References
- Xing, Y., Ma, W.M., Tsui, K.L.: Battery management systems in electric and hybrid vehicles. Energies 4(12), 1840-1857 (2011) https://doi.org/10.3390/en4111840
- Zhang, S., Guo, X., Zhang, X.: An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery. J. Energy Storage 32, 101980 (2020) https://doi.org/10.1016/j.est.2020.101980
- Sun, D., Yu, X., Wang, C., Zhang, C., Bhagat, R.: State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator. Energy 214, 119025 (2020) https://doi.org/10.1016/j.energy.2020.119025
- Yang, B., Wang, J.T., Cao, P.L., Zhu, T.J., Shu, H.C., Chen, J., Zhang, J., Zhu, J.W.: 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, 102572 (2021) https://doi.org/10.1016/j.est.2021.102572
- Yang, N., Zhang, X., Li, G.: State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting. Electrochim. Acta 151, 63-71 (2015) https://doi.org/10.1016/j.electacta.2014.11.011
- Ouyang, T.C., Xu, P.H., Chen, J.X., Su, Z.X., Huang, G.C., Chen, N.: A novel state of charge estimation method for lithium-ion batteries based on bias compensation . Energy 226, 120348 (2021) https://doi.org/10.1016/j.energy.2021.120348
- Xing, Y.J., He, W., Pecht, M., Tsui, K.L.: State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures. Appl Energy 113, 106-115 (2014) https://doi.org/10.1016/j.apenergy.2013.07.008
- Piller, S., Perrin, M., Jossen, A.: Methods for state-of-charge determination and their applications. J. Power Sources 96(1), 113-120 (2001) https://doi.org/10.1016/S0378-7753(01)00560-2
- Chen, J.X., Feng, X., Jiang, L., Zhu, Q.: State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network. Energy 227, 120451 (2021) https://doi.org/10.1016/j.energy.2021.120451
- Sahinoglu, G.O., Pajovic, M., Sahinoglu, Z., Wang, Y., Wada, T.: Battery state-of-charge estimation based on regular/recurrent Gaussian Process Regression. IEEE Trans Ind Electron. 65(5), 4311-4321 (2018) https://doi.org/10.1109/tie.2017.2764869
- Yang, F., Li, W., Li, C., Miao, Q.: State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network. Energy 175, 66-75 (2019) https://doi.org/10.1016/j.energy.2019.03.059
- Singh, K.V., Bansal, H.O., Singh, D.: Hardware-in-the-loop implementation of ANFIS based adaptive soc estimation of lithium-ion battery for hybrid vehicle applications. J. Energy Storage 27, 101124 (2020) https://doi.org/10.1016/j.est.2019.101124
- Zhang, L., Li, K., Du, D.J., Zhang, M.: A sparse least squares support vector machine used for SOC estimation of Li-ion batteries. IFAC-PapersOnLine 52(11), 256-261 (2019) https://doi.org/10.1016/j.ifacol.2019.09.150
- Li, Y.H., Li, K., Liu, K., Wang, Y.X., Zhang, L.: Lithium-ion battery capacity estimation-A pruned convolutional neural network approach assisted with transfer learning. Appl Energy 285, 116410 (2021) https://doi.org/10.1016/j.apenergy.2020.116410
- Liu, D., Li, L., Song, Y.: Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions. Int. J. Electr. Power Energy Syst. 110, 48-61 (2019) https://doi.org/10.1016/j.ijepes.2019.02.046
- Li, S., Fang, H.J., Shi, B.: Remaining useful life estimation of Lithium-ion battery based on interacting multiple model p filter and support vector regression. Reliab Eng Syst Safe 210(8), 107542 (2021) https://doi.org/10.1016/j.ress.2021.107542
- Yang, H., Sun, X., An, Y., Zhang, X., Ma, Y.: Online parameters identification and state of charge estimation for lithium-ion capacitor based on improved Cubature Kalman filter. J Energy Storage 24, 100810 (2019) https://doi.org/10.1016/j.est.2019.100810
