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Thesis

Data-driven battery state of health diagnostics and prognostics

Abstract:

Lithium-ion batteries are increasingly ubiquitous in modern society but the degradation of lithium-ion cells is complex and challenging to predict. Data-driven approaches to estimating and forecasting the state of health of lithium-ion batteries have become increasingly popular in literature due to the growing availability of battery data and the improved flexibility of data-driven approaches relative to physics-based modelling. This thesis begins with a review of health diagnosis and prog...

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Division:
MPLS
Department:
Engineering Science
Role:
Author

Contributors

Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Supervisor
ORCID:
0000-0002-0620-3955
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
Keywords:
Deposit date:
2022-08-20

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