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

CORDIS · observation · Publication date unknown

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

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recordType

award

status

SIGNED

region

EU

value

276187.92

unit

EUR