Learning Data Model Definition and Machine Learning Analysis for Data-Based Li-Ion Battery Performance Prediction

초록

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In orderto develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varyingthe cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance forall combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has beenactively conducted. However, since measurement experiments were conducted with the same battery in the existing published batterydata, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a trainingdata model required to develop an artificial intelligence model that can predict battery performance according to the combination ratioof cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of eachcathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacityas target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the batteryclassification model showed that each battery was classified with an error of about 2%.

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