Efficient lithium-ion batteries are increasingly necessary, especially with the growing demand for energy storage. To enhance their efficiency, these batteries require an accurate and reliable method for online diagnosis. This thesis explores improving online diagnosis by examining the battery voltage response of commercial cells under varied cycling conditions.

The first study investigates battery voltage relaxation, which is the gradual process of voltage equalization following the cutoff of current flow. Voltage relaxation could be pivotal in online battery state determination, however, research in this area is currently underdeveloped. This study reveals that voltage relaxation behavior is complex and is influenced by the depth of discharge, charge rate, temperature, and cell chemistry. The results provide a unique, comprehensive dataset for further research into voltage relaxation. This dataset is used to validate the efficiency of three voltage relaxation models and three voltage relaxation characterization techniques.

The second study examines the effect of temperature on battery voltage response and looks at how it can be integrated into a mechanistic model. This mechanistic model simulates the battery’s voltage response by quantifying the interactions between a cell’s positive and negative electrodes. The results of this study prove that it is possible to emulate the behavior of a cell at temperatures outside of the typical room temperature testing conditions by altering the charge/discharge rate.

Lastly, this thesis analyzes the accuracy of optimization methods for generating synthetic battery data using mechanistic modeling. Results indicate that an exhaustive search technique outperforms the optimization algorithms.

Alexa Fernando is an MS candidate in Electrical Engineering at the University of Hawaiʻi at Mānoa. She received her BS in Electrical Engineering from the University of Hawaiʻi at Mānoa in 2021. Her current research focuses on the improvement of lithium-ion battery diagnosis techniques. She is also interested in optimization and control theory.