Advancing state of charge estimation for LTO battery storage: Experimental data-based techniques for digital twin development

Abeysekera, Muditha ORCID: https://orcid.org/0000-0002-1743-3254 2024. Advancing state of charge estimation for LTO battery storage: Experimental data-based techniques for digital twin development. Presented at: 2023 IEEE Smart World Congress (SWC), Portsmouth, United Kingdom, 28-31 August 2023. Proceedings 2023 IEEE Smart World Congress (SWC). IEEE, 10.1109/SWC57546.2023.10449020

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Abstract

Battery storage is a significant component of the future electrical grid. Technology is rapidly developing towards the digitalization of energy grids. Real time assessment of battery state and its characteristics is essential for dynamic management and to support power grid requirements. The digital twin of battery storage is a digital replica of a physical battery, which estimates and analyses battery operation in real-time. A digital twin of a battery system consists of data collection, pre-processing, parameter estimation, modelling, and forecasting of the state of charge, state of health and remaining unused life of the battery. This paper presents an overview of battery modelling techniques and the practical implementation of a digital twin for real lithium titanium oxide (LTO) battery storage. Physics-based models and data-driven methods are presented. A large dataset of LTO battery operation experimental data from a robot application is used to estimate the state of charge. To estimate the state of charge of the LTO battery, Kalman filter (KF) is used. Experimental and simulation results are discussed and compared.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISBN:	979-8-3503-1980-4
Last Modified:	20 Mar 2024 16:20
URI:	https://orca.cardiff.ac.uk/id/eprint/167089

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