Machine learning-based state-of-health estimation of battery management systems using experimental and simulation data
| dc.contributor.author | Al-Rahamneh, Anas | |
| dc.contributor.author | Izco Berastegui, Irene | |
| dc.contributor.author | Serrano Hernández, Adrián | |
| dc.contributor.author | Faulín Fajardo, Javier | |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.date.accessioned | 2025-08-13T17:16:45Z | |
| dc.date.available | 2025-08-13T17:16:45Z | |
| dc.date.issued | 2025-07-11 | |
| dc.date.updated | 2025-08-13T16:59:44Z | |
| dc.description.abstract | In pursuit of zero-emission targets, increasing sustainability concerns have prompted urban centers to adopt more environmentally friendly modes of transportation, notably through the deployment of electric vehicles (EVs). A prominent manifestation of this shift is the transition from conventional fuel-powered buses to electric buses (e-buses), which, despite their environmental benefits, introduce significant operational challenges—chief among them, the management of battery systems, the most critical and complex component of e-buses. The development of efficient and reliable Battery Management Systems (BMSs) is thus central to ensuring battery longevity, operational safety, and overall vehicle performance. This study examines the potential of intelligent BMSs to improve battery health diagnostics, extend service life, and optimize system performance through the integration of simulation, real-time analytics, and advanced deep learning techniques. Particular emphasis is placed on the estimation of battery state of health (SoH), a key metric for predictive maintenance and operational planning. Two widely recognized deep learning models—Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM)—are evaluated for their efficacy in predicting SoH. These models are embedded within a unified framework that combines synthetic data generated by a physics-informed battery simulation model with empirical measurements obtained from real-world battery aging datasets. The proposed approach demonstrates a viable pathway for enhancing SoH prediction by leveraging both simulation-based data augmentation and deep learning. Experimental evaluations confirm the effectiveness of the framework in handling diverse data inputs, thereby supporting more robust and scalable battery management solutions for next-generation electric urban transportation systems. | en |
| dc.description.sponsorship | This work has been supported by the PID2022-140278NB-I00 project and RED2022-134703-T network from the Spanish Ministry of Science, Innovation, and Universities. Additionally, we acknowledge the support from the UNED Pamplona project (UNEDPAM/PI/PR24/04P). | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Al-Rahamneh, A., Izco, I., Serrano-Hernandez, A., Faulin, J. (2025) Machine learning-based state-of-health estimation of battery management systems using experimental and simulation data. Mathematics, 13(14), 1-23. https://doi.org/10.3390/math13142247. | |
| dc.identifier.doi | 10.3390/math13142247 | |
| dc.identifier.issn | 2227-7390 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/54730 | |
| dc.language.iso | eng | |
| dc.publisher | MDPI | |
| dc.relation.ispartof | Mathematics 2025, 13(14), 2247 | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-140278NB-I00/ES/ | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI//RED2022-134703-T/ | |
| dc.relation.publisherversion | https://doi.org/10.3390/math13142247 | |
| dc.rights | © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Machine learning | en |
| dc.subject | Deep learning | en |
| dc.subject | Simulation | en |
| dc.subject | Agent-based modeling | en |
| dc.subject | Battery management system | en |
| dc.subject | State of health estimation | en |
| dc.subject | Electric urban buses | en |
| dc.title | Machine learning-based state-of-health estimation of battery management systems using experimental and simulation data | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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