Project Abstract

For decades, the primary focus in battery health assessment has been on metrics such as voltage levels and current flow. However, ThermoBatt shifts the lens towards the thermal attributes, a domain less explored but equally vital. ThermoBatt encompasses two innovative models: the first, a machine learning algorithm, predicts the State of Health (SOH) and Remaining Useful Life (RUL) of batteries by analyzing factors such as ambient temperature and usage cycles. The second, a real-time temperature distribution model, utilizes temperature data within charge/discharge cycles to simulate thermal behavior. This approach necessitates several assumptions, underscoring the pioneering nature of our exploration. ThermoBatt aims to deepen our understanding of how heat generation and distribution influence battery health and longevity. By bridging this knowledge gap, our work illuminates the interconnectedness of thermodynamics with battery efficiency and endurance, paving the way for advancements in battery technology and sustainable energy solutions.

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