Abstract
Learning the deterioration of a battery from charge and discharge data is associated with different non-random uncertainties. A specific methodology is developed, capable of integrating expert knowledge about the problem and of handling the epistemic uncertainty associated with conflicts in the available information. It is shown that the simple concatenation of charge and discharge data in a single training set leads to a biased model. Weak supervision techniques are used to assess the relative importance of subsets of the training data in the empirical loss function.
Supported by Ministerio de Economía e Industria de España, grant PID2020-112726RB-I00 and by Principado de Asturias, grant SV-PA-21-AYUD/2021/50994.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amel, K.R.: From shallow to deep interactions between knowledge representation, reasoning and machine learning. In: Proceedings 13th International Conference Scala Uncertainity Mgmt (SUM 2019), Compiegne, LNCS, pp. 16–18 (2019)
Anseán, D., Baure, G., González, M., Cameán, I., García, A., Dubarry, M.: Mechanistic investigation of silicon-graphite/LiNi0.8Mn0.1Co0.1O2 commercial cells for non-intrusive diagnosis and prognosis. J. Power Sour. 459, 227882 (2020)
Bauckhage, C., Ojeda, C., Schücker, J., Sifa, R., Wrobel, S.: Informed machine learning through functional composition. In: LWDA, pp. 33–37 (2018)
Birkl, C.R., Roberts, M.R., McTurk, E., Bruce, P.G., Howey, D.A.: Degradation diagnostics for lithium ion cells. J. Power Sour. 341, 373–386 (2017)
Bloom, I., Jansen, A.N., Abraham, D.P., Knuth, J., Jones, S.A., Battaglia, V.S., Henriksen, G.L.: Differential voltage analyses of high-power, lithium-ion cells: 1. technique and application. J. Power Sour. 139(1–2), 295–303 (2005)
Conway, G., Joshi, A., Leach, F., García, A., Senecal, P.K.: A review of current and future powertrain technologies and trends in 2020. Transp. Eng. 5, 100080 (2021)
Dubarry, M., Baure, G., Anseán, D.: Perspective on state-of-health determination in lithium-ion batteries. J. Electrochem. Energy Conv. Storage 17(4), 044701 (2020)
Dubarry, M., Svoboda, V., Hwu, R., Liaw, B.Y.: Incremental capacity analysis and close-to-equilibrium OCV measurements to quantify capacity fade in commercial rechargeable lithium batteries. Electrochem. Solid State Lett. 9(10), A454 (2006)
Dubarry, M., Truchot, C., Liaw, B.Y.: Synthesize battery degradation modes via a diagnostic and prognostic model. J. Power Sour. 219, 204–216 (2012)
Guillaume, R., Dubois, D.: A min-max regret approach to maximum likelihood inference under incomplete data. Int. J. Approximate Reasoning 121, 135–149 (2020)
Han, X., Lu, L., Zheng, Y., Feng, X., Li, Z., Li, J., Ouyang, M.: A review on the key issues of the lithium ion battery degradation among the whole life cycle. ETransportation 1, 100005 (2019)
Hüllermeier, E., Destercke, S., Couso, I.: Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In: Ben Amor, N., Quost, B., Theobald, M. (eds.) SUM 2019. LNCS (LNAI), vol. 11940, pp. 266–279. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35514-2_20
Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110(3), 457–506 (2021)
Palacín, M.R.: Understanding ageing in li-ion batteries: a chemical issue. Chem. Soc. Rev. 47(13), 4924–4933 (2018)
Rauf, H., Khalid, M., Arshad, N.: Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling. Renew. Sustain. Energy Rev. 156, 111903 (2022)
von Rueden, L., et al.: Informed machine learning - a taxonomy and survey of integrating prior knowledge into learning systems. IEEE Trans. Knowl. Data Eng. (In press)
Severson, K.A., et al.: Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4(5), 383–391 (2019)
Shim, J., Kostecki, R., Richardson, T., Song, X., Striebel, K.A.: Electrochemical analysis for cycle performance and capacity fading of a lithium-ion battery cycled at elevated temperature. J. Power Sourc.112(1), 222–230 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Sánchez, L., Costa, N., Anseán, D., Couso, I. (2022). Informed Weak Supervision for Battery Deterioration Level Labeling. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_59
Download citation
DOI: https://doi.org/10.1007/978-3-031-08974-9_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08973-2
Online ISBN: 978-3-031-08974-9
eBook Packages: Computer ScienceComputer Science (R0)