Skip to main content
SpringerLink
Log in

State of health and remaining useful life prediction for lithium-ion batteries based on differential thermal voltammetry and a long and short memory neural network

  • Original Article
  • Published:
Rare Metals Aims and scope Submit manuscript

Abstract

As the lithium-ion battery is widely applied, the reliability of the battery has become a high-profile content in recent years. Accurate estimation and prediction of state of health (SOH) and remaining useful life (RUL) prediction are crucial for battery management systems. In this paper, the core contribution is the construction of a data-driven model with the long short-term memory (LSTM) network applicable to the time-series regression prediction problem with the integration of two methods, data-driven methods and feature signal analysis. The input features of model are extracted from differential thermal voltammetry (DTV) curves, which could characterize the battery degradation characteristics, so that the accurate prediction of battery capacity fade could be accomplished. Firstly, the DTV curve is smoothed by the Savitzky-Golay filter, and six alternate features are selected based on the connection between DTV curves and battery degradation characteristics. Then, a correlation analysis method is used to further filter the input features and three features that are highly associated with capacity fade are selected as input into the data driven model. The LSTM neural network is trained by using the root mean square propagation (RMSprop) technique and the dropout technique. Finally, the data of four batteries with different health levels are deployed for model construction, verification and comparison. The results show that the proposed method has high accuracy in SOH and RUL prediction and the capacity rebound phenomenon can be accurately estimated. This method can greatly reduce the cost and complexity, and increase the practicability, which provides the basis and guidance for battery data collection and the application of cloud technology and digital twin.

Graphical abstract

摘要

随着锂离子电池的广泛应用, 电池的可靠性成为近年来备受瞩目的内容。准确估计和预测健康状态 (SOH) 和剩余使用寿命 (RUL) 预测对于电池管理系统至关重要。在本文中, 核心贡献是构建了一个适用于时间序列回归预测问题的 LSTM 网络的数据驱动模型, 集成了数据驱动方法和特征信号分析两种方法。从DTV曲线中提取模型的输入特征, 可以表征电池的退化特性, 从而实现对电池容量衰减的准确预测。首先, 通过 SG 滤波器对 DTV 曲线进行平滑处理, 并根据 DTV 曲线与电池劣化特性之间的联系选择 6 个替代特征。然后, 使用相关分析方法对输入特征进行进一步过滤, 选择与容量衰减高度相关的三个特征作为数据驱动模型的输入。 LSTM NN 通过使用均方根传播 (RMSprop) 技术和 dropout 技术进行训练。最后, 部署四种不同健康等级电池的数据, 进行模型构建、验证和比较。结果表明, 该方法在SOH和RUL预测中具有较高的准确率, 能够准确估计容量反弹现象。这种方法可以大大降低成本和复杂性, 增加实用性, 为电池数据采集以及云技术和数字孪生的应用提供依据和指导。

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Lu Q, Jie Y, Meng X, Omar A, Mikhailova D, Cao R, Jiao S, Lu Y, Xu Y. Carbon materials for stable Li metal anodes: challenges, solutions, and outlook. Carbon Energy. 2021;3:957. https://doi.org/10.1002/cey2.147.

    Article  CAS  Google Scholar 

  2. Zhou CC, Su Z, Gao XL, Cao R, Yang SC, Liu XL. Ultra-high-energy lithium-ion batteries enabled by aligned structured thick electrode design. Rare Met. 2022;41(1):14. https://doi.org/10.1007/s12598-021-01785-2.

    Article  CAS  Google Scholar 

  3. Yan X, Lin L, Chen Q, Xie Q, Qu B, Wang L, Peng DL. Multifunctional roles of carbon-based hosts for Li-metal anodes: a review. Carbon Energy. 2021;3(2):303. https://doi.org/10.1002/cey2.95.

    Article  CAS  Google Scholar 

  4. Ruan H, Sun B, Jiang J, Zhang W, He X, Su X, Bian J, Gao W. A modified-electrochemical impedance spectroscopy-based multi-time-scale fractional-order model for lithium-ion batteries. Electrochim Acta. 2021;394: 139066. https://doi.org/10.1016/j.electacta.2021.139066.

