Lithium-ion battery remaining useful life prediction: a federated learning-based approach

Abstract

In line with Industry 5.0 principles, energy systems form a vital part of sustainable smart manufacturing systems. As an integral component of energy systems, the importance of Lithium-Ion (Li-ion) batteries cannot be overstated. Accurately predicting the remaining useful life (RUL) of these batteries is a paramount undertaking, as it impacts the overall reliability and sustainably of the smart manufacturing systems. Despite various existing methods have achieved good results, their applicability is limited due to the data isolation and data silos. To address the aforementioned challenges, this paper presents a novel federated learning (FL)-based approach that predicts the RUL of Li-ion batteries, thereby contributing to the sustainability of smart manufacturing. Firstly, a denoising recursive autoencoder-based transformer (DRAT) model is devised, focusing on extracting robust and latent features for RUL prediction under various conditions. Secondly, we propose an adaptive DRAT-based federated RUL framework (Fed-DRAT) for the collaborative modeling of Li-ion batteries RUL prediction for different energy systems. Specifically, an innovative adaptive model aggregation strategy is developed to equalize the contribution weights of different participating systems and improve model performance. Our extensive experiments with Li-ion batteries datasets indicate that our proposed DRAT significantly outperforms existing methods and demonstrates superior performance in different scenarios.

Keywords

Industry 5.0 / Lithium-ion battery / Remaining useful life prediction / Transformer / Federated learning

References

[1] Ahmad T, Madonski R, Zhang D Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renew Sustain Energy Rev, 2022, 160: 112128

[2] Altinpulluk NB, Altinpulluk D, Ramanan P, et al. (2023) Federated battery diagnosis and prognosis. arXiv preprint arXiv:2310.09628 https://doi.org/10.48550/arXiv.2310.09628

[3] Ansari S, Ayob A, Lipu MH Remaining useful life prediction for lithium-ion battery storage system: a comprehensive review of methods, key factors, issues and future outlook. Energy Rep, 2022, 8: 12153-12185

[4] Arunan A, Qin Y, Li X A federated learning-based industrial health prognostics for heterogeneous edge devices using matched feature extraction. IEEE Trans Autom Sci Eng, 2023

[5] Bai G, Su Y, Rahman MM Prognostics of lithium-ion batteries using knowledge-constrained machine learning and Kalman filtering. Reliab Eng Syst Saf, 2023, 231: 108944

[6] Banabilah S, Aloqaily M, Alsayed E Federated learning review: fundamentals, enabling technologies, and future applications. Inf Process Manage, 2022, 59(6): 103061

[7] Chen D, Hong W, Zhou X. Transformer network for remaining useful life prediction of lithium-ion batteries. IEEE Access, 2022, 10: 19621-19628

[8] Dang W, Liao S, Yang B An encoder-decoder fusion battery life prediction method based on gaussian process regression and improvement. J Energy Storage, 2023, 59: 106469

[9] Guo L, Yu Y, Qian M Fedrul: a new federated learning method for edge-cloud collaboration based remaining useful life prediction of machines. IEEE/ASME Trans Mechatron, 2022, 28(1): 350-359

[10] He W, Williard N, Osterman M Prognostics of lithium-ion batteries based on dempster-Shafer theory and the Bayesian Monte Carlo method. J Power Sources, 2011, 196(23): 10314-10321

[11] Kamei S, Taghipour S. A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life. Reliab Eng Syst Saf, 2023, 233: 109130

[12] Kevin I, Wang K, Zhou X Federated transfer learning based cross-domain prediction for smart manufacturing. IEEE Trans Industr Inf, 2021, 18(6): 4088-4096

[13] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444

[14] Leng J, Sha W, Wang B Industry 5.0: prospect and retrospect. J Manuf Syst, 2022, 65: 279-295

[15] Li L, Fan Y, Tse M A review of applications in federated learning. Comput Ind Eng, 2020, 149: 106854

[16] Li X, Li J, Zuo L Domain adaptive remaining useful life prediction with transformer. IEEE Trans Instrum Meas, 2022, 71: 1-13

[17] Li X, Yu D, Vilsen SB et al (2023) The development of machine learning-based remaining useful life prediction for lithium-ion batteries. Journal of Energy Chemistry. https://doi.org/10.1016/j.jechem.2023.03.026

[18] Lipu MH, Ansari S, Miah MS Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects. J Energy Storage, 2022, 55: 105752

