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Creators/Authors contains: "Olugbade, Emmanuel"

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  1. ; (, Applied Physics Letters)
    The accuracy of single particle (SP) models for lithium-ion batteries at high C-rates is constrained by lithium concentration gradients in the electrolyte, which affect ionic conductivity, overpotential, and reaction rates. This study addresses these limitations using extreme gradient boosting machine learning (ML). By training our ML model with data from a comprehensive electrochemical (P2D) model and performing sensitivity analysis on key battery parameters, we enhance predictive accuracy. Compared to conventional SP and P2D models under constant current loading, our ML-based SP model achieves similar predictive accuracy to P2D, with significant improvements in computational efficiency. Additionally, the ML-based SP model demonstrates improved predictive accuracy under dynamic loading conditions, providing a practical framework for improving battery management and safety. 
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  2. ; ; ; ; ; (, 2023 IEEE Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMC+SIPI))
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  3. ; ; ; ; ; ; ; ; ; ; et al (, 2022 IEEE International Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMCSI))
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