Previous Article in Journal
GITT Limitations and EIS Insights into Kinetics of NMC622
Previous Article in Special Issue
Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Batteries
Volume 11
Issue 6
10.3390/batteries11060235
batteries-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks

by
Riccardo Di Dio
1,
Roberto Di Rienzo
2,
Gianluca Aurilio
1,
Davide Cavaliere
3 and
Roberto Saletti
2,*
1
Marelli Europe S.p.A., Via del Timavo 33, 40131 Bologna, Italy
2
Dipartimento di Ingegneria dell’Informazione, University of Pisa, Via Caruso 16, 56122 Pisa, Italy
3
Marelli Corporation, Kodama Plant, 540-7 Kyoei, Kodama-cho, Honjo-City 367-0206, Saitama, Japan
*
Author to whom correspondence should be addressed.
Batteries 2025, 11(6), 235; https://doi.org/10.3390/batteries11060235
Submission received: 8 May 2025 / Revised: 9 June 2025 / Accepted: 17 June 2025 / Published: 19 June 2025
(This article belongs to the Special Issue Artificial Intelligence and Batteries: AI-Powered Innovations in Battery Technology)
Browse Figure
Versions Notes

Abstract

Lithium-titanate-oxide batteries can reduce the long charging time of electric vehicles by offering fast charging capabilities. However, high charging currents require an accurate estimation of battery internal state to prevent early aging of the battery and dangerous situations. An accurate algorithm based on neural networks for the co-estimation of state of charge, internal resistance, and capacity state of health is proposed in this work. The algorithm is trained with synthetic data generated by an electric vehicle simulation platform running seven different standard driving cycles at various settings. The algorithm is then validated using an additional standard driving cycle, achieving, for state of charge, internal resistance, and capacity state of health, a root mean square error lower than 2%, 80Ω, and 2.9 %, and a mean absolute percentage error lower than 3.4%, 4.4%, and 3.3%, respectively. The results obtained and the comparison with literature works indicate that the co-estimation algorithm proposed is able to estimate the considered quantities with very good accuracy.
Keywords: lithium titanate oxide; battery management system; co-estimation algorithm; neural networks; state of charge; internal resistance; state of health lithium titanate oxide; battery management system; co-estimation algorithm; neural networks; state of charge; internal resistance; state of health
Graphical Abstract

Share and Cite

MDPI and ACS Style

Di Dio, R.; Di Rienzo, R.; Aurilio, G.; Cavaliere, D.; Saletti, R. Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks. Batteries 2025, 11, 235. https://doi.org/10.3390/batteries11060235

AMA Style

Di Dio R, Di Rienzo R, Aurilio G, Cavaliere D, Saletti R. Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks. Batteries. 2025; 11(6):235. https://doi.org/10.3390/batteries11060235

Chicago/Turabian Style

Di Dio, Riccardo, Roberto Di Rienzo, Gianluca Aurilio, Davide Cavaliere, and Roberto Saletti. 2025. "Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks" Batteries 11, no. 6: 235. https://doi.org/10.3390/batteries11060235

APA Style

Di Dio, R., Di Rienzo, R., Aurilio, G., Cavaliere, D., & Saletti, R. (2025). Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks. Batteries, 11(6), 235. https://doi.org/10.3390/batteries11060235

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop