Learning Lithium-Ion Battery Health in Real-Time
Authors: Sahoo Avimanyu , Narayanan Vignesh 
Published: December 2024

Health-conscious decision-making by battery management systems (BMS) is crucial for the safe operation of lithium-ion batteries. Traditional battery models often struggle to accurately predict the dynamic behavior of lithium-ion batteries over time due to the degradation of their health. The Electrochemical-Thermal-Aging (ETA) model, which integrates single-particle, thermal, and aging models, is comprehensive but computationally intensive and challenging to implement on-board a BMS. Recent advancements in physics-informed neural networks (PINNs) provide a promising solution by approximating the solution to the ETA dynamics. Additionally, PINNs offer a significant advantage by avoiding the need for discretization when solving partial differential equations (PDEs) based on ETA models. However, PINNs face difficulties in predicting the future behavior of lithium-ion cells when confronted with new, unseen data resulting from accelerated degradation, internal faults, or abusive conditions.
Our recent work addresses this challenge by developing a novel approach that enhances PINNs with online learning capabilities. By embedding the PINN with the ability to learn continuously, we enable it to adapt to health degradation, maintaining its predictive accuracy over the battery's lifespan. We leverage dynamical system theory to develop the online training algorithm to update the weights. The deviation between the model's predicted output and the measured values is used to adjust the weights of the PINN's final layer online, ensuring that the model's predictions are continuously refined based on the latest data. An essential aspect of our approach is providing analytical guarantees for the convergence of the neural network's weight estimation error and the model output error. Using Lyapunov theory for dynamical systems, we ensure that the adjustments made during online learning lead to a stable and convergent solution. This theoretical foundation is crucial for validating the reliability and robustness of the proposed online learning mechanism. To demonstrate the effectiveness of our online learning scheme, we present simulation results that highlight the performance improvements achieved. These simulations show that the enhanced PINN can accurately track the battery's health status over time, adapting to changes in internal parameters and external conditions. The results also illustrate the computational efficiency of the proposed method, making it feasible for on-board implementation in real-world BMS applications.
In summary, the poster will present our novel approach to embedding PINNs with online learning capabilities, which represents a significant advancement in battery management systems. By enabling real-time adaptation to battery health degradation, our method ensures more accurate predictions, safer operation, and a prolonged battery lifespan. The integration of dynamical system theory, online learning mechanisms, and theoretical guarantees provides a robust and reliable solution for next-generation BMS applications. Through this work, we aim to pave the way for more intelligent, adaptable, and health-conscious battery management systems.