RT Journal Article
T1 Feature-based lithium-ion battery state of health estimation with artificialneural networks.
A1 Driscoll, Lewis
A1 De-la-Torre-Fazio, Sebastián Bienvenido
A1 Gómez-Ruiz, José Antonio
K1 Aprendizaje automático (Inteligencia artificial)
AB Precise online lithium-ion battery state of health estimation is critical for the correct operation and management of battery-based energy storage systems such as microgrids and electric vehicles. However, in such applications it is necessary to maintain standard operation and therefore difficult to experimentally determine. Advancements in machine learning techniques and capabilities allow for precise and efficient data-driven predictions. In this paper we propose a simple, yet effective state of health estimation model based on the extraction of features observed from patterns in the voltage, current and temperature profiles during the charging process, which then through artificial neural networks allow for per cycle estimations. We then apply this model to two groups of batteries from the NASA Ames PCoE Battery data set. Results show that the proposed model is capable of estimating the state of health of batteries discharged under varied conditions with resulting coefficients of determination between 0.896 and 0.992 while also employing significantly less input data than other works.
PB Elsevier
YR 2022
FD 2022-06
LK https://hdl.handle.net/10630/24469
UL https://hdl.handle.net/10630/24469
LA eng
NO Lewis Driscoll, Sebastián de la Torre, Jose Antonio Gomez-Ruiz, Feature-based lithium-ion battery state of health estimation with artificial neural networks, Journal of Energy Storage, Volume 50, 2022, 104584, ISSN 2352-152X, https://doi.org/10.1016/j.est.2022.104584
NO This work was partially supported by the Junta de Andalucia, Spain under project UMA18-FEDERJA-150 and Gobierno de España, Spain under project RTI2018-093421-B-100. Funding for open access charge: Universidad de Málaga–CBUA .
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 27 feb 2026