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

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

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