Page 94 - EER-2-1

Explora: Environment

and Resource

ORIGINAL RESEARCH ARTICLE
Complete electrothermal and lifetime model of

18650 nickel manganese cobalt cell based on
artificial neural network

Joris Jaguemont* , Ali Darwiche , and Fanny Bardé

Department of Cell testing, Solithor, Sint-Truiden, Belgium

Abstract

This study presents a comprehensive electrothermal and lifetime model for cylindrical
3 ampere-hours (Ah) lithium-ion cells using artificial neural networks (ANNs) to
estimate the cell’s lifespan. The model combines an electrothermal component
with an ANN-based lifetime prediction approach, offering a holistic representation
of cell behavior over its lifetime by incorporating key parameters, including the
state of charge, temperature, current, and cycle life. The ANN is trained offline using
extensive experimental data collected from Sony cylindrical 3 Ah cells under various
operating conditions. The electrothermal component employs a second-order
Thévenin equivalent circuit model topology, enhanced with extended versions of
characterization and parameterization procedures. Validation of the coupled model
*Corresponding author: is performed using laboratory tests at different stages of the cells’ life (500, 1000,
Joris Jaguemont and 1500 cycles), demonstrating its ability to estimate cell electrical and thermal
(joris.jaguemont@solithor.com)
performance across a broad lifespan range. Results indicate a maximum error of
Citation: Jaguemont J, Darwiche A, 1% in voltage readings and 3% in temperature evolution during discharge with the
Bardé F. Complete electrothermal
and lifetime model of 18650 nickel complete model. This comprehensive approach not only enhances the understanding
manganese cobalt cell based on of long-term Sony 3 Ah cell dynamics but also provides a computationally efficient
artificial neural network. Explora tool for battery management systems and control strategies. The model’s capability
Environ Resour. 2025;2(1):7228.
doi: 10.36922/eer.7228 to predict both electrical and thermal performance simultaneously at different stages
of the cell’s lifetime makes it particularly valuable for optimizing battery performance
Received: December 10, 2024 and lifespan in various applications.
1st revised: January 9, 2025
2nd revised: February 11, 2025 Keywords: Lifetime modeling; Lithium; Artificial neural networks; Temperature; State of
Accepted: February 14, 2025 charge; Nickel manganese cobalt; Electrothermal modeling
Published online: February 28,
2025
Copyright: © 2024 Author(s). 1. Introduction
This is an Open-Access article
distributed under the terms of the Lithium-ion batteries (LiBs) have become essential components in modern technology,
Creative Commons Attribution
1
License, permitting distribution, powering a wide range of devices from portable electronics to electric vehicles (EVs).
and reproduction in any medium, As the demand for high-performance energy storage solutions continues to grow,
2
provided the original work is
properly cited. understanding and predicting the behavior of batteries over their lifetime have become
increasingly crucial. Modeling and simulation play a vital role in this process, serving
3-6
Publisher’s Note: AccScience 7-9
Publishing remains neutral with as essential tools for evaluating novel concepts and optimizing battery system design.
regard to jurisdictional claims in
published maps and institutional The development of accurate and efficient battery models is crucial for optimizing
affiliations. the performance and longevity of LiBs. 10-13 Traditional approaches to battery modeling
Volume 2 Issue 1 (2025) 1 doi: 10.36922/eer.7228