[19] Liu Y, He Y, Bian H A review of lithium-ion battery state of charge estimation based on deep learning: directions for improvement and future trends. J Energy Storage, 2022, 52: 104664

[20] McMahan B, Moore E, Ramage D, et al (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, PMLR, pp 1273–1282

[21] Nguyen A, Do T, Tran M, et al (2022) Deep federated learning for autonomous driving. In: 2022 IEEE intelligent vehicles symposium (IV), IEEE, pp 1824–1830

[22] Pokhrel SR, Choi J. Federated learning with blockchain for autonomous vehicles: Analysis and design challenges. IEEE Trans Commun, 2020, 68(8): 4734-4746

[23] Rauf H, Khalid M, Arshad N. Machine learning in state of health and remaining useful life estimation: theoretical and technological development in battery degradation modelling. Renew Sustain Energy Rev, 2022, 156: 111903

[24] Rieke N, Hancox J, Li W The future of digital health with federated learning. NPJ Digit Med, 2020, 3(1): 119

[25] Saha B, Goebel K (2007) Battery data set. http://ti.arc.nasa.gov/project/prognostic-datarepository

[26] Su X, Liu H, Tao L An end-to-end framework for remaining useful life prediction of rolling bearing based on feature pre-extraction mechanism and deep adaptive transformer model. Comput Ind Eng, 2021, 161: 107531

[27] Tan AZ, Yu H, Cui L et al (2022) Towards personalized federated learning. IEEE Trans Neural Netw Learn Syst https://doi.org/10.1109/TNNLS.2022.3160699

[28] Ungurean L, Cârstoiu G, Micea MV Battery state of health estimation: a structured review of models, methods and commercial devices. Int J Energy Res, 2017, 41(2): 151-181

[29] Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Adv Neural Inf Process Syst 30

[30] Wang J, Zhang S, Li C A data-driven method with mode decomposition mechanism for remaining useful life prediction of lithium-ion batteries. IEEE Trans Power Electron, 2022, 37(11): 13684-13695

[31] Wang S, Jin S, Bai D A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy Rep, 2021, 7: 5562-5574

[32] Wang T, Peng T, Hu B Two-stage imbalanced learning-based quality prediction method for wheel hub assembly. Adv Eng Inform, 2024, 59: 102309

[33] Wang W, Li X, Qiu X A privacy preserving framework for federated learning in smart healthcare systems. Inf Process Manage, 2023, 60(1): 103167

[34] Wang Y, Chen X, Li C Temperature prediction of lithium-ion battery based on artificial neural network model. Appl Therm Eng, 2023

[35] Whang SE, Roh Y, Song H Data collection and quality challenges in deep learning: a data-centric AI perspective. VLDB J, 2023, 32(4): 791-813

[36] Williard N, He W, Osterman M, et al. (2013) Comparative analysis of features for determining state of health in lithium-ion batteries. Int J Progn Health Manage https://doi.org/10.36001/ijphm.2013.v4i1.1437

[37] Xing Y, Ma EW, Tsui KL An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectron Reliab, 2013, 53(6): 811-820

[38] Zeng A, Chen M, Zhang L, et al. (2023) Are transformers effective for time series forecasting? In: Proceedings of the AAAI conference on artificial intelligence, pp 11121–11128, https://doi.org/10.1609/aaai.v37i9.26317

[39] Zhang C, Xie Y, Bai H A survey on federated learning. Knowl-Based Syst, 2021, 216: 106775

[40] Zhang J, Jiang Y, Li X An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty. Reliab Eng Syst Saf, 2022, 222: 108357

[41] Zhang J, Li X, Tian J An integrated multi-head dual sparse self-attention network for remaining useful life prediction. Reliab Eng Syst Saf, 2023, 233: 109096

[42] Zhang Q, Yang L, Guo W A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system. Energy, 2022, 241: 122716

[43] Zhang Z, Song W, Li Q. Dual-aspect self-attention based on transformer for remaining useful life prediction. IEEE Trans Instrum Meas, 2022, 71: 1-11

[44] Zhong R, Hu B, Feng Y Construction of human digital twin model based on multimodal data and its application in locomotion mode identification. Chin J Mech Eng, 2023, 36(1): 126

[45] Zhong R, Hu B, Hong Z et al (2024) Human-Robot hand over task intention recognition framework by fusing human digital twin and deep domain adaptation. J Eng Design 1–17. https://doi.org/10.1080/09544828.2024.2326111

Funding

National Natural Science Foundation of China(52205288)

National Key Research and Development Program of China(2022YFB3304200)