| Title |
Development of Battery Management System with PCM using Neural Network Based Aging Algorithm for Electric Vehicle |
| Authors |
(Seong Jun Yoon) ; (Teresssa Talluri) ; (Amarnathvarma Angani) ; (Hee Tae Chung) ; (Kyoo Jae Shin) |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.2.280 |
| Keywords |
Battery thermal management; Electric vehicle; Phase change material; Long Short Memory Model; Random Forest method; Prediction of battery temperature |
| Abstract |
The increase in battery temperatures results in the critical risks, including explosions, therefore need of efficient thermal management is increasing. In this point of view, we proposed a novel approach to battery thermal control, employing hot soaking and cold soaking experiments for the first time to identify phase change materials (PCMs) that enhance battery safety under temperature conditions. Machine learning methods such as Llng short-term memory (LSTM) and random forest (RF) models were applied and thermal performance was investigated in lithium polymer pouch batteries integrated with PCMs for fast and accurate prediction. Experiments were conducted at normal temperature of 25?C, hot temperature of 50?C, and cold temperature of ?10?C. Thermal performance metrics such as maximum temperature and thermal gradient ?T were measured during discharge of the battery. In this study we selected PCMs such as RT15, RT31, EG5, EG26, and EG28 to evaluate the performance with LSTM and RF are applied to predict temperature variations influencing thermal behavior. Results indicated that EG26 and EG28 PCMs, significantly improved thermal performance under extreme conditions. The LSTM model demonstrated high predictive accuracy of 99% compared to RF model with 97%. This integrated model approach provided both high predictive accuracy and valuable insights into battery thermal performance, underscoring the importance of PCM selection to ensure battery longevity and stability across diverse environments. |