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.
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Data availability
The data that support the findings of this study are openly available in NASA and CALCE at [https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/] and [https://calce.umd.edu/data#CS2] respectively.
References
Ahmad T, Madonski R, Zhang D et al (2022) 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 160:112128. https://doi.org/10.1016/j.rser.2022.112128
Altinpulluk NB, Altinpulluk D, Ramanan P, et al. (2023) Federated battery diagnosis and prognosis. arXiv preprint arXiv:2310.09628https://doi.org/10.48550/arXiv.2310.09628
Ansari S, Ayob A, Lipu MH et al (2022) Remaining useful life prediction for lithium-ion battery storage system: a comprehensive review of methods, key factors, issues and future outlook. Energy Rep 8:12153–12185. https://doi.org/10.1016/j.egyr.2022.09.043
Arunan A, Qin Y, Li X et al (2023) A federated learning-based industrial health prognostics for heterogeneous edge devices using matched feature extraction. IEEE Trans Autom Sci Eng. https://doi.org/10.1109/TASE.2023.3274648
Bai G, Su Y, Rahman MM et al (2023) Prognostics of lithium-ion batteries using knowledge-constrained machine learning and Kalman filtering. Reliab Eng Syst Saf 231:108944. https://doi.org/10.1016/j.ress.2022.108944
Banabilah S, Aloqaily M, Alsayed E et al (2022) Federated learning review: fundamentals, enabling technologies, and future applications. Inf Process Manage 59(6):103061. https://doi.org/10.1016/j.ipm.2022.103061
Chen D, Hong W, Zhou X (2022) Transformer network for remaining useful life prediction of lithium-ion batteries. IEEE Access 10:19621–19628. https://doi.org/10.1109/ACCESS.2022.3151975
Dang W, Liao S, Yang B et al (2023) An encoder-decoder fusion battery life prediction method based on gaussian process regression and improvement. J Energy Storage 59:106469. https://doi.org/10.1016/j.est.2022.106469
Guo L, Yu Y, Qian M et al (2022) Fedrul: a new federated learning method for edge-cloud collaboration based remaining useful life prediction of machines. IEEE/ASME Trans Mechatron 28(1):350–359. https://doi.org/10.1109/TMECH.2022.3195524
He W, Williard N, Osterman M et al (2011) Prognostics of lithium-ion batteries based on dempster-Shafer theory and the Bayesian Monte Carlo method. J Power Sources 196(23):10314–10321. https://doi.org/10.1016/j.jpowsour.2011.08.040
Kamei S, Taghipour S (2023) A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life. Reliab Eng Syst Saf 233:109130. https://doi.org/10.1016/j.ress.2023.109130
Kevin I, Wang K, Zhou X et al (2021) Federated transfer learning based cross-domain prediction for smart manufacturing. IEEE Trans Industr Inf 18(6):4088–4096
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539
Leng J, Sha W, Wang B et al (2022) Industry 5.0: prospect and retrospect. J Manuf Syst 65:279–295. https://doi.org/10.1016/j.jmsy.2022.09.017
Li L, Fan Y, Tse M et al (2020) A review of applications in federated learning. Comput Ind Eng 149:106854. https://doi.org/10.1016/j.cie.2020.106854
Li X, Li J, Zuo L et al (2022) Domain adaptive remaining useful life prediction with transformer. IEEE Trans Instrum Meas 71:1–13. https://doi.org/10.1109/TIM.2022.3200667
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
Lipu MH, Ansari S, Miah MS et al (2022) 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 55:105752. https://doi.org/10.1016/j.est.2022.105752
Liu Y, He Y, Bian H et al (2022) A review of lithium-ion battery state of charge estimation based on deep learning: directions for improvement and future trends. J Energy Storage 52:104664. https://doi.org/10.1016/j.est.2022.104664
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
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
Pokhrel SR, Choi J (2020) Federated learning with blockchain for autonomous vehicles: Analysis and design challenges. IEEE Trans Commun 68(8):4734–4746
