State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine

Yi Z, Chen Z, Yin K, Wang L, Wang K. Sensing as the key to the safety and sustainability of new energy storage devices. Prot Control Mod Power Syst 2023;8:27.

Chen C, Xiong R, Yang R, Li H. A novel data-driven method for mining battery opencircuit voltage characterization. Green Energy and Intelligent Transportation 2022;1(1):100001.

He H, Sun F, Wang Z, Lin C, Zhang C, Xiong R, et al. China's battery electric vehicles lead the world: achievements in technology system architecture and technological breakthroughs[J]. Green Energy and Intelligent Transportation; 2022. p. 100020.

Li S, Zhang C, Du J, Cong X, Zhang L, Jiang Y, et al. Fault diagnosis for lithium-ion batteries in electric vehicles based on signal decomposition and two-dimensional feature clustering. Green Energy and Intelligent Transportation 2022;1(1):100009.

Li X, Huang Z, Hua W, Rao L, Tian Y, Tian J. Mechanical vibration modeling and characterization of a plastic-cased lithium-ion battery. Green Energy and Intelligent Transportation 2022;1(2):100006.

Zhou Z, Liu Y, You M, Xiong R, Zhou X. Two-stage aging trajectory prediction of LFP lithium-ion battery based on transfer learning with the cycle life prediction. Green Energy and Intelligent Transportation, 1(1), 100008.

Li W, Rentemeister M, Badeda J, Jöst D, Schulte D, Sauer D. U. Digital twin for battery systems: cloud battery management system with online state-of-charge and state-of-health estimation. J Energy Storage, 30, 101557.

Fan Y, Xiao F, Li C, Yang G, Tang X. A novel deep learning framework for state of health estimation of lithium-ion battery. J Energy Storage, 32, 101741.

Li Y, Sheng H, Cheng Y, Stroe D. I, Teodorescu R. State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis. Appl Energy, 277, 115504.

Song L, Zhang K, Liang T, Han X, Zhang Y. Intelligent state of health estimation for lithium-ion battery pack based on big data analysis. J Energy Storage, 32, 101836.

Chen C, Xiong R, Yang R, Li H. A novel data-driven method for mining battery opencircuit voltage characterization. Green Energy and Intelligent Transportation 2022;1(1):100001.

Fang D, Wu W, Li J, Yuan W, Liu T, Dai C, et al. Performance simulation method and state of health estimation for lithium-ion batteries based on aging-effect coupling model. Green Energy and Intelligent Transportation 2023;2(3):100082.

Liu B, Tang X, Gao F. Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model. Electrochim 2020;344:136098.

Calborean A, Bruj O, Murariu T, Morari C. Resonance frequency analysis of lead -acid cells: an EIS approach to predict the state-of-health. J Energy Storage 2020;27:101143.

Mawonou KS, Eddahech A, Dumur D, Beauvois D, Godoy E. Improved state of charge estimation for Li-ion batteries using fractional order extended Kalman filter. J Power Sources 2019;435:226710.

Zhang M, Yang D, Du J, Sun H, Li L, Wang L, et al. A review of SOH prediction of Liion batteries based on data-driven algorithms. Energies 2023;16(7):3167.

Zhang M, Liu Y, Li D, et al. Electrochemical impedance spectroscopy: a new chapter in the fast and accurate estimation of the state of health for lithium-ion batteries. Energies 2023;16(4):1599.

Chang C, Wang Q, Jiang J, Wu T. Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm. J Energy Storage 2021;38:102570.

Yu X, Ma N, Zheng L, Wang L, Wang K. Developments and applications of artificial intelligence in music education. Technologies 2023;11:42.

Zhang S, Zhai B, Guo X, Wang K, Peng N, Zhang X. 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 2019;26:100951.

Dai H, Zhao G, Lin M, Wu J, Zheng G. A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain. IEEE Trans Ind Electron 2018;66(10):7706–16.

Shen S, Sadoughi M, Chen X, Hong M, Hu C. A deep learning method for online capacity estimation of lithium-ion batteries. J Energy Storage 2019;25:100817.

Patil MA, Tagade P, Hariharan KS, Kolake SM, Song T, Yeo T, et al. A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation. Appl Energy 2015;159:285–97.

Yu Q, Liu Y, Long S, Jin X, Li J, Shen W. A branch current estimation and correction method for a parallel connected battery system based on dual BP neural networks. Green Energy and Intelligent Transportation 2022;1(2):100029.

Sun T, Xu B, Cui Y, Feng X, Han X, Zheng Y. A sequential capacity estimation for the lithium-ion batteries combining incremental capacity curve and discrete Arrhenius fading model. J Power Sources 2021;484:229248.

Lin H, Kang L, Xie D, Linghu J, Li J. Online state-of-health estimation of lithium-ion battery based on incremental capacity curve and BP neural network. Batteries 2022;8(4):29.

Chang C, Wang Q, Jiang J, Wu T. Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm. J Energy Storage 2021;38:102570.

Huang GB, Zhu QY, Siew CK. Extreme learning machine: theory and applications. Neurocomputing 2006;70(1–3):489–501.

Huang GB, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 2011;42(2):513–29.

Gautam SS, Abhishekh. A novel moving average forecasting approach using fuzzy time series data set. Journal of Control, Automation and Electrical Systems 2019;30:532–44.

Xin Peizhe, Liu Ying, Yang Nan, Song Xuankun, Huang Yu. Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation. Global Energy Interconnection 2020;3(3):247–58.

Peng X, Li Y. A fusion method based on EEMD, Pearson correlation analysis, improved LSTM, and Gaussian function-Trust region algorithm for state of health Prediction of lithium-ion Batteries. Journal of Electrochemical Energy Conversion and Storage 2022;19(3):030903.

Syed MA, Khalid M. Moving regression filtering with battery state of charge feedback control for solar PV firming and ramp rate curtailment. IEEE Access 2021;9:13198–211.

Jiaqiang E, Zeng Yan, Jin Yu, Zhang Bin, Huang Zhonghua, Wei Kexiang, et al. Heat dissipation investigation of the power lithium-ion battery module based on orthogonal experiment design and fuzzy grey relation analysis. Energy 2020;211:118596.

Thi Kim Lien Nguyen, Hoang Nga Le, Van Hai Ngo Bang An Hoang. CRITIC method and grey system theory in the study of global electric cars. World Electric Vehicle Journal 2020;11(4):79.

Yao Lei, Wen Jishu, Xu Shiming, Zheng Jie, Hou Junjian, Fang Zhanpeng, et al. State of health estimation based on the Long short-term memory network using incremental capacity and transfer learning. Sensors 2022;22(20):7835.

Xing Shu, Li Guang, Zhang Yuanjian, Shen Jiangwei, Chen Zheng, Liu Yonggang. Online diagnosis of state of health for lithium-ion batteries based on short-term charging profiles. J Power Sources 2020;471:228478.

Ma Yanying, Wu Lifeng, Guan Yong, Peng Zhen. The capacity estimation and cycle life prediction of lithium-ion batteries using a new broad extreme learning machine approach. J Power Sources 2020;476:228581.