Diagnosing Batteries via Operando Optical Fiber Sensors and Machine Learning

The battery stands as a crucial component in the pursuit of a carbon-neutral future. Maximizing the utilization of battery data is vital for improving the quality, reliability, longevity, and safety of battery systems [1]. In this presentation, we show the innovative integration of optical fiber sensors into commercial cylindrical and pouch cells [2]. Utilizing advanced optical fiber sensing technologies, we can monitor batteries in operando, capturing critical thermal and chemical phenomena such as solid electrolyte interphase formation, structural transformations, and electrolyte degradation [2-4]. By employing machine learning techniques, we have developed data-driven models capable of analyzing the extensive physicochemical data gathered from battery sensors, facilitating more effective diagnostics [5,6]. The synergy between optical fiber sensors and machine learning is set to transform battery informatics, unlocking promising possibilities for both academic research and industrial applications. Reference [1] J. Huang, S. T. Boles, J.-M. Tarascon*, “Sensing as the key to battery lifetime and sustainability”, Nature Sustainability 2022, 5, 194. [2] J. Huang, L. Albero Blanquer, J. Bonefacino, E. R. Logan, D. Alves Dalla Corte, C. Delacourt, B. M. Gallant, S. T. Boles, J. R. Dahn, H.-Y. Tam, J.-M. Tarascon*, “Operando decoding of chemical and thermal events in commercial Na(Li)-ion cells via optical sensors”, Nature Energy 2020, 5, 674. [3] J. Huang, X. Han, F. Liu, C. Gervillié, L. Albero Blanquer, T. Guo*, J.-M. Tarascon*, “Monitoring battery electrolyte chemistry via in-operando tilted fiber Bragg grating sensors”, Energy & Environmental Science 2021, 14, 6464. [4] J. Huang, C. Delacourt, P. Desai, C. Gervillié-Mouravieff, L. Albero Blanquer, R. Tan, J.-M. Tarascon*, “Unravelling thermal and enthalpy evolutions of commercial sodium-ion cells upon cycling ageing via fiber optic sensors”, Journal of The Electrochemical Society 2023, 170, 090510 [5] J. Huang, C. Delacourt, P. Desai, J.-M. Tarascon*, “Operando entropy profiling of sodium-ion batteries via optical fiber sensing for thermal management and ageing monitoring”, Journal of The Electrochemical Society 2024, 171, 030516 [6] R. Tan, X. Lu, M. Cheng, J. Li*, J. Huang*, T.-Y. Zhang*, “Forecasting battery degradation trajectory under domain shift with domain generalization”, Energy Storage Materials 2024, 72, 103725 [7] T. Zhang1, R. Tan1, P. Zhu, T.-Y. Zhang*, J. Huang*, “Unlocking ultrafast diagnosis of retired batteries via interpretable machine learning and optical fiber sensors”, under revision.