Rauf H, Khalid M, Arshad N (2022) Machine learning in state of health and remaining useful life estimation: theoretical and technological development in battery degradation modelling. Renew Sustain Energy Rev 156:111903. https://doi.org/10.1016/j.rser.2021.111903
Rieke N, Hancox J, Li W et al (2020) The future of digital health with federated learning. NPJ Digit Med 3(1):119
Saha B, Goebel K (2007) Battery data set. http://ti.arc.nasa.gov/project/prognostic-datarepository
Su X, Liu H, Tao L et al (2021) 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 161:107531. https://doi.org/10.1016/j.cie.2021.107531
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
Ungurean L, Cârstoiu G, Micea MV et al (2017) Battery state of health estimation: a structured review of models, methods and commercial devices. Int J Energy Res 41(2):151–181. https://doi.org/10.1002/er.3598
Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Adv Neural Inf Process Syst 30
Wang J, Zhang S, Li C et al (2022) A data-driven method with mode decomposition mechanism for remaining useful life prediction of lithium-ion batteries. IEEE Trans Power Electron 37(11):13684–13695. https://doi.org/10.1109/TPEL.2022.3183886
Wang S, Jin S, Bai D et al (2021) A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy Rep 7:5562–5574. https://doi.org/10.1016/j.egyr.2021.08.182
Wang T, Peng T, Hu B et al (2024) Two-stage imbalanced learning-based quality prediction method for wheel hub assembly. Adv Eng Inform 59:102309
Wang W, Li X, Qiu X et al (2023) A privacy preserving framework for federated learning in smart healthcare systems. Inf Process Manage 60(1):103167
Wang Y, Chen X, Li C et al (2023) Temperature prediction of lithium-ion battery based on artificial neural network model. Appl Therm Eng. https://doi.org/10.1016/j.applthermaleng.2023.120482
Whang SE, Roh Y, Song H et al (2023) Data collection and quality challenges in deep learning: a data-centric AI perspective. VLDB J 32(4):791–813. https://doi.org/10.1007/s00778-022-00775-9
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
Xing Y, Ma EW, Tsui KL et al (2013) An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectron Reliab 53(6):811–820. https://doi.org/10.1016/j.microrel.2012.12.003
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
Zhang C, Xie Y, Bai H et al (2021) A survey on federated learning. Knowl-Based Syst 216:106775. https://doi.org/10.1016/j.knosys.2021.106775
Zhang J, Jiang Y, Li X 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 222:108357. https://doi.org/10.1016/j.ress.2022.108357
Zhang J, Li X, Tian J et al (2023) An integrated multi-head dual sparse self-attention network for remaining useful life prediction. Reliab Eng Syst Saf 233:109096. https://doi.org/10.1016/j.ress.2023.109096
Zhang Q, Yang L, Guo W et al (2022) A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system. Energy 241:122716. https://doi.org/10.1016/j.energy.2021.122716
Zhang Z, Song W, Li Q (2022) Dual-aspect self-attention based on transformer for remaining useful life prediction. IEEE Trans Instrum Meas 71:1–11. https://doi.org/10.1109/TIM.2022.3160561
Zhong R, Hu B, Feng Y et al (2023) Construction of human digital twin model based on multimodal data and its application in locomotion mode identification. Chin J Mech Eng 36(1):126
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
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (No. 52205288).
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All authors contributed to the study conception and design. Framework construction, algorithm design, data analysis were performed by RZ, and BH. The first draft of the manuscript was written by RZ and BH, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Zhong, R., Hu, B., Feng, Y. et al. Lithium-ion battery remaining useful life prediction: a federated learning-based approach. Energ. Ecol. Environ. (2024). https://doi.org/10.1007/s40974-024-00323-x
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DOI: https://doi.org/10.1007/s40974-024-00323-x