Plenary Lecture - Applications of path Signature methods to electric battery lifetime prognostics

Abstract:
We discuss recent developments in Battery life modelling and prediction using data-driven tools, including new insights on battery degradation via Path Signatures. The Path signature is a rich mathematical structure originating from the field of rough path theory. It is characterized by the ability to extract high-level information from a stream of data using a few summary parameters, which in turn can be used as features for a machine learning model.

We present our findings in the context of two real-life use cases (#1 and #2). From a mathematical point of view, our results are achieved by leveraging key properties of path signatures:  signature invariance under time reparameterizations, and to use the signature's rich expressivity inside a Markovian model to model the highly non-linear non-Markovian degradation of electric batteries.

References:
#1 Ibraheem, R., Wu, Y., Lyons, T. and Dos Reis, G., 2023. Early prediction of Lithium-ion cell degradation trajectories using signatures of voltage curves up to 4-minute sub-sampling rates. Applied Energy, 352, p.121974. https://doi.org/10.1016/j.apenergy.2023.121974

#2 Ibraheem, R., Dechent, P., & Dos Reis, G. (2025). Path signature-based life prognostics of Li-ion battery using pulse test data. Applied Energy, 378, 124820. https://doi.org/10.1016/j.apenergy.2024.124820

## Bayer, C., dos Reis, G., Horvath, B., and Oberhauser, H. (Eds.). 2025. Signature Methods in Finance: An Introduction with Computational Applications. Springer Nature. 

Speaker: Goncalo Dos Reis (University of Edinburgh)
Co-Authors: R. Ibraheem (UoE), Y. Wu (Strathclyde), T. Lyons (Oxford), P. Dechent (Aachen--Oxford).

Date and Time: Wednesday, September 30, 2026 | 08:30 - 09:30
Venue: NTI Lecture Hall