie, C., Nešić, D., Kallapur, A.G.: Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery. J. Power Sour. 335, 121–130 (2016)

Li, X., Wang, Z., Zhang, L.: Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles. Energy 174, 33–44 (2019)

Xie, J., Ma, J., Bai, K.: State-of-charge estimators considering temperature effect, hysteresis potential, and thermal evolution for LiFePO4 batteries. Int. J. Energy Res. 42(8), 2710–2727 (2018)

Wei, Z., Zou, C., Leng, F., Soong, B.H., Tseng, K.J.: Online model identification and state-of-charge estimate for lithium-ion battery with a recursive total least squares-based observer. IEEE Trans. Ind. Electron. 65(2), 1336–1346 (2017)

Wang, Y., Zhang, C., Chen, Z.: On-line battery state-of-charge estimation based on an integrated estimator. Appl. Energy 185, 2026–2032 (2017)

Xie, J., Ma, J., Chen, J.: Available power prediction limited by multiple constraints for LiFePO4 batteries based on central difference Kalman filter. Int. J. Energy Res. 42(15), 4730–4745 (2018)

Wang, Y., Chen, Z., Zhang, C.: On-line remaining energy prediction: a case study in embedded battery management system. Appl. Energy 194, 688–695 (2017)

Wladislaw, W., Fleischer, C., Sauer, D.U.: Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. J. Power Sour. 258, 321–339 (2014)

Zhang, W., Wei, S., Ma, Z.: Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery. J. Power Sour. 289, 50–62 (2015)

Wang, Y., Zhang, C., Chen, Z.: An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles. J. Power Sour. 305, 80–88 (2016)

Dong, G., Zhang, X., Zhang, C., Chen, Z.: A method for state of energy estimation of lithium-ion batteries based on neural network model. Energy 90, 879–888 (2015)

Zhang, K.: Comparison of Nonlinear Filtering Methods for Battery State of Charge Estimation. M.S. Thesis, University of New Orleans, U.S.A. (2014)

Berrueta, A., Urtasun, A., Ursúa, A., Sanchis, P.: A comprehensive model for lithium-ion batteries: from the physical principles to an electrical model. Energy 144, 286–300 (2018)

Fotouhi, A., Auger, D.J., Propp, K., Longo, S., Wild, M.: A review on electric vehicle battery modelling: from lithium-ion toward lithium–sulphur. Renew. Sustain. Energy Rev. 56, 1008–1021 (2016)

He, H., Xiong, R., Guo, H., Li, S.: Comparison study on the battery models used for the energy management of batteries in electric vehicles. Energy Convers. Manag. 64, 113–121 (2012)

He, H., Xiong, R., Fan, J.: Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach. Energies 4, 582–598 (2011)

He, H., Xiong, R., Zhang, X., Sun, F., Fan, J.: State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model. IEEE Trans. Veh. Technol. 60(4), 1461–1469 (2011)

Plett, G.L.: Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: part 2. Modeling and identification. J. Power Sour. 134(2), 262–276 (2004)

Ma, J., Xie, J., Bai, K.: Comparative study of non-electrochemical hysteresis models for LiFePO4/graphite batteries. J. Power Electron. 18(5), 1585–1594 (2018)

Hu, X., Yuan, H., Zou, C., Li, Z., Zhang, L.: Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order calculus. IEEE Trans. Veh. Technol. 67(11), 10319–10329 (2018)

He, H., Zhang, X., Xiong, R., Xu, Y., Guo, H.: Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles. Energy 39(1), 310–318 (2012)

He, Y., Liu, X., Zhang, C., Chen, Z.: A new model for state-of-charge (SOC) estimation for high-power Li-ion batteries. Appl. Energy 101, 808–814 (2013)

Malysz, P., Ye, J., Gu, R., Yang, H., Emadi, A.: Battery state-of-power peak current calculation and verification using an asymmetric parameter equivalent circuit model. IEEE Trans. Veh. Technol. 65(6), 4512–4522 (2015)

Hu, X., Li, S., Peng, H., Sun, F.: Robustness analysis of State-of-Charge estimation methods for two types of Li-ion batteries. J. Power Sour. 217(1), 209–219 (2012)