Renato‘s research on “Accurate State-of-Charge Prediction of Li-ion Batteries”, was awarded 1st place at Temple University’s Graduate Research Poster Competition, on February 21th, 2024.

Research Topic

Data-driven Discovery of Governing Equations of Li-ion Batteries Pertaining State of Charge

Publications

• Rodriguez, R., Ahmadzadeh, O., Wang, Y., & Soudbakhsh, D. “Data-Driven Discovery of Lithium-Ion Battery State of Charge Dynamics.” ASME. J. Dyn. Sys., Meas., Control. January 2024; 146(1): 011101. https://doi.org/10.1115/1.4064026.
• Rodriguez, R., Ahmadzadeh, O., Wang, Y., & Soudbakhsh, D. (2023, May). Discovering governing equations of li-ion batteries pertaining state of charge using input-output data. In 2023 American Control Conference (ACC) (pp. 3081-3086), IEEE, https://doi.org/10.23919/ACC55779.2023.10156114.
• Ahmadzadeh, O., Rodriguez, R., Wang, Y., & Soudbakhsh, D. (2023, May). A physics-inspired machine learning nonlinear model of li-ion batteries. In 2023 American Control Conference (ACC) (pp. 3087-3092). IEEE, https://doi.org/10.23919/ACC55779.2023.10156368.
• Ahmadzadeh, O., Rodriguez, R., and Soudbakhsh, D. “Modelling of Li-ion Batteries for Real-Time Analysis: A Data-Driven Approach”, American Control Conference, 2022, https://doi.org/10.23919/ACC53348.2022.9867616

Title: A data-driven framework for learning governing equations of Li-ion batteries and co-estimating voltage and state-of-charge

Abstract: This paper presents a reduced-order nonlinear model for Lithium-ion batteries (LiBs). Unlike mechanistic models, data-driven models offer accurate representations of system dynamics without relying on in-situ measurements and proprietary information. However, these models may perform poorly in unseen scenarios due to overfitting training data, which is typical. We propose a physics-inspired, data-driven approach to determine LiBs governing equations based on their electrochemistry rather than generic terms. We employ a sparse identification method achieved through sequentially thresholding ridge regression to construct a nonlinear model from electrical current (excitation input) and measured voltage. We formulate the problem to optimize the sparsification parameters as hyperparameters and minimize a cost function comprised of training and validation sets and the number of terms as a measure of complexity. We augment the model with a joint unscented Kalman filter to handle noisy experimental data, enabling a more accurate estimate of the state of charge (SOC) and voltage. Model performance in unseen scenarios is evaluated with urban dynamometer driving schedule (UDDS) data, where the identified model achieves a root mean square error of 1.26e−2 for SOC and voltage prediction.

Ahmadzadeh, O., Wang, Y., & Soudbakhsh, D. (2024). A data-driven framework for learning governing equations of Li-ion batteries and co-estimating voltage and state-of-charge. Journal of Energy Storage, https://doi.org/10.1016/j.est.2024.110743

Title: Data-driven Discovery of Lithium-Ion Battery State of Charge Dynamics

Abstract: 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’s 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’s 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’s 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 × 10-6 and 4.8 × 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 × 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.

• Rodriguez, R., Ahmadzadeh, O., Wang, Y., & Soudbakhsh, D. (2023). Data-driven Discovery of Lithium-Ion Battery State of Charge Dynamics. Journal of Dynamic Systems, Measurement, and Control, 1-13, https://doi.org/10.1115/1.4064026.