Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a greater emphasis on machine learning for SOH estimation. Furthermore, data preprocessing has received much attention because it is an important step in determining the efficiency of machine learning methods. In this paper, we propose a new preprocessing method for improving the efficiency of machine learning for SOH estimation. The proposed method consists of the relative state of charge (SOC) and data processing, which transforms time-domain data into SOC-domain data. According to the correlation analysis, SOC-domain data are more correlated with the usable capacity than time-domain data. Furthermore, we compare the estimation results of SOC-based data and time-based data in feedforward neural networks (FNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM). The results show that the SOC-based preprocessing outperforms conventional time-domain data-based techniques. Furthermore, the accuracy of the simplest FNN model with the proposed method is higher than that of the CNN model and the LSTM model with a conventional method when training data are small.

This paper discusses the fabrication and characterization of electrospun nanofiber scaffolds made of polystyrene (PS). The scaffolds were characterized in terms of their basis material molecular weight, fiber diameter distribution, contact angles, contact angle hysteresis, and transmittance. We propose an aligned electrospun fiber scaffold using an alignment tool (alignment jig) for the fabrication of highly hydrophobic (θ_W > 125°) and highly transparent (T > 80.0%) films. We fabricated the alignment jig to align the electrospun fibers parallel to each other. The correlation between the water contact angles and surface roughness of the aligned electrospun fibers was investigated. We found that the water contact angle increased as the surface roughness was increased. Therefore, the hydrophobic properties of the aligned electrospun fibers were enhanced by increasing the surface roughness. With the change in the electrospinning mode to produce aligned fibers rather than randomly distributed fibers, the transmittance of the aligned electrospun fibers increased. The increase in the porous area, leading to better light transmittance in comparison to randomly distributed light scattering through the aligned electrospun fibers increased with the fibers. Through the above investigation of electrospinning parameters, we obtained the simultaneous transparent (>80%) and hydrophobic (θ_W > 140°) electrospun fiber scaffold. The aligned electrospun fibers of PS had a maximum transmittance of 91.8% at the electrospinning time of 10 s. The water contact angle (WCA) of the aligned electrospun fibers increased from 77° to 141° as the deposition time increased from 10 s to 40 s. The aligned fibers deposited at 40 s showed highly hydrophobic characteristics (θ_W > 140°).