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Accurate remaining useful life estimation of lithium-ion batteries in electric vehicles based on a measurable feature-based approach with explainable AI

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Abstract

As Electric Vehicles (EVs) become increasingly prevalent, accurately estimating Lithium-ion Batteries (LIBs) Remaining Useful Life (RUL) is crucial for ensuring safety and avoiding operational risks beyond their service life threshold. However, directly measuring battery capacity during EV operation is challenging. In this paper, we propose a novel approach that leverages measurable features based on the discharge time and battery temperature to estimate RUL. Our framework relies on a novel feature extraction strategy that accurately characterizes the battery, leading to improved RUL predictions. Multiple machine learning algorithms are employed and evaluated. Our experimental results demonstrate that the proposed method accurately estimates capacity with minimal hyperparameter tuning. The \(R^2\) scores across various battery numbers indicate strong predictive performance for models like XGBoost, RF, AdaBoost, and others, with improvement percentages ranging from 85% to 99%, which the model’s generalizability verifies across other batteries. The results show the effectiveness of our proposed method in accurately estimating the RUL of LIBs in EVs.

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References

  1. **ong J, Xu D (2021) Relationship between energy consumption, economic growth and environmental pollution in China. Environ Res 194:110718

    Article  CAS  PubMed  Google Scholar 

  2. Chen L, Zhang Y, Zheng Y, Li X, Zheng X (2020) Remaining useful life prediction of lithium-ion battery with optimal input sequence selection and error compensation. Neurocomputing 414:245–254

    Article  Google Scholar 

  3. Zhang Y, Wang Z, Alsaadi FE (2020) Detection of intermittent faults for nonuniformly sampled multi-rate systems with dynamic quantisation and missing measurements. Int J Control 93(4):898–909

    Article  MathSciNet  Google Scholar 

  4. Šeruga D, Gosar A, Sweeney CA, Jaguemont J, Van Mierlo J, Nagode M (2021) Continuous modelling of cyclic ageing for lithium-ion batteries. Energy 215:119079

    Article  Google Scholar 

  5. Cheng M, Sun H, Wei G, Zhou G, Zhang X (2022) A sustainable framework for the second-life battery ecosystem based on blockchain. Elsevier, Amsterdam

    Book  Google Scholar 

  6. Tang T, Yuan H (2022) A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery. Reliab Eng Syst Saf 217:108082

    Article  Google Scholar 

  7. Sierra G, Orchard M, Goebel K, Kulkarni C (2019) Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms. Reliab Eng Syst Saf 182:166–178

    Article  Google Scholar 

  8. Liu Z, He B, Zhang Z, Deng W, Dong D, **a S, Zhou X, Liu Z (2022) Lithium/graphene composite anode with 3D structural LiF protection layer for high-performance lithium metal batteries. ACS Appl Mater Interfaces 14(2):2871–2880

    Article  CAS  PubMed  Google Scholar 

  9. Tang X, Zou C, Yao K, Chen G, Liu B, He Z, Gao F (2018) A fast estimation algorithm for lithium-ion battery state of health. J Power Sour 396:453–458

    Article  CAS  Google Scholar 

  10. Ng SSY, **ng Y, Tsui KL (2014) A naive Bayes model for robust remaining useful life prediction of lithium-ion battery. Appl Energy 118:114–123

    Article  ADS  Google Scholar 

  11. Wang S, ** S, Bai D, Fan Y, Shi H, Fernandez C (2021) A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy Rep 7:5562–5574

    Article  Google Scholar 

  12. Chen L, Ding Y, Liu B, Wu S, Wang Y, Pan H (2022) Remaining useful life prediction of lithium-ion battery using a novel particle filter framework with grey neural network. Energy 244:122581

    Article  Google Scholar 

  13. Tian H, Qin P, Li K, Zhao Z (2020) A review of the state of health for lithium-ion batteries: research status and suggestions. J Clean Prod 261:120813