    Article  CAS  Google Scholar 

  5. Zhu Y, Xie J, Pei A, Liu B, Wu Y, Lin D, Li J, Wang H, Chen H, Xu J, Yang A, Wu CL, Wang H, Chen W, Cui Y. Fast lithium growth and short circuit induced by localized-temperature hotspots in lithium batteries. Nat Commun. 2019;10(1):1. https://doi.org/10.1038/s41467-019-09924-1.

    Article  CAS  Google Scholar 

  6. Zhang LS, Gao XL, Liu XH, Zhang ZJ, Cao R, Cheng HC, Wang MY, Yan XY, Yang SC. CHAIN: unlocking informatics-aided design of Li metal anode from materials to applications. Rare Met. 2022;41(5):1477. https://doi.org/10.1007/s12598-021-01925-8.

    Article  CAS  Google Scholar 

  7. Li K, Shen W, Xu T, Yang L, Xu X, Yang F, Zhang L, Wang Y, Zhou Y, Zhong M, Wei D. Fibrous gel polymer electrolyte for an ultrastable and highly safe flexible lithium-ion battery in a wide temperature range. Carbon Energy. 2021;3(6):916. https://doi.org/10.1002/cey2.151.

    Article  CAS  Google Scholar 

  8. Gao XL, Liu XH, Xie WL, Zhang LS, Yang SC. Multiscale observation of Li plating for lithium-ion batteries. Rare Met. 2021;40(11):3038. https://doi.org/10.1007/s12598-021-01730-3.

    Article  CAS  Google Scholar 

  9. Chen Z, Shu X, Xiao R, Yan W, Liu Y, Shen J. Optimal charging strategy design for lithium-ion batteries considering minimization of temperature rise and energy loss. Int J Energy Res. 2019;43(9):4344. https://doi.org/10.1002/er.4560.

    Article  Google Scholar 

  10. Zhao Y, Fang LZ, Kang YQ, Wang L, Zhou YN, Liu XY, Li T, Li YX, Liang Z, Zhang ZX, Li BH. A novel three-step approach to separate cathode components for lithium-ion battery recycling. Rare Met. 2021;40(6):1431. https://doi.org/10.1007/s12598-020-01587-y.

    Article  CAS  Google Scholar 

  11. Li Y, Liu K, Foley AM, Zülke A, Berecibar M, Nanini-Maury E, Van Mierlo J, Hoster HE. Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review. Renew Sustain Energy Rev. 2019;113:109254. https://doi.org/10.1016/j.rser.2019.109254.

    Article  Google Scholar 

  12. Elattar HM, Elminir HK, Riad AM. Prognostics: a literature review. Complex Intell Syst. 2016;2(2):125. https://doi.org/10.1007/s40747-016-0019-3.

    Article  Google Scholar 

  13. Lucu M, Martinez-Laserna E, Gandiaga I, Camblong H. A critical review on self-adaptive Li-ion battery ageing models. J Power Sources. 2018;401:85. https://doi.org/10.1016/j.jpowsour.2018.08.064.

    Article  CAS  Google Scholar 

  14. Lai X, Gao W, Zheng Y, Ouyang M, Li J, Han X, Zhou L. A comparative study of global optimization methods for parameter identification of different equivalent circuit models for Li-ion batteries. Electrochim Acta. 2019;295:1057. https://doi.org/10.1016/j.electacta.2018.11.134.

    Article  CAS  Google Scholar 

  15. Schmitt J, Maheshwari A, Heck M, Lux S, Vetter M. Impedance change and capacity fade of lithium nickel manganese cobalt oxide-based batteries during calendar aging. J Power Sources. 2017;353:183. https://doi.org/10.1016/j.jpowsour.2017.03.090.

    Article  CAS  Google Scholar 

  16. Wang Y, Chen Z. A framework for state-of-charge and remaining discharge time prediction using unscented particle filter. Appl Energy. 2020;260: 114324. https://doi.org/10.1016/j.apenergy.2019.114324.

