Abstract
LIB (Lithium Ion Battery) has been widely utilized in various fields due to its high energy density and long usage cycle, and is particularly actively applied as a power source for unmanned vehicles. LIBs deteriorate with repeated charge/discharge cycles, reducing usable capacity, and the LIB’s power performance is determined by the available capacity. Therefore, in order to smoothly supply power to unmanned vehicles, LIBs with guaranteed available capacity must be used. In this paper, we propose a learning-based LIB status diagnosis system to effectively determine the available capacity of LIB. The proposed system is constructed using a convolutional neural network-based classification model, extracts and synthesizes features from diagnostic data, and outputs LIB status diagnosis results. The state of the LIB is defined as three states depending on the operating conditions of the vehicle and the available capacity of the LIB, and diagnostic data is generated based on the time-series discharge data to effectively reflect the deterioration characteristics of the LIB. To verify the performance of the proposed state diagnosis system, model training and verification are conducted using random discharge data and confusion matrix, and the results are analyzed.
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