Co‐Estimation of State of Health and State of Charge for Lithium‐Ion Batteries via the Normalized State of Charge and Open Circuit Voltage Relationship

ABSTRACT The relationship between state of charge (SoC) and open circuit voltage (OCV) is fundamental to SoC estimation in equivalent circuit models (ECMs). While its dependency on temperature and aging is recognized, the influence of real‐time capacity variations is often underexplored. This study investigates the impact of capacity degradation on the SoC–OCV relationship across different temperatures, aging levels, and OCV testing methods, using the CALCE and NASA battery datasets. Results show that when SoC is normalized by the degraded capacity, the SoC–OCV relationship remains nearly constant for SoC values above 20%. Leveraging this property, we propose a real‐time algorithm capable of simultaneously estimating SoC and capacity throughout the battery lifecycle. The algorithm also estimates state of health (SoH) by independently quantifying resistance and capacity related degradation. A first‐order ECM with a single resistor‐capacitor branch models battery dynamics, while Kalman filtering enables real‐time state updates. The method is validated under diverse conditions including partial and full discharges, varying temperatures, dynamic load profiles (e.g., US06, FUDS, BJDST, HPPC), and different aging states. Experimental results demonstrate robust performance, with SoC estimation errors within ±0.01 and capacity estimation errors within ±0.05 Ah, confirming the algorithm's effectiveness for real‐world battery management system applications.

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Publication Details

Journal
Energy Storage
Published
2025-09-16
DOI
https://doi.org/10.1002/est2.70270
Citations
1
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.68
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article

Co‐Estimation of State of Health and State of Charge for Lithium‐Ion Batteries via the Normalized State of Charge and Open Circuit Voltage Relationship

Onur Kadem
1 citations
Energy Storage
Advanced Battery Technologies Research
0.68
article

Co‐Estimation of State of Health and State of Charge for Lithium‐Ion Batteries via the Normalized State of Charge and Open Circuit Voltage Relationship

Onur Kadem
article en
1 citations

Abstract

ABSTRACT The relationship between state of charge (SoC) and open circuit voltage (OCV) is fundamental to SoC estimation in equivalent circuit models (ECMs). While its dependency on temperature and aging is recognized, the influence of real‐time capacity variations is often underexplored. This study investigates the impact of capacity degradation on the SoC–OCV relationship across different temperatures, aging levels, and OCV testing methods, using the CALCE and NASA battery datasets. Results show that when SoC is normalized by the degraded capacity, the SoC–OCV relationship remains nearly constant for SoC values above 20%. Leveraging this property, we propose a real‐time algorithm capable of simultaneously estimating SoC and capacity throughout the battery lifecycle. The algorithm also estimates state of health (SoH) by independently quantifying resistance and capacity related degradation. A first‐order ECM with a single resistor‐capacitor branch models battery dynamics, while Kalman filtering enables real‐time state updates. The method is validated under diverse conditions including partial and full discharges, varying temperatures, dynamic load profiles (e.g., US06, FUDS, BJDST, HPPC), and different aging states. Experimental results demonstrate robust performance, with SoC estimation errors within ±0.01 and capacity estimation errors within ±0.05 Ah, confirming the algorithm's effectiveness for real‐world battery management system applications.

Energy StorageVol. 7(7)
Bursa Technical University (TR)
Openalex Percentile: Top 25%
Advanced Battery Technologies Research
0.68
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Co‐Estimation of State of Health and State of Charge for Lithium‐Ion Batteries via the Normalized State of Charge and Open Circuit Voltage Relationship — Onur Kadem · Energy Storage (2025) | TGRS Research Map | TGRS