- Moura, S., Argomedo, F., Klein, R., Mirtabatabaei, A., Krstic, M.: Battery state estimation for a single p model with electrolyte dynamics. IEEE Trans. Contr. Syst. Technol. 25(2), 453-468 (2017) https://doi.org/10.1109/TCST.2016.2571663
- Shrivastava, P., Soon, T.K., Idris, M., Mekhilef, S.: Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries. Renew Sustain Energy Rev 113, 109233 (2019) https://doi.org/10.1016/j.rser.2019.06.040
- Li, X.Y., Wang, Z.P., Zhang, L.: Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles. Energy 174, 33-44 (2019) https://doi.org/10.1016/j.energy.2019.02.147
- Wei, Z., He, H., Pou, J., Tsui, K.L., Quan, Z., Li, Y.W.: Signal-disturbance interfacing elimination for unbiased model parameter identification of lithium-ion battery. IEEE Trans Ind Inform 17(9), 5887-5897 (2021) https://doi.org/10.1109/TII.2020.3047687
- Hu, J., He, H.W., Wei, Z.B., Li, Y.: Disturbance-immune and aging-robust internal short circuit diagnostic for lithium-ion battery. IEEE Trans Ind Electron (2021). https://doi.org/10.1109/TIE.2021.3063968
- Wei, Z., Zhao, D., He, H., Dong, W.G.: A noise-tolerant model parameterization method for lithium-ion battery management system. Appl Energy 268, 114932 (2020) https://doi.org/10.1016/j.apenergy.2020.114932
- Zhu, Q., Xu, M.G., Liu, W.Q., Zheng, M.Q.: A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended Kalman filter. Energy 187, 115880 (2019) https://doi.org/10.1016/j.energy.2019.115880
- Li, C.H., Ma, J., Yang, Y.J., Xiao, B.S.: Adaptively robust square-root cubature Kalman filter based on amending. IEEE Access 9, 47579-47587 (2021) https://doi.org/10.1109/ACCESS.2021.3068602
- Shen, Y.: Adaptive extended Kalman filter based state of charge determination for lithium-ion batteries. Electrochim. Acta 283, 1432-1440 (2018) https://doi.org/10.1016/j.electacta.2018.07.078
- Sangwan, V., Kumar, R., Rathore, A.K.: State-of-charge estimation of Li-ion battery at different temperatures using p filter. J. Eng. 18, 5320-5324 (2019) https://doi.org/10.1049/joe.2018.9234
- Zhang, K., Ma, J., Zhao, X., Zhang, D., He, Y.: State of charge estimation for lithium battery based on adaptively weighting cubature p filter. IEEE Access 7, 166657-166666 (2019) https://doi.org/10.1109/access.2019.2953478
- Shu, X., Li, G., Shen, J.W., Yan, W.S., Chen, Z., Liu, Y.G.: An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation. J. Power Sources 10, 228132 (2020)
- Dai, K., Wang, J., He, H.: An improved SOC estimator using time-varying discrete sliding mode observer. IEEE Access 7, 115463-115472 (2019) https://doi.org/10.1109/access.2019.2932507
- Wei, Z., Hu, G.J., He, H., Li, Y., Xiong, B.: Load current and state of charge co-estimation for current sensor-free lithium-ion battery. IEEE Trans. Power Electron. (2021). https://doi.org/10.1109/TPEL.2021.3068725
- Lai, X., Zheng, Y., Sun, T.: A comparative study of different equivalent circuit models for estimating state-of-charge of lithium ion batteries. Electrochim. Acta 259, 566-577 (2018) https://doi.org/10.1016/j.electacta.2017.10.153
- Campestrini, C., Heil, T., Kosch, S., Jossen, A.: A comparative study and review of different Kalman filters by applying an enhanced validation method. J. Energy Storage 8, 142-159 (2016) https://doi.org/10.1016/j.est.2016.10.004