<![CDATA[Over the past few decades, the demand for environmentally friendly energy has led to an increase in the use of energy storage technologies such as batteries. One type of battery that is widely used is the lithium-ion battery because it has high durability, high energy density, and is lightweight. However, this battery is sensitive to extreme conditions such as high temperatures and excessive charging or discharging, which can affect battery health. This study aims to determine the health condition of lithium-ion batteries based on temperature characteristics from thermal images, as well as to evaluate the accuracy of a fuzzy logic system in predicting battery health status. The fuzzy logic system is used because it can handle uncertainty within varying temperature data ranges. The data used consists of 20 battery samples categorized into three groups: Healthy, Warning, and Unhealthy. The input parameters include the battery's operating temperature and the difference between the battery temperature and the ambient temperature. Evaluation was conducted using confusion matrices such as accuracy, precision, recall, and F1-score. The analysis results show that the fuzzy model has an accuracy of 84% and a precision rate of 84% for the Healthy category, 75% for the Warning category, and 93.75% for the Unhealthy category, as well as a recall evaluation of 91.30% for the Healthy category, 54.55% for the Warning category, and 93.75% for the Unhealthy category. These findings indicate that the fuzzy method is quite effective in monitoring battery health through temperature analysis.]]>
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