Data-driven battery degradation study

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This project, in collaboration with Upside Energy Ltd (UEL), will create a programme of research focused on data-driven battery energy storage degradation. UEL has an innovative, cloud-based platform that can connect with a multitude of devices across commercial, industrial and domestic sites.It uses advanced algorithms and artificial intelligence to match energy demand with the available supply, helping the electricity grid deal with fluctuations and times of peak usage.

The knowledge exchange and feasibility parts of this project will use the PIs expertise in battery energy storage systems to establish the challenges and requirements of using UEL’s data for the estimation of battery states including prediction of battery cell degradation.

The project will deliver methods for estimating battery states that UEL will evaluate on their data set. These methods have been developed using the PIs research on the 2MW/1MWh Willenhall battery energy storage system and data collected on battery cells in the lab. This project will enable these methods to be validated against a range of different battery systems whilst meeting the requirements that underlying datasets cannot be shared outside of UEL. Finally, to facilitate data sharing between HEI’s for algorithm validation, improvements will be made to an open-source database software for storing lab battery test data.

Project Lead

Professor Dan Gladwin – University of Sheffield


Upside Energy Ltd

University of Oxford

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