    Article  Google Scholar 

  17. Ecker M, Gerschler JB, Vogel J, Käbitz S, Hust F, Dechent P, Sauer DU. Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data. J Power Sources. 2012;215:248. https://doi.org/10.1016/j.jpowsour.2012.05.012.

    Article  CAS  Google Scholar 

  18. Park J, Appiah WA, Byun S, Jin D, Ryou MH, Lee YM. Semi-empirical long-term cycle life model coupled with an electrolyte depletion function for large-format graphite/LiFePO4 lithium-ion batteries. J Power Sources. 2017;365:257. https://doi.org/10.1016/j.jpowsour.2017.08.094.

    Article  CAS  Google Scholar 

  19. You H, Zhu J, Wang X, Jiang B, Sun H, Liu X, Wei X, Han G, Ding S, Yu H, Li W, Sauer DU, Dai H. Nonlinear health evaluation for lithium-ion battery within full-lifespan. J Energy Chem. 2022;72:333. https://doi.org/10.1016/j.jechem.2022.04.013.

    Article  CAS  Google Scholar 

  20. Feng F, Xue C, Han QL, Han F, Du J. Robust estimation for state-of-charge and state-of-health of lithium-ion batteries using integral-type terminal sliding-mode observers. IEEE Trans Ind Electron. 2019;67(5):4013. https://doi.org/10.1109/TIE.2019.2916389.

    Article  Google Scholar 

  21. Chen Z, Mi CC, Fu Y, Xu J, Gong X. Online battery state of health estimation based on genetic algorithm for electric and hybrid vehicle applications. J Power Sources. 2013;240:184. https://doi.org/10.1016/j.jpowsour.2013.03.158.

    Article  CAS  Google Scholar 

  22. Yu J. State-of-health monitoring and prediction of lithium-ion battery using probabilistic indication and state-space model. IEEE Trans Instrum Meas. 2015;64(11):2937. https://doi.org/10.1109/TIM.2015.2444237.

    Article  CAS  Google Scholar 

  23. Kemper P, Li SE, Kum D. Simplification of pseudo two dimensional battery model using dynamic profile of lithium concentration. J Power Sources. 2015;286:510. https://doi.org/10.1016/j.jpowsour.2015.03.134.

    Article  CAS  Google Scholar 

  24. Bizeray AM, Kim JH, Duncan SR, Howey DA. Identifiability and parameter estimation of the single particle lithium-ion battery model. IEEE Trans Control Syst Technol. 2018;27(5):1862. https://doi.org/10.1109/TCST.2018.2838097.

    Article  Google Scholar 

  25. Lyu C, Lai Q, Ge T, Yu H, Wang L, Ma N. A lead-acid battery’s remaining useful life prediction by using electrochemical model in the particle filtering framework. Energy. 2017;120:975. https://doi.org/10.1016/j.energy.2016.12.004.

    Article  CAS  Google Scholar 

  26. Wang M, Wang H, Yin Y, Rahardja S, Qu Z. Temperature field prediction for various porous media considering variable boundary conditions using deep learning method. Int Commun Heat Mass Transf. 2022;132: 105916. https://doi.org/10.1016/j.icheatmasstransfer.2022.105916.

    Article  Google Scholar 

  27. Patil MA, Tagade P, Hariharan KS, Kolake SM, Song T, Yeo T, Doo S. A novel multistage support vector machine based approach for Li ion battery remaining useful life estimation. Appl Energy. 2015;159:285. https://doi.org/10.1016/j.apenergy.2015.08.119.

    Article  CAS  Google Scholar 

  28. Richardson RR, Osborne MA, Howey DA. Gaussian process regression for forecasting battery state of health. J Power Sources. 2017;357:209. https://doi.org/10.1016/j.jpowsour.2017.05.004.

    Article  CAS  Google Scholar 

  29. Jiang B, Dai H, Wei X, Jiang Z. Multi-kernel relevance vector machine with parameter optimization for cycling aging prediction of lithium-ion batteries. IEEE J Emerg Sel Top Power Electron. 2021. https://doi.org/10.1109/JESTPE.2021.3133697.

