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A rest-time-based prognostic model for remaining useful life prediction of lithium-ion battery

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Abstract

This paper proposes a novel empirical model for the remaining useful life prediction of lithium-ion battery. The proposed model is capable of modeling both the global degradation and local degradation of lithium-ion battery aging process. The global degradation process is regarded as the ideal aging profile without any interference by regeneration phenomenon. However, the regeneration phenomenon inevitably occurs in practical usage of lithium-ion battery and affects the local degradation significantly. Therefore, we separate the local degradation process from the global degradation process to represent the local battery aging process affected by regeneration phenomenon. We unify the modeling method for the global and local degradation process by exponential functions with cleverly designing of the corresponding cycles. The particle filter framework is employed to estimate the model parameters with measurement data. The future capacity is predicted after the identification, and the remaining useful life is extracted by calculating the difference between the predicted capacity and failure threshold. Model comparisons on benchmark battery datasets have been conducted. The results demonstrate that our proposed method is capable of capturing the degradation and regeneration phenomena, and the remaining useful life prediction performance of our proposed model is better than state-of-the-art modeling methods.

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References

  1. Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans Signal Process 50(2):174–188

    Article  Google Scholar 

  2. Birkl CR, Roberts MR, McTurk E, Bruce PG, Howey DA (2017) Degradation diagnostics for lithium ion cells. J Power Sour 341:373–386

    Article  Google Scholar 

  3. Choi SS, Lim HS (2002) Factors that affect cycle-life and possible degradation mechanisms of a li-ion cell based on licoo2. J Power Sour 111(1):130–136

    Article  Google Scholar 

  4. Daigle M, Kulkarni CS (2016) End-of-discharge and end-of-life prediction in lithium-ion batteries with electrochemistry-based aging models. In: AIAA Infotech@ aerospace, p 2132

  5. Deng LM, Hsu YC, Li HX (2017) An improved model for remaining useful life prediction on capacity degradation and regeneration of lithium-ion battery. In: Annual conference of the prognostics and health management society 2017 (PHM 2017)

  6. Dubarry M, Liaw BY, Chen MS, Chyan SS, Han KC, Sie WT, Wu SH (2011) Identifying battery aging mechanisms in large format li ion cells. J Power Sour 196(7):3420–3425

    Article  Google Scholar 

  7. Eddahech A, Briat O, Bertrand N, Delétage JY, Vinassa JM (2012) Behavior and state-of-health monitoring of li-ion batteries using impedance spectroscopy and recurrent neural networks. Int J Electr Power Energy Syst 42(1):487–494

    Article  Google Scholar 

  8. Eddahech A, Briat O, Vinassa JM (2013) Lithium-ion battery performance improvement based on capacity recovery exploitation. Electrochim Acta 114:750–757

    Article  Google Scholar 

  9. Fernández I, Calvillo C, Sánchez-Miralles A, Boal J (2013) Capacity fade and aging models for electric batteries and optimal charging strategy for electric vehicles. Energy 60:35–43

    Article  Google Scholar 

  10. Gordon NJ, Salmond DJ, Smith AF (1993) Novel approach to nonlinear/non-gaussian bayesian state estimation. In: IEE proceedings F (radar and signal processing), IET, vol 140, pp 107–113

  11. He W, Williard N, Osterman M, Pecht M (2011) Prognostics of lithium-ion batteries based on dempster-shafer theory and the bayesian monte carlo method. J Power Sour 196(23):10314–10321

    Article  Google Scholar 

  12. Huang C, Wang Z, Zhao Z, Wang L, Lai CS, Wang D (2018) Robustness evaluation of extended and unscented kalman filter for battery state of charge estimation. IEEE Access

  13. Huggins R (2008) Advanced batteries: materials science aspects. Springer, Berlin

    Google Scholar 

  14. Jin G, Matthews DE, Zhou Z (2013) A bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft. Reliab Eng Syst Saf 113:7–20

    Article  Google Scholar 

  15. Klass V, Behm M, Lindbergh G (2014) A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation. J Power Sour 270:262–272

    Article  Google Scholar 

  16. Liu D, Pang J, Zhou J, Peng Y, Pecht M (2013) Prognostics for state of health estimation of lithium-ion batteries based on combination gaussian process functional regression. Microelectron Reliab 53(6):832–839

    Article  Google Scholar 

  17. Liu K, Hu X, Wei Z, Li Y, Jiang Y (2020) Modified gaussian process regression models for cyclic capacity prediction of lithium-ion batteries. IEEE Trans Transp Electr 5(4):1225–1236

    Article  Google Scholar 

  18. Liu K, Li Y, Hu X, Lucu M, Widanage WD (2020) Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries. IEEE Trans Ind Inform 16(6):3767–3777

    Article  Google Scholar 

  19. Liu K, Shang Y, Ouyang Q, Widanage WD (2020) A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery. IEEE Trans Ind Electron, pp 1–1

  20. Long B, Xian W, Jiang L, Liu Z (2013) An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries. Microelectron Reliab 53(6):821–831

    Article  Google Scholar 

  21. Lu L, Han X, Li J, Hua J, Ouyang M (2013) A review on the key issues for lithium-ion battery management in electric vehicles. J Power Sour 226:272–288

    Article  Google Scholar 

  22. Miao Q, Xie L, Cui H, Liang W, Pecht M (2013) Remaining useful life prediction of lithium-ion battery with unscented particle filter technique. Microelectron Reliab 53(6):805–810

    Article  Google Scholar 

  23. Micea MV, Ungurean L, Carstoiu GN, Groza V (2011) Online state-of-health assessment for battery management systems. IEEE Trans Instrum Meas 60(6):1997–2006

