Implementation of Battery Degradation on LithiumIon Batteries using PYNQ-FPGA

Predicting the remaining usable life (RUL) of a lithium-ion battery properly is vital for appropriate maintenance and overall health evaluation, which is particularly pertinent in the burgeoning electric vehicle industry, where optimising battery performance is essential. Determining the rate of battery deterioration is a complex task because of the wide variety of internal and external elements that could affect it. Our study addresses this challenge by using datasets on battery ageing sourced from NASA's Prognostic Center of Excellence (PCoE) to introduce a data-driven approach for State of Health (SOH) estimation. In our pursuit of RUL prediction, we have devised a machine-learning model employing the ADAM optimiser for optimisation. Consequently, our proposed model utilises software programming on PYNQ FPGA to discern battery degradation. The findings of these innovative approaches are thoroughly analysed and assessed, showcasing the effectiveness of our approach in navigating the complexities associated with predicting battery RUL.