    Article  Google Scholar 

  30. Eddahech A, Briat O, Bertrand N, Delétage JY, Vinassa JM. Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks. Int J Electr Power Energy Syst. 2012;42(1):487. https://doi.org/10.1016/j.ijepes.2012.04.050.

    Article  Google Scholar 

  31. Gers FA, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. Neural Comput. 2000;12(10):2451. https://doi.org/10.1049/cp:19991218.

    Article  CAS  Google Scholar 

  32. Li Y, Zou C, Berecibar M, Nanini-Maury E, Chan JCW, Van den Bossche P, Van Mierlo J, Omar N. Random forest regression for online capacity estimation of lithium-ion batteries. Appl Energy. 2018;232:197. https://doi.org/10.1016/j.apenergy.2018.09.182.

    Article  CAS  Google Scholar 

  33. Wang Z, Liu N, Guo Y. Adaptive sliding window LSTM NN based RUL prediction for lithium-ion batteries integrating LTSA feature reconstruction. Neurocomputing. 2021;466:178. https://doi.org/10.1016/j.neucom.2021.09.025.

    Article  Google Scholar 

  34. Hong S, Yue T, Liu H. Vehicle energy system active defense: a health assessment of lithium-ion batteries. Int J Intell Syst. 2020. https://doi.org/10.1002/int.22309.

    Article  Google Scholar 

  35. Hannan MA, How DNT, Hossain Lipu MS, Ker PJ, Dong ZY, Mansur M, Blaabjerg F. SOC estimation of Li-ion batteries with learning rate-optimized deep fully convolutional network. IEEE Trans Power Electron. 2021;36(7):7349. https://doi.org/10.1109/TPEL.2020.3041876.

    Article  Google Scholar 

  36. Li W, Sengupta N, Dechent P, Howey D, Annaswamy A, Sauer DU. Online capacity estimation of lithium-ion batteries with deep long short-term memory networks. J Power Sources. 2021;482:228863. https://doi.org/10.1016/j.jpowsour.2020.228863.

    Article  CAS  Google Scholar 

  37. Yang S, He R, Zhang Z, Cao Y, Gao X, Liu X. CHAIN: cyber hierarchy and interactional network enabling digital solution for battery full-lifespan management. Matter. 2020;3(1):27. https://doi.org/10.1016/j.matt.2020.04.015.

    Article  Google Scholar 

  38. Zhu J, Wang Y, Huang Y, Gopaluni RB, Cao Y, Heere M, Mühlbauer MJ, Mereacre L, Dai H, Liu X, Senyshyn A, Wei X, Knapp M, Ehrenberg H. Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation. Nat Commun. 2022;13(1):1. https://doi.org/10.1038/s41467-022-29837-w.

    Article  CAS  Google Scholar 

  39. Tang X, Zou C, Yao K, Chen G, Liu B, He Z, Gao F. A fast estimation algorithm for lithium-ion battery state of health. J Power Sources. 2018;396:453. https://doi.org/10.1016/j.jpowsour.2018.06.036.

    Article  CAS  Google Scholar 

  40. Weng C, Cui Y, Sun J, Peng H. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression. J Power Sources. 2013;235:36. https://doi.org/10.1016/j.jpowsour.2013.02.012.

    Article  CAS  Google Scholar 

  41. Wang L, Pan C, Liu L, Cheng Y, Zhao X. On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis. Appl Energy. 2016;168:465. https://doi.org/10.1016/j.apenergy.2016.01.125.

    Article  CAS  Google Scholar 

  42. Zheng L, Zhu J, Wang G, Lu DDC, He T. Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter. Energy. 2018;158:1028. https://doi.org/10.1016/j.energy.2018.06.113.

    Article  Google Scholar 

  43. Wu B, Yufit V, Merla Y, Martinez-Botas RF, Brandon NP, Offer GJ. Differential thermal voltammetry for tracking of degradation in lithium-ion batteries. J Power Sources. 2015;273:495. https://doi.org/10.1016/j.jpowsour.2014.09.127.

