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Since lithium-ion batteries (LIB) has high energy density, its market range is expanding from small devices to large-sized such as electric vehicles (EVs) or energy storage systems (ESSs). So, determining and predicting the performance or life of applications using LIBs is important. Especially, increasing the energy density of LIBs has been top priority in the battery industry. Consequently, the capacity (how many electrons can be stored within the cell) is the most frequently measured electrochemical specification at different C-rates and temperatures with voltage profiles. When small electric devices are the main application of LIBs, the capacity measured at low C-rates such as 0.2C is enough to design devices, because their power consumption is low. However, with the advent of the large-sized applications, which have to run a high-power electric motor using a limited number of LIBs, power has come into the limelight, and is considered to be as important as capacity. However, it is a time-consuming process to experimentally evaluate the performance of all applications or batteries. In order to overcome the stumbling block, computational calculation is necessary. This makes it possible to predict the battery’s rate of capacity loss or remain capacity.
Therefore, I fundamentally compared and analyzed both power and capacity values of two different type of LIB cells (cylinder and pouch type) to deduce generalized power and capacity sensitivities to temperature in this work. In addition, we focused the voltage profile changes depending on state of charges (SOCs) and temperatures within the optimal temperature range (15~35 °C). Moreover, I developed the model predicting the performance (e.g. capacity retention, voltage) of the commercial battery cell to overcome considerable money and time-waste while experiment.