Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model

• URL: http://arxiv.org/abs/2404.17174v1
• Date: Fri, 26 Apr 2024 06:06:37 GMT
• Title: Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model
• Authors: Constantin-Daniel Nicolae, Sara Sameer, Nathan Sun, Karena Yan,
• Abstract summary: Accurately measuring the cycle lifetime of commercial lithium-ion batteries is crucial for performance and technology development. We introduce a novel hybrid approach combining a physics-based equation with a self-attention model to predict the cycle lifetimes of commercial lithium iron phosphate graphite cells via early-cycle data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately measuring the cycle lifetime of commercial lithium-ion batteries is crucial for performance and technology development. We introduce a novel hybrid approach combining a physics-based equation with a self-attention model to predict the cycle lifetimes of commercial lithium iron phosphate graphite cells via early-cycle data. After fitting capacity loss curves to this physics-based equation, we then use a self-attention layer to reconstruct entire battery capacity loss curves. Our model exhibits comparable performances to existing models while predicting more information: the entire capacity loss curve instead of cycle life. This provides more robustness and interpretability: our model does not need to be retrained for a different notion of end-of-life and is backed by physical intuition.

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