    Article  CAS  Google Scholar 

  44. Merla Y, Wu B, Yufit V, Brandon NP, Martinez-Botas RF, Offer GJ. Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries. J Power Sources. 2016;307:308. https://doi.org/10.1016/j.jpowsour.2015.12.122.

    Article  CAS  Google Scholar 

  45. Merla Y, Wu B, Yufit V, Brandon NP, Martinez-Botas RF, Offer GJ. Extending battery life: a low-cost practical diagnostic technique for lithium-ion batteries. J Power Sources. 2016;331:224. https://doi.org/10.1016/j.jpowsour.2016.09.008.

    Article  CAS  Google Scholar 

  46. Goebel K, Saha B, Saxena A, Celaya JR, Christophersen JP. Prognostics in battery health management. IEEE Instrum Meas Mag. 2008;11(4):33. https://doi.org/10.1177/0972063413486066.

    Article  Google Scholar 

  47. Krishnan SR, Seelamantula CS. On the selection of optimum Savitzky-Golay filters. IEEE Trans Signal Process. 2012;61(2):380. https://doi.org/10.1109/TSP.2012.2225055.

    Article  Google Scholar 

  48. Chemali E, Kollmeyer PJ, Preindl M, Ahmed R, Emadi A. Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries. IEEE Trans Ind Electron. 2018;65(8):6730. https://doi.org/10.1109/TIE.2017.2787586.

    Article  Google Scholar 

  49. Tong Z, Miao J, Tong S, Lu Y. Early prediction of remaining useful life for lithium-ion batteries based on a hybrid machine learning method. J Clean Prod. 2021;317: 128265. https://doi.org/10.1016/j.jclepro.2021.128265.

    Article  CAS  Google Scholar 

  50. Zhang Y, Xiong R, He H, Pecht MG. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Trans Veh Technol. 2018;67(7):5695. https://doi.org/10.1109/TVT.2018.2805189.

    Article  Google Scholar 

  51. Ma G, Zhang Y, Cheng C, Zhou B, Hu P, Yuan Y. Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network. Appl Energy. 2019;253:113626. https://doi.org/10.1016/j.apenergy.2019.113626.

    Article  Google Scholar 

  52. Liu J, Chen Z. Remaining useful life prediction of lithium-ion batteries based on health indicator and Gaussian process regression model. IEEE Access. 2019;7:39474. https://doi.org/10.1109/ACCESS.2019.2905740.

    Article  Google Scholar 

  53. Zhang L, Mu Z, Sun C. Remaining useful life prediction for lithium-ion batteries based on exponential model and particle filter. IEEE Access. 2018;6:17729. https://doi.org/10.1109/ACCESS.2018.2816684.

    Article  Google Scholar 

  54. Zhang H, Miao Q, Zhang X, Liu Z. An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction. Microelectron Reliab. 2018;81:288. https://doi.org/10.1016/j.microrel.2017.12.036.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (No. 52102470) and the Science and Technology Development Project of Jilin province (No. 20200501012GX).

Author information

Authors and Affiliations

  1. College of Automotive Engineering, Jilin University, Changchun, 130022, China

    Bin Ma, Xian-Bin Yang, Hai-Cheng Xie & Si-Yan Chen

  2. School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China

    Bin Ma, Han-Qing Yu, Wen-Tao Wang, Li-Sheng Zhang & Xin-Hua Liu

  3. School of Automotive Engineering, Harbin Institute of Technology, Weihai, 264209, China

    Han-Qing Yu

  4. Institute for Clean Growth & Future Mobility, Coventry University, Coventry, CV1 5FB, UK

    Cheng Zhang

Authors
  1. Bin Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Han-Qing Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-Tao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xian-Bin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li-Sheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hai-Cheng Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Cheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Si-Yan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xin-Hua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Si-Yan Chen.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, B., Yu, HQ., Wang, WT. et al. State of health and remaining useful life prediction for lithium-ion batteries based on differential thermal voltammetry and a long and short memory neural network. Rare Met. 42, 885–901 (2023). https://doi.org/10.1007/s12598-022-02156-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12598-022-02156-1

Keywords

Search

Navigation