Sliding mode-based H-infinity filter for SOC estimation of lithium-ion batteries
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Zhang C, Yang F, Ke XY, Liu ZF, Yuan C (2019) Predictive modeling of energy consumption and greenhouse gas emissions from autonomous electric vehicle operations. Appl Energy 254:113597
Saha P, Dey S, Khanra M (2020) Modeling and State-of-Charge Estimation of Supercapacitor Considering Leakage Effect. IEEE Trans on Ind Electron 67:350–357
Berecibar M, Gandiaga I, Villarreal I, Omar N, Mierlo JV, Bossche PVD (2016) Critical review of state of health estimation methods of Li-ion batteries for real applications. Renew Sustain Energy Rev 56:572–587
Hannan MA, Lipu MSH, Hussain A, Mohamed A (2017) A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations. Renew Sustain Energy Rev 78:834– 854
Wang YB, Fang HZ, Zhou L, Wada T (2017) Revisiting the state-of-charge estimation for lithium-ion batteries: A methodical investigation of the extended Kalman filter approach. IEEE Contr Syst 37:73–96
Li XY, Wang ZP, Zhang L (2019) Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles. Energy 174:33–44
Xiong R, Tian JP, Mu H, Wang C (2017) A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries. Appl Energy 207:372–383
Zheng FD, Xing YJ, Jiang JC, Sun BX, Kim J, Pecht M (2016) Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries. Appl Energy 183:513–525
Hu XS, Jiang HF, Feng F, Liu B (2020) An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management. Appl Energy 257:114019
Ghalkhani M, Bahiraei F, Nazi GA, Saif M (2017) ElectrochemicaleThermal model of pouch-type lithium-ion batteries. Electrochim Acta 247:569–587
Wang QK, He YJ, Shen JN, Ma ZF, Zhong GB (2017) A unified modeling framework for lithium-ion batteries: an artificial neural network based thermal coupled equivalent circuit model approach. Energy 138:118–132
Jiao M, Wang DQ, Qiu LJ (2020) GRU-RNN based momentum optimized algorithm for SOC estimation. J Power Sources 459:228051
Chemali E, Kollmeyer PJ, Preindl M, Ahmed R, Emadi A (2018) Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries. IEEE Trans Ind Electron 65:6730–6739
Sheng H, Xiao J (2015) Electric vehicle state of charge estimation: nonlinear correlation and fuzzy support vector machine. J Power Sources 281:131–137
Xia B, Cui D, Sun Z, Lao Z, Zhang R, Wang W (2018) State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network. Energy 153:694–705
Moura S, Chaturvedi N, Krstic M (2012) PDE estimation techniques for advanced battery management systems; Part I: SOC estimation. Am Control Conf 2012:559–565
Klein R, Chaturvedi N, Christensen J, Ahmed J, Findeisen R, Kojic A (2013) Electrochemical model based observer design for a lithiumion battery. IEEE Trans Contr Syst Technol 21:289–301
Tran N, Vilathgamuwa D, Li Y, Farrell TW, Choi SS, Teague J (2017) State of charge estimation of lithium ion batteries using an extended single particle model and sigma-point Kalman1 filter. In: IEEE southern power electronics conference, vol 2017, pp 624–629
Yang JF, Huang W, Xia B, Mi C (2019) The improved open-circuit voltage characterization test using active polarization voltage reduction method. Appl Energy 237:682–694
Shen YQ (2018) A chaos genetic algorithm based extended Kalman filter for the available capacity evaluation of lithium-ion batteries. Electrochim Acta 264:400–409
Zhang LJ, Peng H, Ning ZS, Mu ZQ, Sun CY (2017) Comparative research on RC equivalent circuit models for lithium-ion batteries of electric vehicles. Appl Sci 7:1002
Dai HF, Wei XZ, Sun ZC, Wang JY, Gu WJ (2012) Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications. Appl Energy 95:227–237
He HW, Qin HZ, Sun XK, Shui YP (2013) Comparison study on the battery SoC estimation with EKF and UKF algorithms. Energies 6:5088–5100
