With the rapid expansion of electric vehicles (EVs) and energy storage systems (ESS), ensuring the operational safety of lithium-ion batteries has become a critical technical challenge. Conventional battery management systems (BMS) primarily rely on threshold-based rule logic, which is limited in detecting coupled anomalies and early-stage degradation patterns. In this study, a deep learning-based framework for multivariate anomaly detection is proposed using BMS sensor data, including voltage, current, temperature, state of charge (SOC), and state of health (SOH). Five representative fault scenarios were defined, including thermal runaway precursors, cell voltage imbalance, SOC inconsistency, internal resistance increase, and communication delay. The proposed CNN-LSTM model was compared with conventional Rule-based methods and machine learning models, including Isolation Forest, Autoencoder, and LSTM. Experimental results show that the proposed model achieved the highest performance, with an F1-score of 0.885, an AUC of 0.94, and a detection delay of 8.1 s. In contrast, the Rule-based method exhibited a significantly higher false negative rate of 42.0%, indicating limitations in detecting complex anomaly patterns. These results demonstrate that the proposed spatiotemporal deep learning approach can significantly improve the accuracy and responsiveness of battery anomaly detection. Furthermore, the proposed method is expected to contribute to enhancing safety, reliability, and predictive diagnostics in next-generation intelligent BMS platforms.

Cu₂O metal oxide was synthesized using NaBH₄ as a reducing agent in this study. The transformation of Cu composite with the pH adjustment was investigated, and the conditions for Cu₂O synthesis were analyzed. As pH of the solution was changed, the synthesized Cu composite evolved into Cu/Cu₂O and Cu/Cu₂O/CuO composites. The Cu₂O composite synthesized under conditions closest to pure Cu₂O was heat-treated at 200℃. The remaining minor Cu metal was oxidized, resulting in pure Cu₂O particles with enhanced crystallinity. The synthesized Cu₂O exhibited various morphology with particle sizes of about 160~720 nm, and the shape and size of the Cu₂O particles remained significantly unchanged after heat treatment. Surface analysis was conducted to compare the changes before and after heat treatment. No significant changes were observed, except for those attributed to water evaporation. The Cu₂O synthesized via this simple chemical reduction method can be utilized in various application fields, including catalysts, optical devices, and sensors.