    Article  CAS  Google Scholar 

  14. Lashgari F, Petkovski E, Cristaldi L (2022) State of health analysis for lithium-ion batteries considering temperature effect. In: 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE, pp 40–45

  15. Barcellona S, Cristaldi L, Faifer M, Petkovski E, Piegari L, Toscani S (2021) State of health prediction of lithium-ion batteries. In: 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0 &IoT), IEEE, pp 12–17

  16. Zhang S, Zhai B, Guo X, Wang K, Peng N, Zhang X (2019) Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks. J Energy Storage 26:100951

    Article  Google Scholar 

  17. Hu X, Xu L, Lin X, Pecht M (2020) Battery lifetime prognostics. Joule 4:310–346

    Article  CAS  Google Scholar 

  18. Sulzer V, Mohtat P, Aitio A, Lee S, Yeh YT, Steinbacher F, Khan MU, Lee JW, Siegel JB, Stefanopoulou AG (2021) The challenge and opportunity of battery lifetime prediction from field data. Joule 5:1934–1955

    Article  Google Scholar 

  19. Tian Y, Lin C, Li H, Du J, **ong R (2021) Detecting undesired lithium plating on anodes for lithium-ion batteries–a review on the in-situ methods. Appl Energy 300:117386

    Article  CAS  Google Scholar 

  20. Yu B, Qiu H, Weng L, Huo K, Liu S, Liu H (2020) A health indicator for the online lifetime estimation of an electric vehicle power Li-ion battery. World Electr Veh J 11(3):59

    Article  Google Scholar 

  21. Zhongwei D, Xu L, Liu H, Hu X, Duan Z, Xu Y (2023) Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles. Appl Energy 339:120954

    Article  Google Scholar 

  22. Li J, Deng Z, Liu H, **e Y, Liu C, Chen L (2022) Battery capacity trajectory prediction by capturing the correlation between different vehicles. Energy 260:125123

    Article  Google Scholar 

  23. Niri MF et al (2020) Remaining energy estimation for lithium-ion batteries via Gaussian mixture and Markov models for future load prediction. J Energy Storage 28:101271

    Article  Google Scholar 

  24. Bui TMN et al (2021) A study of reduced battery degradation through state-of-charge pre-conditioning for vehicle-to-grid operations. IEEE Access 9:155871–155896

    Article  Google Scholar 

  25. Niri MF et al (2020) State of power prediction for lithium-ion batteries in electric vehicles via Wavelet–Markov load analysis. IEEE Trans Intell Transp Syst 22(9):5833–5848

    Article  Google Scholar 

  26. Song W, Wu D, Shen W, Boulet B (2023) A remaining useful life prediction method for lithium-ion battery based on temporal transformer network. Procedia Comput Sci 217:1830–1838

    Article  Google Scholar 

  27. Sadabadi KK, ** X, Rizzoni G (2021) Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health. J Power Sour 481:228861

    Article  Google Scholar 

  28. Ren L, Zhao L, Hong S, Zhao S, Wang H, Zhang L (2018) Remaining useful life prediction for lithium-ion battery: a deep learning approach. IEEE Access 6:50587–50598

    Article  Google Scholar 

  29. Zraibi B, Okar C, Chaoui H, Mansouri M (2021) Remaining useful life assessment for lithium-ion batteries using CNN-LSTM-DNN hybrid method. IEEE Trans Veh Technol 70(5):4252–4261

    Article  Google Scholar 

  30. Kara A (2021) A data-driven approach based on deep neural networks for lithium-ion battery prognostics. Neural Comput Appl 33(20):13525–13538

    Article  Google Scholar 

  31. Toughzaoui Y, Toosi SB, Chaoui H, Louahlia H, Petrone R, Le Masson S, Gualous H (2022) State of health estimation and remaining useful life assessment of lithium-ion batteries: a comparative study. J Energy Storage 51:104520

