Lithium-ion batteries are emerging as a crucial technology in the world’s clean energy transition. These batteries face challenges as they degrade with use due to unwanted chemical side reactions. In this thesis, we propose two methods of using relatively accessible battery data to predict important health metrics. These health metrics improve battery safety, control, and decision-making.

In the first method, we leverage battery charging times to decipher measures of internal chemical degradation. Using machine learning, different modes of degradation can be attributed to segments of the constant current and constant voltage charging curves. This model is trained and tested using cells cycled under varying depths of discharge and C-rate conditions inducing an array of degradation pathways. We can gather insights into the model’s learning through input feature analysis to determine key areas within the charging regime.

At the end of the battery’s first life, we can analyze its degradation modes to determine its viability in second-life applications. This is conducted by using features extracted from electrochemical impedance spectroscopy as input data to a binary classifier. This determines whether a battery should be reused or recycled. The distinction is made based on a metric that includes the current state of health of the battery, and the slope of capacity degradation to the end of second life.

These contributions look to quantify variance and non-linearity in Lithium-ion battery degradation to inform economic and safety-based decision-making. These contributions also address challenges in data-driven battery modelling regarding model explainability and data scarcity.