@article{Zhou2025, 
author = {Zeyang Zhou and Zachary James Ryan and Utkarsh Sharma and Tran Tien Anh and Shashi Mehrotra and Angelo Greco and Jason West and Mukesh Prasad},
title = {Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {83},
number = {3},
pages = {4795-4813},
keywords = {Battery capacity estimation, state of charge, deep learning, prediction efficiency, energy storage systems},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.060291},
doi = {10.32604/cmc.2025.060291},
abstract = {Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an  R2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural Networks (CNN) and Radial Basis Function Networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.}
}