Year: 2025
Type: article
Abstract: Accurate estimation of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing the reliability and efficiency of energy storage systems. This study explores custom deep learning models to predict RUL using a dataset from the Hawaii Natural Energy Institute (HNEI). Three approaches are investigated: an Encoder-only Transformer model, its enhancement with SimSiam transfer learning, and a CNN–Encoder hybrid model. These models leverage advanced mechanisms such as multi-head attention, robust feedforward networks, and self-supervised learning to capture complex degradation patterns in the data. Rigorous preprocessing and optimisation ensure optimal performance, reducing key metrics such as mean squared error (MSE) and mean absolute error (MAE). Experimental results demonstrated that Transformer–CNN with Noise Augmentation outperforms other methods, highlighting its potential for battery health monitoring and predictive maintenance.
Source: Information
DOI: 10.3390/info16100878
Authors: Emek Utku Capoglu, Aboozar Taherkhani
Institution: De Montfort University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4415047308