{"id":924,"date":"2023-01-01T01:26:00","date_gmt":"2023-01-01T06:26:00","guid":{"rendered":"https:\/\/sites.temple.edu\/dslab\/?p=924"},"modified":"2024-04-04T14:30:12","modified_gmt":"2024-04-04T18:30:12","slug":"renatos-paper-to-appear-in-the-journal-of-dynamic-systems-measurements-and-control-jdsmc","status":"publish","type":"post","link":"https:\/\/sites.temple.edu\/dslab\/?p=924","title":{"rendered":"Publication Alert: Renato’s paper to appear in the Journal of Dynamic Systems, Measurements, and Control (JDSMC)"},"content":{"rendered":"\n

Title:<\/strong> Data-driven Discovery of Lithium-Ion Battery State of Charge Dynamics<\/a><\/p>\n\n\n\n

Abstract:<\/strong> We present a physics-inspired input\/output predictor of lithium-ion batteries (LiBs) for online state-of-charge (SOC) prediction. The complex electrochemical behavior of batteries results in nonlinear and high-dimensional dynamics. Accurate SOC prediction is paramount for increased performance, improved operational safety, and extended longevity of LiBs. The battery\u2019s internal parameters are cell-dependent and change with operating conditions and battery health variations. We present a data-driven solution to discover governing equations pertaining to SOC dynamics from battery operando measurements. Our approach relaxes the need for detailed knowledge of the battery\u2019s composition while maintaining prediction fidelity. The predictor consists of a library of candidate terms and a set of coefficients found via a sparsity-promoting algorithm. The library was enhanced with explicit physics-inspired terms to improve the predictor\u2019s interpretability and generalizability. Further, we developed a Monte Carlo search of additional nonlinear terms to efficiently explore the high-dimensional search space and improved the characterization of highly nonlinear behaviors. Additionally, we developed a hyperparameter autotuning approach for identifying optimal coefficients that balance accuracy and complexity. The resulting SOC predictor achieved high predictive performance scores (RMSE) of 2.2 \u00d7 10-6 and 4.8 \u00d7 10-4, respectively, for training and validation on experimental results corresponding to a stochastic drive cycle. Furthermore, the predictor achieved an RMSE of 8.5 \u00d7 10-4 on unseen battery measurements corresponding to the standard US06 drive cycle, further showcasing the adaptability of the predictor and the enhanced modeling approach to new conditions.<\/p>\n\n\n\n