Towards a Probabilistic Fusion Approach for Robust Battery Prognostics
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Abstract
Batteries are a key enabling technology for the decarbonization of transport and energy sectors. The safe and reliable operation of batteries is crucial for battery-powered systems. In this direction, the development of accurate and robust battery state-of-health prognostics models can unlock the potential of autonomous systems for complex, remote and reliable operations. The combination of Neural Networks, Bayesian modelling concepts and ensemble learning strategies, form a valuable prognostics framework to combine uncertainty in a robust and accurate manner. Accordingly, this paper introduces a Bayesian ensemble learning approach to predict the capacity depletion of lithium-ion batteries. The approach accurately predicts the capacity fade and quantifies the uncertainty associated with battery design and degradation processes. The proposed Bayesian ensemble methodology employs a stacking technique, integrating multiple Bayesian neural networks (BNNs) as base learners, which have been trained on data diversity. The proposed method has been validated using a battery aging dataset collected by the NASA Ames Prognostics Center of Excellence. Obtained results demonstrate the improved accuracy and robustness of the proposed probabilistic fusion approach with respect to (i) a single BNN model and (ii) a classical stacking strategy based on different
BNNs.
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Bayesian Neural Network, Uncertainty Quantification, Bayesian Ensemble, Battery Health Management
terior predictive framework for weighting ensemble regional climate models. Geoscientific Model Development, 10(6), 2321–2332. Gneiting, T., Raftery, A. E., Westveld, A. H., & Goldman,
10.1016/j.inffus.2021.05.008
Bai, G., & Chandra, R. (2023, November). Gradient boosting Bayesian neural networks via Langevin MCMC. Neurocomputing, 558, 126726. doi:
10.1016/j.neucom.2023.126726
Barrett, J. P. (1974). The coefficient of determination—some limitations. The American Statistician, 28(1), 19–20. Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra,
D. (2015). Weight uncertainty in neural network. In F. Bach & D. Blei (Eds.), International conference on machine learning (Vol. 37, pp. 1613–1622). Lille, France: PMLR. Bosman, P. A., & Thierens, D. (2000). Negative loglikelihood and statistical hypothesis testing as the basis of model selection in ideas. In Proceedings of the tenth dutch–netherlands conference on machine learning. tilburg university. Che, Y., Zheng, Y., Forest, F. E., Sui, X., Hu, X., & Teodorescu, R. (2024, January). Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection. Reliability Engineering & System Safety, 241, 109603. doi:
T. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review, 133(5), 10981118. doi: 10.1175/MWR2904.1 Hadigol, M., Maute, K., & Doostan, A. (2015). On uncertainty quantification of lithium-ion batteries: Application to an lic6/licoo2 cell. Journal of Power Sources, 300, 507–524. Hodson, T. O. (2022). Root mean square error (rmse) or mean absolute error (mae): When to use them or not. Geoscientific Model Development Discussions, 2022, 1–10. Jung, Y., Jo, H., Choo, J., & Lee, I. (2022, June). Statistical model calibration and design optimization under aleatory and epistemic uncertainty. Reliability Engineering & System Safety, 222, 108428. doi:
10.1016/j.ress.2022.108428
Kuleshov, V., Fenner, N., & Ermon, S. (2018, July). Accurate uncertainties for deep learning using calibrated regression. In J. Dy & A. Krause (Eds.), Proceedings of the 35th international conference on machine learning (Vol. 80, pp. 2796–2804). PMLR.
LeBlanc, M., & Tibshirani, R. (1996). Combining estimates in regression and classification. Journal of the American Statistical Association, 91(436), 1641–1650.
Lee, G., Kwon, D., & Lee, C. (2023). A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability. Mechanical Systems and Signal Processing, 188, 110004. doi:
10.1016/j.ress.2023.109603
10.1016/j.ymssp.2022.110004
Chung, Y., Char, I., Guo, H., Schneider, J., & Neiswanger, W.
(2021). Uncertainty toolbox: an open-source library for assessing, visualizing, and improving uncertainty quantification. arXiv preprint arXiv:2109.10254. Cobb, A. D., Himes, M. D., Soboczenski, F., Zorzan, S., O’Beirne, M. D., Baydin, A. G., . . . Angerhausen, D. (2019, June). An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval. The Astronomical Journal, 158(1), 33. doi: 10.3847/15383881/ab2390 Dai, H., Pollock, M., & Roberts, G. O. (2023, February).
Bayesian fusion: Scalable unification of distributed statistical analyses. Journal of the Royal Statistical Society Series B: Statistical Methodology, 85(1), 84–107. doi: 10.1093/jrsssb/qkac007 Dillon, J. V., Langmore, I., Tran, D., Brevdo, E., Vasudevan, S., Moore, D., . . . Saurous, R. A. (2017, November). TensorFlow Distributions (No. arXiv:1711.10604). arXiv.
Fan, Y., Olson, R., & Evans, J. P. (2017). A bayesian pos-
Liu, Y., Sun, J., Shang, Y., Zhang, X., Ren, S., & Wang, D. (2023, May). A novel remaining useful life prediction method for lithium-ion battery based on long short-term memory network optimized by improved sparrow search algorithm. Journal of Energy Storage, 61, 106645. doi: 10.1016/j.est.2023.106645 Nam, G., Yoon, J., Lee, Y., & Lee, J. (2021). Diversity matters when learning from ensembles. In M. Ranzato,
A. Beygelzimer, Y. Dauphin, P. Liang, & J. W. Vaughan (Eds.), Advances in neural information processing systems (Vol. 34, pp. 8367–8377). Curran Associates, Inc. Nemani, V., Biggio, L., Huan, X., Hu, Z., Fink, O., Tran, A., . . . Hu, C. (2023). Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial. Mechanical Systems and Signal Processing, 205, 110796. doi:10.1016/j.ymssp.2023.110796
Saha, B., & Goebel, K. (2007). Nasa ames prognostics data repository. NASA Ames, Moffett Field, CA, USA.
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