We are working towards data-driven modeling (DDM) of Li-ion batteries (LIBs). Lithium-ion batteries are present in many modern world applications due to several desirable properties like high energy and power density. Accurate real-time modeling of the LiBs improves their operation and safety. However, developing physics-based models is a very cumbersome, time-consuming task and requires several measurements and information that often are not available. The new DDM techniques offer a solution for control-oriented modeling of energy storage devices. We developed a sparse model of batteries using a technique called sparse identification of nonlinear dynamics. We explored a set of potential terms known as library to develop the model. The sparse model was achieved by formulating the problem as a ridge regression optimization and finding the dominant terms. Model performance and robustness were assessed via validation and generalization tests. Additionally, the model was tested for its robustness to noise. We showed the trend of the model parameters with the charge/discharge curves. Next, we improved the model by including information about the state of charge (SOC) in the library. The model with SOC as a parameter does not need the interpolation of the parameters as the battery goes through charge/discharge. We showed the performance of this model using the US06 highway driving cycle.
O. Ahmadzadeh, R. Rodriguez and D. Soudbakhsh, “Modeling of Li-ion batteries for real-time analysis and control: A data-driven approach,” 2022 American Control Conference (ACC), Atlanta, GA, USA, 2022, pp. 392-397