Lithium-ion battery life prediction using gated convolutional neural network with convolutional block attention module

Abstract A Green World Campaign using batteries focuses on powering environmentally friendly initiatives and promoting sustainable practices. As a result, demand for lithium-ion batteries (LIB) has continued to increase over the last decade. The battery is used ubiquitously in daily life, including consumer electronics, portable devices, and electric vehicles. However, they have safety and performance concerns related to their end-of-life (EOL). Once the battery has reached its EOL, its capacity is prone to leaking, causing the battery cells to lose their capacity more rapidly. As a consequence, they need to be charged more often. Moreover, the leaks could cause short circuits, leading to serious accidents, such as fires and even explosions. This condition can be prevented by forecasting the remaining useful life of the battery. An accurate battery life prediction system monitors the remaining life of LIBs and provides an early warning when the battery is nearing its EOL. In this paper, we propose a system that utilizes a Gated Convolutional Neural Network (GCNN) with a Convolutional Block Attention Module (CBAM) to predict the remaining useful life of LIBs. We evaluated the method on two datasets: NASA and CALCE. The experimental results show that our proposed method enhances prediction accuracy by reducing the prediction error by 30.51% for RMSE and 37.38% for MAPE on the NASE dataset. Conversely, the reduction in prediction error on the CALCE datasets was 30.31% for RMSE and 38.43% for MAPE.