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Multiphysics-informed machine learning for assessing battery safety risk evolution with degradation

Project description

Efficient method to model battery degradation safety risk

The rise of electric vehicles (EVs) has raised concerns about battery safety. Over 80 % of EV fires occur under normal conditions, without explicit abuse, suggesting that battery degradation may play a role. Supported by the Marie Skłodowska-Curie Actions programme, the MIRACLE project is developing a method for simulating and predicting the progression of lithium-ion batteries into unsafe states under typical usage conditions, with a focus on degradation effects. The project will create a physics-based model to link degradation and failure mechanisms, capturing how batteries move to unsafe conditions. It will also integrate multiphysics with machine learning to enable fast predictions of battery failure, establishing the first state variable to assess battery safety during use.

Objective

The growing use of electric vehicles (EVs) and shift to higher-energy-density battery chemistry have made battery safety a top research priority. The statistics show that >80% of EV fire accidents occurred without explicit abuse, where battery degradation can play a role. Nevertheless, most existing battery safety models focus on thermal runaway triggered by various forms of abuse, largely overlooking the evolution of battery safety with degradation during “normal” cycling conditions. MIRACLE therefore aims to address this gap by developing an efficient and precise approach for simulating and predicting the progression of lithium-ion batteries into unsafe states under non-abuse conditions with a focus on degradation effects. Specifically, MIRACLE will (1) develop a physics-based model that fundamentally bridges the gap between degradation and failure mechanisms, capturing when and how batteries will transition into unsafe states as a consequence of multiple coupled degradation mechanisms over significant periods of time, (2) embed the multiphysics into machine learning to enable fast, precise and physically interpretable predictions of battery cycling to failure, and (3) conduct model-based analyses to formulate the first state variable for estimating battery safety status evolution during cycling, addressing a key missing piece in current battery management systems and thereby enhancing the reliability and safety of battery operation. The multidisciplinary research leverages the collective expertise of the host group and the fellow, promoting effective two-way knowledge transfer. The fellowship programme sets the stage for significant personal and professional growth, paving the way for the fellow to become an independent academic in Europe.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
Net EU contribution
€ 276 187,92
Address
SOUTH KENSINGTON CAMPUS EXHIBITION ROAD
SW7 2AZ London
United Kingdom
Region
London Inner London — West Westminster
Activity type
Higher or Secondary Education Establishments
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Total cost
No data

Partners (2)