A Lightweight Deep Learning Approach for Lithium-ion Battery RUL Estimation

Lithium-ion batteries represent a pivotal component within contemporary energy storage solutions, exhibiting a diverse range of applications spanning from consumer electronics to electric vehicles and renewable energy systems. Nevertheless, the progressive degradation of these batteries, resulting in a reduction in capacity and performance, poses significant challenges in terms of system safety and reliability. In this context, the evaluation of the Remaining Useful Life (RUL) plays a central role in assessing the health of lithium-ion batteries. Ensuring precise and reliable RUL prediction is critical for the proper operation of a system. In this work, to address these challenges, a novel lightweight deep learning approach has been proposed for battery RUL estimation, by using voltage and current data. The proposed model is an approach based on the Echo State Networks (ESNs), which is compared to conventional deep learning models, such as Long Short-Term Memory (LSTM) networks, which re-quire more complex architectures and substantial computational resources. The ESN-based model demonstrates a comparable predictive capacity, while substantially reducing training and inference times. The model was tested with the CALCE dataset, focused on data obtained during charge and discharge cycles of lithium-ions batteries. Specifically, under the test conditions of 1 C discharge, the ESN requires only 0.3 seconds for training and approximately 0.06 seconds for inference, thus offering a computational advantage over the LSTM model, which requires 384 seconds for training and approximately 0.19 seconds for inference with the same hardware.