Ramadan H, Becherif M, Claude F (2017) Extended Kalman filter for accurate state of charge estimation of lithium-based batteries: a comparative analysis. Int J Hydrogen Energy 42:29033–29046
Xu J, Mi CC, Cao BG, Deng JJ, Chen ZZ, Li S (2014) The state of charge estimation of lithium-ion batteries based on a proportional-integral observer. IEEE Trans Veh Technol 63:1614–1621
Chen XP, Shen WX, Cao ZW, Kapoor A (2014) A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles. J Power Sources 2469:667–678
Chen S, Fu YH, Mi C (2013) State of charge estimation of lithium-ion batteries in electric drive vehicles using extended Kalman filtering. IEEE Trans Veh Technol 62:1020–1030
Wang YJ, Zhang CB, Chen ZH (2015) A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy. Appl Energy 137:427–434
Perez G, Garmendia M, Reynaud JF, Crego J, Viscarret U (2015) Enhanced closed loop State of Charge estimator for lithium-ion batteries based on Extended Kalman Filter. Appl Energy 155:834–845
Li WQ, Yang Y, Wang DQ, Yin SQ (2020) The multi-innovation extended Kalman filter algorithm for battery SOC estimation. Ionics 26:6145–6156
Zhu Q, Li L, Hu XS, Xiong N, Hu G (2017) H$\infty $-based nonlinear observer design for state of charge estimation of lithium-ion battery with polynomial parameters. IEEE Trans Veh Technol 66:10853–10865
Liu Z, Dang XJ (2018) A new method for State of Charge and capacity estimation of lithium-ion battery based on dual strong tracking adaptive H-infinity filter. Math Probl Eng :5218205
Farmann A, Waag W, Marongiu A (2015) Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles. J Power Sources 281:114–130
Z Wei, C Zou, F Leng, BH Soong, KJ Tseng (2018) Online model identification and state-ofcharge estimate for lithium-ion battery with a recursive total least squares-based observer. IEEE Trans Ind Electron 65:1336–1346
Chen XK, Lei H, Xiong R, Shen WX, Yang R (2019) A novel approach to reconstruct open circuit voltage for state of charge estimation of lithium ion batteries in electric vehicles. Appl Energy 255:113758
Luo JY, Peng JK, HE HW (2019) Lithium-ion battery SOC estimation study based on Cubature Kalman filter. Energy Procedia 158:3421–3426
Lao ZZ, Xia BZ, Wang W, Sun W, Lai Y, Wang M (2018) A novel method for lithium-ion battery online parameter identification based on variable forgetting factor recursive least squares. Energies 11:1358
Claude F, Becherif M, Ramadan HS (2017) Experimental validation for Li-ion battery modeling using Extended Kalman Filters. Int J Hydrogen Energy 42:25509–25517
Feng L, Ding J, Han YY (2020) Improved sliding mode based EKF for the SOC estimation of lithium-ion batteries. Ionics 26:2875–2882
Chen QY, Jiang JC, Ruan HJ (2017) Simply designed and universal sliding mode observer for the SOC estimation of lithium-ion batteries. IET Power Electron 10:697–705
Hu XS, Li SB, Peng H (2012) A comparative study of equivalent circuit models for Li-ion batteries. J Power Sources 198:359–367
Zhu R, Duan B, Zhang J, Zhang Q, Zhang Q (2020) Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter. Appl Energy 277:115494
Constantin P, Jacob B, Silviu C (2008) A robust variable forgetting factor recursive least-squares algorithm for system identification. IEEE Signal Process Lett 15:597–600
Li XL, Zhou LC, Sheng J (2014) Recursive least squares parameter estimation algorithm for dual-rate sampled-data nonlinear systems. Nonlinear Dyn 76:1327–1334
Sun F, Xiong R (2015) A novel dual-scale cell state-of-charge estimation approach for series-connected battery pack used in electric vehicles. J Power Sources 274:582–594
Thein MWL (2003) A discrete time variable structure observer for uncertain systems with measurement noise. In: Proc. IEEE conference on decision and control, vol 2003, pp 2582–2587
Harikumar K, Bera T, Bardhan R (2019) Discrete-time sliding mode observer for the state estimation of a manoeuvring target. J Syst Contr Eng 233:095965181982648