Session: Government Agency Student Posters

Paper Number: 173183

Data-Driven Modeling of Non-Linear Dynamics of Lithium-Ion Batteries With Dynamic Mode Decomposition 

Lithium-ion batteries play a crucial role in portable energy storage systems for applications in electric vehicles (EVs), drones, electric vertical take-off and landing vehicles(e-VTOLs), electric aircraft and so on for their higher energy density, longer cycle life and higher energy conversion efficiency. The complex electrochemical behavior of lithium-ion batteries result in non-linear dynamics and appropriate modeling of this non-linear dynamical system is of interest for better management and control. Significant efforts were put forward to understand the behavior of battery systems and dynamics from first principles through solving a set of coupled partial differential equations, commonly known as pseudo-2-dimensional (P2D) model. This approach is known as physics-based modeling techniques. System identification techniques, on the other hand, only rely on input-output data to model a system and are commonly known as data-driven modeling techniques.

In this work, we proposed a family of dynamic mode decomposition (DMD)-based data driven models that do not require detailed knowledge of the composition of the battery materials but can essentially capture the non-linear dynamics with higher computational efficiency. Only voltage and current data obtained from hybrid pulse power characterization (HPPC) tests were utilized to form the state space matrices and subsequently used for predicting the future terminal voltage at different state of charge (SoC) and aging levels. To construct the system model, 60% of the data from a single HPPC test was utilized to generate time-delay embedded snapshots, with embedding dimension ranging from 40 to 2000. Among these, an embedding dimension of 1810 resulted in the least residual sum of squares over the signal sum of squares (RSS/SSS) error of 3.7473% for the dynamic mode decomposition with control (DMDc) model and 32% for the standard DMD model. For DMDc model, delay embeddings (ranging from 1 to 12) were also incorporated into the input current signals. For the input matrix, an embedding dimension of 6 resulted in a minimum RSS/SSS error of 1.7374%. Furthermore, the system matrices A and B identified from the HPPC test when the cell is in its healthy state were held fixed and used to simulate the system dynamics for aged batteries by updating only the control input. Despite the presence of nonlinear degradation effects in later cycles, the DMDc model effectively captured key inner dynamics such as voltage dips and transient responses for subsequent charge and discharge cycles. As for the standard DMD model, the state-transition matrix A obtained from the healthy HPPC data was also able to capture the transient dynamics of the aged battery, but the resulting residuals were significantly higher compared to the DMDc model. Overall, the DMDc approach demonstrated higher computational efficiency and significantly improved accuracy in capturing the battery's charge and discharge dynamics. The findings of this study underscore the potential of using data-driven system identification techniques to significantly improve online battery monitoring, control methodologies, and prognostic accuracy.

Presenting Author: Khalid Mahmud Labib South Dakota State University

Presenting Author Biography: I am a master's student in the Department of Mechanical Engineering at South Dakota State University. I currently hold the position of Graduate Research Assistant under the supervision of Dr. Shabbir Ahmed. My research focuses on data-driven modeling and analysis of lithium-ion batteries, with particular interest in system identification and predictive modeling techniques

Authors:

Khalid Mahmud Labib South Dakota State University
Shabbir Ahmed South Dakota State University