Visualization and Interpretation of Lithium Iron Phosphate Cell Variable Characteristics by Adapting Python Data Science Packages

DataScience describes itself as an exploration of how to analyze unstructured data and draw conclusions from it. The batteries are used in a variety of applications to supply or absorb power, including vehicle electrification, stationary energy storage, and smart grid, where appropriate management systems are required to guarantee their effectiveness, safety, and sustainability. Numerous cutting-edge data science techniques have been developed in this field to manage battery operation effectively. Simple statistical methods, more complex and advanced machine learning methods, and so forth can all be used to generate insights Lithium ion cell data sheets must be interpreted in order to comprehend how the cells behave when exposed to different temperatures. It is aimed to visualize and identify correlations between voltage, current, and temperature of LFP data as it is used in Electric Vehicles. The present investigation will examine and emphasize the correlations between the output voltage and current of lithium iron phosphate cells under different conditions. From the Analysis, it is found that a weak negative correlation between voltage and temperature, meaning that a higher voltage corresponds to a slight decrease in temperature, but the effect may not be very strong due to the variability in the data. The cell voltage trend indicates a peak in temperature around 4.0V, followed by a decline, reflecting a weak negative correlation between voltage and temperature that varies with cycle count segments.