Prediction Of The Remaining Useful Life Of Lithium-Ion Batteries Based On An Empirical Mode Approach With Artificial Neural Networks

Öz

Forecasting future capacities and estimating the remaining useful life, while incorporating uncertainty quantification, poses a crucial yet formidable challenge in the realm of battery health diagnosis and management. In this study, a data-driven model based on artificial neural networks (ANN) and signal decomposition techniques including Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Empirical Wavelet Transform (EWT) is presented to predict the capacity value of lithium-ion batteries. Signal decomposition was performed using the discharge voltage values for four different batteries. A total of 22 features were obtained. The features of the signal decomposition methods were evaluated separately as well as hybrid approaches. Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) performance metrics are used in the proposed method and the values obtained are 3.67×10-6, 0.001351 and 0.002311, respectively. According to the findings, the hybrid model proposed demonstrated positive results in terms of accuracy, adaptability, and robustness.

Anahtar Kelimeler

• Lithium-ion batteries
• Signal decomposition
• Artificial neural networks
• Prediction

Kaynakça

1. [1]. Adnan, M., The Future of Energy Storage: Advancements and Roadmaps for Lithium-Ion Batteries. 2023, MDPI. p. 7457.
2. [2]. Fang, H. Challenges with the ultimate energy density with Li-ion batteries. in IOP Conference Series: Earth and Environmental Science. 2021. IOP Publishing.
3. [3]. Hanifah, R.A., S.F. Toha, and S. Ahmad, Electric Vehicle Battery Modelling and Performance Comparison in Relation to Range Anxiety. Procedia Computer Science, 2015. 76: p. 250-256.
4. [4]. Ji, Y., et al., An RUL prediction approach for lithium-ion battery based on SADE-MESN. Applied Soft Computing, 2021. 104: p. 107195.
5. [5]. Pang, X., et al., A lithium-ion battery RUL prediction method considering the capacity regeneration phenomenon. Energies, 2019. 12(12): p. 2247.
6. [6]. Deng, Y., et al., Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries. Energy, 2019. 176: p. 91-102.
7. [7]. Dai, H., et al., A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain. IEEE Transactions on Industrial Electronics, 2019. 66(10): p. 7706-7716.
8. [8]. Li, P., et al., State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network. Journal of Power Sources, 2020. 459: p. 228069.

9. [9]. Chang, Y., H. Fang, and Y. Zhang, A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery. Applied Energy, 2017. 206: p. 1564-1578.
10. [10]. Chen, Z., et al., State of Health Estimation for Lithium-ion Batteries Based on Fusion of Autoregressive Moving Average Model and Elman Neural Network. IEEE Access, 2019. 7: p. 102662-102678.
11. [11]. Wei, J., G. Dong, and Z. Chen, Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression. IEEE Transactions on Industrial Electronics, 2018. 65(7): p. 5634-5643.
12. [12]. Gao, D. and M. Huang, Prediction of remaining useful life of lithium-ion battery based on multi-kernel support vector machine with particle swarm optimization. Journal of Power Electronics, 2017. 17(5): p. 1288-1297.
13. [13]. Li, X., C. Yuan, and Z. Wang, State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression. Energy, 2020. 203: p. 117852.
14. [14]. Fei, Z., et al., Early prediction of battery lifetime via a machine learning based framework. Energy, 2021. 225: p. 120205.
15. [15]. Sahaand, B. and K. Goebel, Battery Data Set, NASA ames prognostics data repository. NASA Ames Research Center, 2007.
16. [16]. Zhao, L., Y. Wang, and J. Cheng, A Hybrid Method for Remaining Useful Life Estimation of Lithium-Ion Battery with Regeneration Phenomena. Applied Sciences, 2019. 9(9): p. 1890.
17. [17]. Huang, N.E., et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 1998. 454(1971): p. 903-995.
18. [18]. Wu, Z. and N.E. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis, 2009. 1(01): p. 1-41.
19. [19]. Torres, M.E., et al. A complete ensemble empirical mode decomposition with adaptive noise. in 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). 2011. IEEE.
20. [20]. Gilles, J., Empirical wavelet transform. IEEE transactions on signal processing, 2013. 61(16): p. 3999-4010.
21. [21]. Liu, W. and W. Chen, Recent advancements in empirical wavelet transform and its applications. IEEE Access, 2019. 7: p. 103770-103780.
22. [22]. Hu, Y., et al., An enhanced empirical wavelet transform for noisy and non-stationary signal processing. Digital signal processing, 2017. 60: p. 220-229.
23. [23]. Yegnanarayana, B., Artificial neural networks. 2009: PHI Learning Pvt. Ltd.
24. [24]. Zou, J., Y. Han, and S.-S. So, Overview of artificial neural networks. Artificial neural networks: methods and applications, 2009: p. 14-22.
25. [25]. Abraham, A., Artificial neural networks. Handbook of measuring system design, 2005.
26. [26]. Li, L., et al., Battery Remaining Useful Life Prediction with Inheritance Particle Filtering. Energies, 2019. 12(14): p. 2784.
27. [27]. Zhang, C., et al., Capacity Prognostics of Lithium-Ion Batteries using EMD Denoising and Multiple Kernel RVM. IEEE Access, 2017. 5: p. 12061-12070.
28. [28]. Ali, M.U., et al., Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features. Energies, 2019. 12(22): p. 4366.
29. [29]. Wu, J., C. Zhang, and Z. Chen, An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Applied Energy, 2016. 173: p. 134-140.
30. [30]. Khumprom, P. and N. Yodo, A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm. Energies, 2019. 12(4): p. 660.
31. [31]. Ansari, S., et al., Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries. Energies, 2021. 14(22): p. 7521