SOH Estimation Model Based on an Ensemble Hierarchical Extreme Learning Machine DOI Open Access
Yu He, Norasage Pattanadech,

Kasian Sukemoke

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(9), P. 1832 - 1832

Published: April 29, 2025

This paper addresses the challenges of accurately estimating state health (SOH) retired batteries, where factors such as limited historical data, non-linear degradation, and unstable parameters complicate process. We propose a novel SOH estimation model based on an Integrated Hierarchical Extreme Learning Machine (I-HELM). The minimizes reliance data reduces computational complexity by introducing indicators derived from constant charging time current area. hierarchical structure (HELM) effectively captures relationship between battery capacity, improving accuracy learning efficiency. Additionally, integrating multiple HELM models enhances stability robustness results, making approach more reliable across varying operational conditions. proposed is validated experimental datasets collected two Samsung packs, four single cells, Panasonic batteries under both constant-current dynamic Experimental results demonstrate superior performance model: maximum error for cells packs does not exceed 2.2% 2.6%, respectively, with root mean square errors (RMSEs) below 1%. For remains 3%.

Language: Английский

Analysis of Flow-Electrode Capacitive Deionization: Performance Assessment of Voltage-Driven, Current-Driven, and Hybrid Control Strategies DOI

Amin Navapour,

Ardalan Ganjizade, Seyed Nezameddin Ashrafizadeh

et al.

Electrochimica Acta, Journal Year: 2025, Volume and Issue: unknown, P. 145907 - 145907

Published: Feb. 1, 2025

Language: Английский

Citations

0
A multiple aging factor interactive learning framework for lithium-ion battery state-of-health estimation DOI
Zhengyi Bao, Tingting Luo, Mingyu Gao

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 148, P. 110388 - 110388

Published: March 9, 2025

Language: Английский

Citations

0
Capacity and State-of-Health Prediction of Lithium-Ion Batteries Using Reduced Equivalent Circuit Models DOI Creative Commons
Hakeem Thomas, Mark H. Weatherspoon

Batteries, Journal Year: 2025, Volume and Issue: 11(4), P. 162 - 162

Published: April 19, 2025

Knowledge of battery health and its degradation has been a research focus since it enables users to use batteries optimally. The dynamic electrochemical properties within cell can be represented by an equivalent circuit observe the impedance over range frequencies, which is indicator cell’s buildup from electrical framework. This process provides information on different processes observed at frequency ranges, used optimally predict capacity fade cell. With increasing demand for batteries, faster less computationally intensive means are being explored batteries. proposed method in this article introduces effective reduced model (ER-ECM) prognosis studies. ER-ECM measures parameters spectra high- mid-frequency regions data input. These then accurately state health. results show that overarching charge transfer resistance most salient predictions, having average error 1.4%, 40% reduction compared using all ER-ECM. ECMs study also provide training testing 6% global spectra.

Language: Английский

Citations

0
SOH Estimation Model Based on an Ensemble Hierarchical Extreme Learning Machine DOI Open Access
Yu He, Norasage Pattanadech,

Kasian Sukemoke

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(9), P. 1832 - 1832

Published: April 29, 2025

This paper addresses the challenges of accurately estimating state health (SOH) retired batteries, where factors such as limited historical data, non-linear degradation, and unstable parameters complicate process. We propose a novel SOH estimation model based on an Integrated Hierarchical Extreme Learning Machine (I-HELM). The minimizes reliance data reduces computational complexity by introducing indicators derived from constant charging time current area. hierarchical structure (HELM) effectively captures relationship between battery capacity, improving accuracy learning efficiency. Additionally, integrating multiple HELM models enhances stability robustness results, making approach more reliable across varying operational conditions. proposed is validated experimental datasets collected two Samsung packs, four single cells, Panasonic batteries under both constant-current dynamic Experimental results demonstrate superior performance model: maximum error for cells packs does not exceed 2.2% 2.6%, respectively, with root mean square errors (RMSEs) below 1%. For remains 3%.

Language: Английский

Citations

0