    Article  Google Scholar 

  32. Ardeshiri RR, Liu M, Ma C (2022) Multivariate stacked bidirectional long short term memory for lithium-ion battery health management. Reliab Eng Syst Saf 224:108481

    Article  Google Scholar 

  33. Yao F, He W, Wu Y, Ding F, Meng D (2022) Remaining useful life prediction of lithium-ion batteries using a hybrid model. Energy 248:123622

    Article  Google Scholar 

  34. 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 68(4):3170–3180

    Article  Google Scholar 

  35. Fan J, Fan J, Liu F, Qu J, Li R (2019) A novel machine learning method based approach for Li-ion battery prognostic and health management. IEEE Access 7:160043–160061

    Article  Google Scholar 

  36. Saha B, Goebel K (2007) NASA Ames prognostics data repository. NASA Ames: moffett field, CA, USA, 2007. Available at: http://ti.arc.nasa.gov/project/prognostic-data-repository

  37. Jafari S et al (2022) Lithium-ion battery health prediction on hybrid vehicles using machine learning approach. Energies 15(13):4753

    Article  CAS  Google Scholar 

  38. Jafari S, Byun Y-C (2022) Prediction of the battery state using the digital twin framework based on the battery management system. IEEE Access 10:124685–124696

    Article  Google Scholar 

  39. Shahbazi Z, Byun Y-C (2022) Blockchain and machine learning for intelligent multiple factor-based ride-hailing services. Comput Mater Contin 70(3):1–18

    Google Scholar 

  40. Qayyum F, Afzal MT (2019) Identification of important citations by exploiting research articles’ metadata and cue-terms from content. Scientometrics 118:21–43

    Article  Google Scholar 

  41. Qayyum F et al (2022) Toward potential hybrid features evaluation using MLP-ANN binary classification model to tackle meaningful citations. Scientometrics 127(11):6471–6499

    Article  CAS  Google Scholar 

  42. Qayyum F et al (2021) Towards potential content-based features evaluation to tackle meaningful citations. Symmetry 13(10):1973

    Article  ADS  Google Scholar 

  43. Zhou D et al (2020) State of health monitoring and remaining useful life prediction of lithium-ion batteries based on temporal convolutional network. IEEE Access 8:53307–53320

    Article  Google Scholar 

  44. Wei Y, Wu D (2023) Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms. Reliab Eng Syst Saf 230:108947

    Article  Google Scholar 

  45. Tang X et al (2023) Lithium-ion battery remaining useful life prediction based on hybrid model. Sustainability 15(7):6261

    Article  CAS  Google Scholar 

Download references

Funding

This research was financially supported by the Ministry of Small and Medium-sized Enterprises(SMEs) and Startups(MSS), Korea, under the “Regional Specialized Industry Development Plus Program (R &D, S3246057)” supervised by the Korea Technology and Information Promotion Agency for SMEs(TIPA). This work was also financially supported by the Ministry Of Trade, Industry & ENERGY(MOTIE) through the fostering project of The Establishment Project of Industry-University Fusion District).

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Authors and Affiliations

  1. Department of Electronic Engineering, Jeju National University, Jeju, 63243, South Korea

    Sadiqa Jafari

  2. Department of Computer Engineering, Major of Electronic Engineering, Jeju National University, Institute of Information Science and Technology, Jeju, 63243, South Korea

    Yung Cheol Byun

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  1. Sadiqa Jafari
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YCB contributed to Funding acquisition and Project administration; SJ contributed to Investigation, Methodology and Writing—review & editing. All authors have read and agreed to the published version of the manuscript

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Correspondence to Yung Cheol Byun.

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Jafari, S., Byun, Y.C. Accurate remaining useful life estimation of lithium-ion batteries in electric vehicles based on a measurable feature-based approach with explainable AI. J Supercomput 80, 4707–4732 (2024). https://doi.org/10.1007/s11227-023-05648-8

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