2021 Liu Lithium Ion Modelling Monitoring State Estimation

This thesis presents a comprehensive study focused on novel modelling, monitoring, and state estimation methods for lithium-ion batteries, which are essential for various applications including electric vehicles and energy storage systems. The objective of this work is to enhance the performance and reliability of lithium-ion batteries through innovative methodologies. The methodology involves the development of advanced algorithms for state-of-charge estimation and temperature compensation, alongside data-driven calibration methods for real-time monitoring. Key experiments were performed to validate these approaches, and a dual polarization battery model using an extended Kalman filter was implemented to improve estimation accuracy. The results indicate significant improvements in state estimation precision and battery performance under varying operational conditions. Additionally, a novel temperature calibration method for Fiber Bragg-grating sensors was developed, allowing for real-time monitoring of battery temperature. These findings contribute valuable insights into the effective management and optimization of lithium-ion battery systems, thereby supporting the transition towards sustainable energy solutions.