Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural network and Gaussian process regression

Abstract

Lithium-ion batteries (LIBs) are widely used in transportation, energy storage, and other fields. The prediction of the remaining useful life (RUL) of lithium batteries not only provides a reference for health management but also serves as a basis for assessing the residual value of the battery. In order to improve the prediction accuracy of the RUL of LIBs, a two-phase RUL early prediction method combining neural network and Gaussian process regression (GPR) is proposed. In the initial phase, the features related to the capacity degradation of LIBs are utilized to train the neural network model, which is used to predict the initial cycle lifetime of 124 LIBs. The Pearson coefficient’s two most significant characteristic factors and the predicted normalized lifetime form a 3D space. The Euclidean distance between the test dataset and each cell in the training dataset and validation dataset is calculated, and the shortest distance is considered to have a similar degradation pattern, which is used to determine the initial Dual Exponential Model (DEM). In the second phase, GPR uses the DEM as the initial parameter to predict each test set’s early RUL (ERUL). By testing four batteries under different working conditions, the RMSE of all capacity estimation is less than 1.2%, and the accuracy percentage (AP) of remaining life prediction is more than 98%. Experiments show that the method does not need human intervention and has high prediction accuracy.

Graphical abstract

Keywords

lithium-ion batteries / RUL prediction / double exponential model / neural network / Gaussian process regression (GPR)

References

[1] Sun F. Green energy and intelligent transportation—Promoting green and intelligent mobility. Green Energy and Intelligent Transportation, 2022, 1(1): 100017

[2] XiongRKim JShenW, . Key technologies for electric vehicles. Green Energy and Intelligent Transportation, 2022, 1(2): 100041

[3] He H, Sun F, Wang Z. . China’s battery electric vehicles lead the world: Achievements in technology system architecture and technological breakthroughs. Green Energy and Intelligent Transportation, 2022, 1(1): 100020

[4] Lipu M S H, Hannan M A, Hussain A. . A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations. Journal of Cleaner Production, 2018, 205: 115–133

[5] Meng H, Li Y F. A review on prognostics and health management (PHM) methods of lithium-ion batteries. Renewable & Sustainable Energy Reviews, 2019, 116: 109405

[6] Ge M F, Liu Y B, Jiang X X. . A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries. Measurement, 2021, 174: 109057

[7] Rezvanizaniani S M, Liu Z, Chen Y. . Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility. Journal of Power Sources, 2014, 256: 110–124

[8] Xiong X S, Sun R, Yan W Q. . A lithiophilic AlN-modified copper layer for high-performance lithium metal anodes. Journal of Materials Chemistry. A, Materials for Energy and Sustainability, 2022, 10(26): 13814–13820

[9] Wang T, He J R, Cheng X B. . Strategies toward high-loading lithium-sulfur batteries. ACS Energy Letters, 2022, 8(1): 116–150

[10] Ates M, Chebil A. Supercapacitor and battery performances of multi-component nanocomposites: Real circuit and equivalent circuit model analysis. Journal of Energy Storage, 2022, 53: 105093

[11] Li D Z, Yang D F, Li L W. . Electrochemical impedance spectroscopy based on the state of health estimation for lithium-ion batteries. Energies, 2022, 15(18): 6665

[12] Son J B, Zhou S Y, Sankavaram C. . Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter. Reliability Engineering & System Safety, 2016, 152: 38–50

[13] Xia Q, Wang Z L, Ren Y. . A modified reliability model for lithium-ion battery packs based on the stochastic capacity degradation and dynamic response impedance. Journal of Power Sources, 2019, 423: 40–51

[14] Mo B H, Yu J S, Tang D Y, et al. A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter. In: 2016 IEEE International Conference on Prognostics and Health Management, Ottawa, Canada, 2016

[15] Wang D, Yang F F, Tsui K L. . Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter. IEEE Transactions on Instrumentation and Measurement, 2016, 65(6): 1282–1291

[16] Xie G, Peng X, Li X. . Remaining useful life prediction of lithium-ion battery based on an improved particle filter algorithm. Canadian Journal of Chemical Engineering, 2020, 98(6): 1365–1376

[17] Ren L, Zhao L, Hong S. . Remaining useful life prediction for lithium-ion battery: A deep learning approach. IEEE Access: Practical Innovations, Open Solutions, 2018, 6: 50587–50598

[18] Dong G Z, Yang F F, Wei Z B. . Data-driven battery health prognosis using adaptive Brownian motion model. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4736–4746

[19] Li Y, Liu K L, Foley A M. . Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable & Sustainable Energy Reviews, 2019, 113: 109254

[20] Severson K A, Attia P M, Jin N. . Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 2019, 4(5): 383–391

[21] He J T, Wei Z B, Bian X L. . State-of-health estimation of lithium-ion batteries using incremental capacity analysis based on voltage-capacity model. IEEE Transactions on Transportation Electrification, 2020, 6(2): 417–426

[22] Yang Y X. A machine-learning prediction method of lithium-ion battery life based on charge process for different applications. Applied Energy, 2021, 292: 116897

[23] Wang J G, Zhang S D, Li C Y. . A data-driven method with mode decomposition mechanism for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Power Electronics, 2022, 37(11): 13684–13695

