This paper presents a reduced-order modeling technique for energy storage systems (ESS) such as Lithium-ion batteries (LIBs). Data-driven models (DDMs) accurately represent the system dynamics without requiring the in situ measurements and proprietary information needed by physics-based models. However, the DDM sometimes result in poor performance in unseen scenarios as they tend to overfit the available data. Here, we present a novel data-driven modeling technique to discover the governing equations of individual cells using only the excitation inputs and measured outputs. Instead of adding generic terms to discover the model, we seek physics-informed reduced order nonlinear models. Our technique is based on Sparse Identification of Nonlinear Dynamics with Control (SINDYc) and was solved using Sequentially Thresholded Ridge regression (STRidge) optimization. The method accounts for the noisy data using Kalman filters, which update the terms for an enhanced state of charge (SOC) estimation. We propose using two scenarios as training and validation sets to tune the hyperparameters (threshold and regularization parameters) and a third scenario to validate the model (test set). The data for the system identification was generated using a high-fidelity model of a Li-ion cell. The model was trained on uniformly distributed electrical current signals with maximum amplitudes of 2C charge and 4C discharge rates. We used the US-highway profile (US06) as the validation set. The generalizability of the model was assessed with Urban Dynamometer Driving Schedule (UDDS) data where the identified model achieved the normalized root mean square error (NRMSE) of $8.3\times10^{-5}$ for SOC and Voltage predictions.

O. Ahmadzadeh, Y. Wang, and D. Soudbakhsh, “Sparse modeling of energy storage systems in presence of noise,” IFAC-PapersOnLine 56.2 (2023): 3764-3769

doi: https://doi.org/10.1016/j.ifacol.2023.10.1546

Accurate modeling of Lithium-ion batteries (LiBs) allows for more efficient utilization of their potential without compromising their safety or useful life. Accurate physics-based models require in-situ measurements and proprietary information unavailable for each cell. Data driven models offer a solution to identify governing equations of individual cells using only the excitation inputs and measured outputs. However, the main drawback of such models is their performance in unseen scenarios, as they tend to overfit the training data and perform poorly in other scenarios. We seek physics-informed reduced-order nonlinear models of LiBs from measured data. The model was trained using a high-fidelity model of a Li-ion cell. We used Sequentially Thresholded Ridge regression (STRidge) optimization to determine the optimal reduced-order model. Using a validation set, we proposed an algorithm to tune hyperparameters (threshold and regularization parameters). A stochastic electrical current signal up to 2C/4C-rates charge/discharge was used in the training set, and the US highway profile (US06 drive cycle) was used for the validation. The model was validated with EPA Urban Dynamometer Driving Schedule (UDDS) as the test set. The test errors (normalized root mean square error (NRMSE)) were 6.3e-3 for SOC and Voltage predictions.

O. Ahmadzadeh, R. Rodriguez, Y. Wang and D. Soudbakhsh, “A Physics-Inspired Machine Learning Nonlinear Model of Li-ion Batteries,” 2023 American Control Conference (ACC), San Diego, CA, USA, 2023, pp. 3087-3092,

doi: 10.23919/ACC55779.2023.10156368

Renato’s paper on an adaptive control strategy to optimize the sailing maneuvers of an AC75 foiling sailboat competing in America’s Cup, was accepted for publication at the Journal of Dynamic Systems, Measurements, and Control, 11/2022.

Rodriguez, R., Wang, Y., Ozanne, J., Morrow, J., Sumer, D., Filev, D., Soudbakhsh, D., “Adaptive Learning and Optimization of High-speed Sailing Maneuvers for America’s Cup”, Journal of Dynamic Systems, Measurements, and Control 2022.

Abstract: This paper presents an adaptive control strategy to optimize the sailing maneuvers of an AC75 foiling sailboat competing in America’s Cup. Foiling yachts have nonlinear, high-dimensional, and unstable dynamics due to several articulations for fast motions and maneuverability. Achieving aggressive and optimal maneuvers requires taking these complex dynamics into account instead of analytical optimizations using reduced-order models.
We compared extremum-seeking and Jacobian learning (JL) control approaches on a full-order model to achieve optimal maneuvers and used JL to optimize articulations. The controllers were integrated with a high-fidelity sailboat simulator for safe and efficient maneuver optimization.
The optimal solutions were subject to physical/actuator constraints and those enforced to ensure the feasibility of the maneuvers by humans (sailors). The close-hauled and tacking maneuvers were optimized to achieve maximum Velocity Made Good (VMG) and minimum loss of VMG, respectively. The optimal maneuvers boast a marginal VMG loss of less than 1.5%, which enables exploiting areas of good wind conditions in the racing environment.

https://doi.org/10.1115/1.4056107