    Article  Google Scholar 

  24. Olivares BE, Munoz MAC, Orchard ME, Silva JF (2013) Particle-filtering-based prognosis framework for energy storage devices with a statistical characterization of state-of-health regeneration phenomena. IEEE Trans Instrum Meas 62(2):364–376

    Article  Google Scholar 

  25. Omar N, Monem MA, Firouz Y, Salminen J, Smekens J, Hegazy O, Gaulous H, Mulder G, Van den Bossche P, Coosemans T et al (2014) Lithium iron phosphate based battery-assessment of the aging parameters and development of cycle life model. Appl Energy 113:1575–1585

    Article  Google Scholar 

  26. Orchard M, Tang L, Saha B, Goebel K, Vachtsevanos G (2010) Risk-sensitive particle-filtering-based prognosis framework for estimation of remaining useful life in energy storage devices. Stud Inform Control 19(3):209–218

    Article  Google Scholar 

  27. Orchard ME, Lacalle MS, Olivares BE, Silva JF, Palma-Behnke R, Estévez PA, Severino B, Calderon-Muñoz W, Cortés-Carmona M (2015) Information-theoretic measures and sequential monte carlo methods for detection of regeneration phenomena in the degradation of lithium-ion battery cells. IEEE Trans Reliab 64(2):701–709

    Article  Google Scholar 

  28. Orchard ME, Vachtsevanos GJ (2009) A particle-filtering approach for on-line fault diagnosis and failure prognosis. Trans Inst Meas Control 31(3–4):221–246

    Article  Google Scholar 

  29. Petzl M, Kasper M, Danzer MA (2015) Lithium plating in a commercial lithium-ion battery-a low-temperature aging study. J Power Sour 275:799–807

    Article  Google Scholar 

  30. Qin T, Zeng S, Guo J, Skaf Z (2016) A rest time-based prognostic framework for state of health estimation of lithium-ion batteries with regeneration phenomena. Energies 9(11):896

    Article  Google Scholar 

  31. Safari M, Morcrette M, Teyssot A, Delacourt C (2009) Multimodal physics-based aging model for life prediction of li-ion batteries. J Electrochem Soc 156(3):A145–A153

    Article  Google Scholar 

  32. Saha B, Goebel K (2007) Battery data set. https://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/battery. Accessed 03 May 2016

  33. Saha B, Goebel K (2009) Modeling li-ion battery capacity depletion in a particle filtering framework. In: Proceedings of the annual conference of the prognostics and health management society, San Diego, pp 2909–2924

  34. Saha B, Goebel K, Christophersen J (2009) Comparison of prognostic algorithms for estimating remaining useful life of batteries. Trans Inst Meas Control 31(3–4):293–308

    Article  Google Scholar 

  35. Saha B, Goebel K, Poll S, Christophersen J (2009) Prognostics methods for battery health monitoring using a bayesian framework. IEEE Trans Instrum Meas 58(2):291–296

    Article  Google Scholar 

  36. Tang S, Yu C, Wang X, Guo X, Si X (2014) Remaining useful life prediction of lithium-ion batteries based on the wiener process with measurement error. Energies 7(2):520–547

    Article  Google Scholar 

  37. Van Der Merwe R, Doucet A, De Freitas N, Wan EA (2001) The unscented particle filter. In: Advances in neural information processing systems, pp 584–590

  38. Wang D, Miao Q, Pecht M (2013) Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model. J Power Sour 239:253–264

    Article  Google Scholar 

  39. Wang D, Yang F, Tsui KL, Zhou Q, Bae SJ (2016) Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter. IEEE Trans Instrum Meas 65(6):1282–1291

    Article  Google Scholar 

  40. Wu J, Zhang C, Chen Z (2016) An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Appl Energy 173:134–140

    Article  Google Scholar 

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

    Article  Google Scholar 

  42. Yang B, Liu R, Zio E (2019) Remaining useful life prediction based on a double-convolutional neural network architecture. IEEE Trans Ind Electron 66(12):9521–9530

    Article  Google Scholar 

  43. Yang F, Wang D, Xing Y, Tsui KL (2017) Prognostics of li (nimnco) o2-based lithium-ion batteries using a novel battery degradation model. Microelectron Reliab 70:70–78

    Article  Google Scholar 

  44. Yang N, Zhang X, Shang B, Li G (2016) Unbalanced discharging and aging due to temperature differences among the cells in a lithium-ion battery pack with parallel combination. J Power Sour 306:733–741

    Article  Google Scholar 

Download references

Acknowledgements

This work was in part supported by National Natural Science Foundations of China under Grant Nos. 61872351, 51906160, International Science and Technology Cooperation Projects of Guangdong under Grant No. 2019A050510030, the Natural Science Foundation of Guangdong Province under Grant 2018A030313747, the Strategic Priority CAS Project under Grant XDB38000000, the Major Projects from General Logistics Department of People’s Liberation Army under Grant AWS13C008 and Natural Science Foundation of Top Talent of SZTU (Grant No. 1814309011180003).

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Authors and Affiliations

  1. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China

    Liming Deng

  2. Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China

    Wenjing Shen

  3. Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China

    Hongfei Wang

  4. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Shuqiang Wang

  5. Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen, 518000, China

    Shuqiang Wang

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  1. Liming Deng
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  2. Wenjing Shen
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Correspondence to Shuqiang Wang.

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Deng, L., Shen, W., Wang, H. et al. A rest-time-based prognostic model for remaining useful life prediction of lithium-ion battery. Neural Comput & Applic 33, 2035–2046 (2021). https://doi.org/10.1007/s00521-020-05105-0

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