논문 게재

저자 : Woongchul Choi

게재 : 2025/06/20

설명 : Global electric vehicle (EV) markets are rapidly expanding, and the efficient management of batteries has become increasingly important due to supply constraints of rare metals and other raw materials required for lithium-ion batteries. Accordingly, the reuse and recycling of used batteries from early EVs are emerging as key solutions. This study proposes a machine learning-based approach to rapidly and reliably estimate the static capacity of used batteries. While conventional methods require significant measurement time, this study demonstrates that accurate static capacity estimation is possible using only short-term partial discharge data (6 min under 1C-rate CC conditions) by leveraging an RNN (recurrent neural network) architecture specialized for time-series data processing. The proposed model achieves high prediction accuracy, with an average RMSE of 28.439 mAh, average MSE of 808.799 mAh2, average MAE of 13.049 mAh, and average R2 of 0.9993, while significantly reducing the evaluation time compared to conventional methods. This is expected to greatly enhance the efficiency and practicality of battery reuse and recycling processes. 

저자 : Geunbae Hong, Woongchul Choi

게재 : 2025/03/11

설명 : The rapid proliferation of electric vehicles has posed significant challenges regarding the disposal of end-of-life lithium-ion batteries. Effective re-use, remanufacturing, and recycling of these batteries are essential for sustainable resource management and reducing environmental impact. By enabling circular use of battery materials, these processes can substantially mitigate greenhouse gas emissions, conserve natural resources, and minimize waste generation. However, achieving these sustainable battery life cycles requires accurate and rapid health assessments. Traditional evaluation methods are often time-consuming and costly, limiting their applicability for profitable industrial operations. This study proposes a novel rapid assessment method that utilizes partial discharge data collected over a brief three-minute period to evaluate battery capacity. By applying an ensemble technique, the proposed approach … 

저자 : Younggill Son, Woongchul Choi

게재 : 2024/11/26

설명 : Rapid growth of global electric vehicle (EV) market, strongly supported not only by government authorities but also by many private sectors created the challenge of managing life cycle of Li-ion batteries mainly due to the limited resource of raw materials. One of the main ideas to surmount the issue is the reuse or recycle of the retired batteries from the initial use with EVs. In doing so, a fast and reliable methodology for the estimation of static capacity of the retired batteries are a must for the success. Traditional evaluation method, however, is quite time-consuming which takes longer than a few hours. To overcome this shortcoming, the current research showed a machine learning-based approach that can reliably estimate the static capacity of the retired batteries.