Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2025.060291
Journal Menu
Special Issues
Table of Content

All issues

Online First

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

Zeyang Zhou1,*, Zachary James Ryan1, Utkarsh Sharma2, Tran Tien Anh3, Shashi Mehrotra4, Angelo Greco5, Jason West6, Mukesh Prasad1,*
1 School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
2 School of Data Science and Engineering, Indian Institute of Science Education and Research, Bhopal, 462066, India
3 Research Institute of Marine Systems Engineering, Seoul National University, Seoul, 08826, Republic of Korea
4 Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, 201204, India
5 Battery System Modelling Department, CLARIOS VARTA Hannover GmbH, Hanover, 30419, Germany
6 Science and Innovation Group, Bureau of Meteorology, Brisbane, 4000, Australia
* Corresponding Author: Zeyang Zhou. Email: email; Mukesh Prasad. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.060291

Received 29 October 2024; Accepted 07 March 2025; Published online 11 April 2025

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 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.

Keywords

Battery capacity estimation; state of charge; deep learning; prediction efficiency; energy storage systems

  • 340

    View

  • 191

    Download

  • 0

    Like

Related articles

Share Link