2021-07-08December 22020-12December 2https://hdl.handle.net/10657/7842Lithium-Ion battery prognostic and health prediction is an essential part of our modern world today. Reliable predictions of the current state and remaining useful life are critical to a wide range of industrial applications’ safe operations. There have been existing and widely studied battery state prediction models, the equivalent circuit, and physics-based models. The emergence and advancements of machine learning algorithms, such as support vector machine (SVM), have led to a new era of data-driven models. In this research, we first establish a baseline prediction using traditional machine learning algorithms and then utilize advance deep learning algorithms. We analyze the battery’s degradation pattern based on the State of Health (SOH) prediction and the Remaining Useful Life (RUL), which aims to predict the degradation from a specific threshold cycle to the end of life (EOL) of the battery. One of the challenges this paper aims to solve is having a comparable baseline for several machine learning-based prognostic predictions. We solve this by performing several experiments on the same data set using traditional algorithms and then perform further experiments using the neural network model. The classification accuracy agrees with several benchmarks established in the research literature. This research further proves the viability of using Long Short Term Memory (LSTM) models for RUL prediction and advancing the role of data-driven models in prognostic and health management for critical engineering applications.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Remaining Useful Life, End of Life, State of Health, State of Charge, Battery2021-07-08Thesisborn digital