Hybrid gray wolf optimization method in support vector regression framework for highly precise prediction of remaining useful life of lithium-ion batteries

Wang S et al (2018) A novel safety anticipation estimation method for the aerial lithium-ion battery pack based on the real-time detection and filtering. J Clean Prod 185:187–197 Wang S et al (2020) A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm. J Power Sources 471:1–13 Lai X et al (2022) Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects. Energy 238:1–14 Zhang MY et al (2022) A novel square root adaptive unscented Kalman filter combined with variable forgetting factor recursive least square method for accurate state-of-charge estimation of lithium-ion batteries. Int J Electrochem Sci 17(9):1–15 Wang S et al (2020) A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions. J Power Sources 450:1–14 Hasib SA et al (2021) A comprehensive review of available battery datasets, RUL prediction approaches, and advanced battery management. Ieee Access 9:86166–86193 Zhang J et al (2022) An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty. Reliab Eng Syst Saf 222:1–11 Li W et al (2019) An indirect RUL prognosis for lithium-ion battery under vibration stress using Elman neural network. Int J Hydrogen Energy 44(23):12270–12276 Wang F et al (2022) A transferable lithium-ion battery remaining useful life prediction method from cycle-consistency of degradation trend. J Power Sources 521:1–14 Pang X et al (2019) A lithium-ion battery RUL prediction method considering the capacity regeneration phenomenon. Energies 12(12):1–14 Downey A et al (2019) Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds. Reliab Eng Syst Saf 182:1–12 Nagulapati VM et al (2021) Capacity estimation of batteries: influence of training dataset size and diversity on data driven prognostic models. Reliab Eng Syst Saf 216:1–11 Thelen A et al (2022) Augmented model-based framework for battery remaining useful life prediction. Appl Energy 324:1–18 Zhang S, Guo X, Zhang X (2020) Multi-objective decision analysis for data-driven based estimation of battery states: a case study of remaining useful life estimation. Int J Hydrogen Energy 45(27):14156–14173 Wang F-K et al (2022) Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism. Energy 254:1–10 Zhang C, Zhao S, He Y (2022) An integrated method of the future capacity and RUL prediction for lithium-ion battery pack. IEEE Trans Veh Technol 71(3):2601–2613 Liu Q et al (2020) The remaining useful life prediction by using electrochemical model in the particle filter framework for lithium-ion batteries. IEEE Access 8:126661–126670 Wu L, Fu X, Guan Y (2016) Review of the remaining useful life prognostics of vehicle lithium-ion batteries using data-driven methodologies. Applied Sciences-Basel 6(6):1–11 Afshari SS et al (2022) Remaining useful life early prediction of batteries based on the differential voltage and differential capacity curves. IEEE Trans Instrum Meas 71:1–13 Gao D, Huang M (2017) Prediction of remaining useful life of lithium-ion battery based on multi-kernel support vector machine with particle swarm optimization. J Power Electron 17(5):1288–1297 Gou B, Xu Y, Feng X (2020) State-of-health estimation and remaining-useful-life prediction for lithium-ion battery using a hybrid data-driven method. IEEE Trans Veh Technol 69(10):10854–10867 Khumprom P, Yodo N (2019) A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm. Energies 12(4):1–21 Liu K et al (2021) A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery. IEEE Trans Industr Electron 68(4):3170–3180 Long B et al (2019) Prognostics comparison of lithium-ion battery based on the shallow and deep neural networks model. Energies 12(17):1–13 Park K et al (2020) LSTM-based battery remaining useful life prediction with multi-channel charging profiles. IEEE Access 8:20786–20798 Lai X et al (2021) Rapid sorting and regrouping of retired lithium-ion battery modules for echelon utilization based on partial charging curves. IEEE Trans Veh Technol 70(2):1246–1254 Camargos MO et al (2020) Data-driven prognostics of rolling element bearings using a novel error based evolving Takagi-Sugeno fuzzy model. Applied Soft Comput 96:1–16 Chehade AA, Hussein AA (2022) A multioutput convolved Gaussian process for capacity forecasting of li-ion battery cells. IEEE Trans Power Electron 37(1):896–909 Guha A, Patra A (2018) State of health estimation of lithium-ion batteries using capacity fade and internal resistance growth models. IEEE Trans Transp Electrification 4(1):135–146 Wang C et al (2021) Prognostics and health management system for electric vehicles with a hierarchy fusion framework: concepts, architectures, and methods. Advances In Civil Engineering 2021:1–11 Liu H, Song W, Zio E (2022) Fractional Levy stable motion with LRD for RUL and reliability analysis of li-ion battery. ISA Trans 125:360–370 Zhang X et al (2021) Time-series regeneration with convolutional recurrent generative adversarial network for remaining useful life estimation. IEEE Trans Industr Inf 17(10):6820–6831 Liao L, Koettig F (2014) Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Trans Reliab 63(1):191–207 Chen XZ et al (2012) A novel PF-LSSVR-based Framework for failure prognosis of nonlinear systems with time-varying parameters. Chin J Aeronaut 25(5):715–724 He N, Qian C, He LL (2022) Short-term prediction of remaining life for lithium-ion battery based on adaptive hybrid model with long short-term memory neural network and optimized particle filter. J Electrochemical Energy Conversion and Storage 19(3):1121–1144 Li LL et al (2019) Enhancing the lithium-ion battery life predictability using a hybrid method. Appl Soft Comput 74:110–121 Chen L et al (2020) Remaining useful life prediction of lithium-ion battery with optimal input sequence selection and error compensation. Neurocomputing 414:245–254 Xue Z et al (2020) Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression. Neurocomputing 376:95–102 Wang Y et al (2019) Remaining useful life prediction of lithium-ion batteries using support vector regression optimized by artificial bee colony. IEEE Trans Veh Technol 68(10):9543–9553 Zhao S, Zhang C, Wang Y (2022) Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network. J Energy Storage 52:1–15 Zhang S et al (2019) Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks. J Energy Storage 26:1–12 Mao L et al (2020) A LSTM-STW and GS-LM fusion method for lithium-ion battery RUL prediction based on EEMD. Energies 13(9):1–13 Li X, Ma Y, Zhu J (2021) An online dual filters RUL prediction method of lithium-ion battery based on unscented particle filter and least squares support vector machine. Measurement 184:1–13