[24] Zhang W, Li X, Li X. Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and online validation. Measurement, 2020, 164: 108052

[25] Weng C H, Sun J, Peng H. Model parametrization and adaptation based on the invariance of support vectors with applications to battery state-of-health monitoring. IEEE Transactions on Vehicular Technology, 2015, 64(9): 3908–3917

[26] ShiY HYang Y RWenJ, . Remaining useful life Prediction for lithium-ion battery based on CEEMDAN and SVR. In: 18th IEEE International Conference on Industrial Informatics, Warwick, UK, 2020, 888–893

[27] Dong H C, Jin X N, Lou Y B. . Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter. Journal of Power Sources, 2014, 271: 114–123

[28] ZhaoG QZhang G HLiuY F, . Lithium-ion battery remaining useful life prediction with deep belief network and relevance vector machine. In: IEEE International Conference on Prognostics and Health Management, Dallas, TX, USA, 2017, 7–13

[29] Li X Y, Wang Z P, Yan J Y. Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression. Journal of Power Sources, 2019, 421: 56–67

[30] Pang X Q, Huang R, Wen J. . A lithium-ion battery RUL prediction method considering the capacity regeneration phenomenon. Energies, 2019, 12(12): 2247

[31] Niri M F, Bui T M N, Dinh T Q. . Remaining energy estimation for lithium-ion batteries via Gaussian mixture and Markov models for future load prediction. Journal of Energy Storage, 2020, 28: 101271

[32] Li X Y, Zhang L, Wang Z P. . Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks. Journal of Energy Storage, 2019, 21: 510–518

[33] Karami H, Mousavi M F, Shamsipur M. . New dry and wet Zn-polyaniline bipolar batteries and prediction of voltage and capacity by ANN. Journal of Power Sources, 2006, 154(1): 298–307

[34] Chan C C, Lo E W C, Shen W X. The available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles. Journal of Power Sources, 2000, 87(1–2): 201–204

[35] Mazzeo D, Herdem M S, Matera N. . Artificial intelligence application for the performance prediction of a clean energy community. Energy, 2021, 232: 120999

[36] Wang M Y, Hu W F, Jiang Y F. . Internal temperature prediction of ternary polymer lithium-ion battery pack based on CNN and virtual thermal sensor technology. International Journal of Energy Research, 2021, 45(9): 13681–13691

[37] Qu J T, Liu F, Ma Y X. . A neural-network-based method for RUL prediction and SOH monitoring of lithium-ion battery. IEEE Access: Practical Innovations, Open Solutions, 2019, 7: 87178–87191

[38] Catelani M, Ciani L, Fantacci R. . Remaining useful life estimation for prognostics of lithium-ion batteries based on recurrent neural network. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1

[39] Feng X, Chen J X, Zhang Z W. . State-of-charge estimation of lithium-ion battery based on clockwork recurrent neural network. Energy, 2021, 236: 121360

[40] Zheng X J, Fang H J. An integrated unscented Kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction. Reliability Engineering & System Safety, 2015, 144: 74–82

[41] Xue Z W, Zhang Y, Cheng C. . Remaining useful life prediction of lithium-ion batteries with adaptive unscented Kalman filter and optimized support vector regression. Neurocomputing, 2020, 376: 95–102

[42] Deng Z W, Xu L, Liu H A. . Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles. Applied Energy, 2023, 339: 120954

[43] Deng Z W, Lin X K, Cai J W. . Battery health estimation with degradation pattern recognition and transfer learning. Journal of Power Sources, 2022, 525: 231027

[44] Zhao S S, Zhang C L, Wang Y Z. Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network. Journal of Energy Storage, 2022, 52: 104901

[45] Zhang C L, Zhao S S, He Y G. An integrated method of the future capacity and RUL prediction for lithium-ion battery pack. IEEE Transactions on Vehicular Technology, 2022, 71(3): 2601–2613

[46] Zhang C L, Zhao S S, Yang Z. . A reliable data-driven state-of-health estimation model for lithium-ion batteries in electric vehicles. Frontiers in Energy Research, 2022, 10: 1013800

[47] Zhou Z, Liu Y, You M. . Two-stage aging trajectory prediction of LFP lithium-ion battery based on transfer learning with the cycle life prediction. Green Energy and Intelligent Transportation, 2022, 1(1): 100008

[48] Ma G J, Wang Z D, Liu W B. . A two-stage integrated method for early prediction of remaining useful life of lithium-ion batteries?. Knowledge-based Systems, 2023, 259: 110012

[49] Zhang L J, Mu Z Q, Sun C Y. Remaining useful life prediction for lithium-ion batteries based on exponential model and particle filter. IEEE Access: Practical Innovations, Open Solutions, 2018, 6: 17729–17740

[50] Huang Z L, Xu F, Yang F F. State of health prediction of lithium-ion batteries based on autoregression with exogenous variables model. Energy, 2023, 262: 125497

[51] Zhang Q S, Yang L, Guo W C. . 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

Acknowledgements

This work was supported by the Major Science and Technology Projects for Independent Innovation of China FAW Group Co., Ltd. (Grant Nos. 20220301018GX and 20220301019GX).

Competing interests

The authors declare that they have no competing interests.

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