Considering Calendar-Aging High-Precision State of Health Estimation and Active Management Technology for Energy-Storage Batteries
4 affiliations
- 1Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China
- 2Department of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
- 3State Grid Henan Electric Power Co., Ltd., Linying Power Supply Company, Luohe 462000, China
- 4College of Mechanical and Electrical Engineering, Jilin University of Chemical Technology, Jilin 132022, China
Abstract
As a core component of the new power system, energy-storage batteries undergo capacity degradation, increasing internal resistance and declining state of health (SOH) as a result of calendar aging. These changes compromise the operational safety and life-cycle economic performance of energy-storage stations. To address the need for calendar-aging risk management in stationary batteries and accurate SOH estimation from cycling data, this study develops two complementary components: a field priority-charging strategy for stationary cells and a data-driven SOH prediction model evaluated on public cycling datasets. The strategy defines criteria for low-energy stationary batteries and establishes weighted priority-charging rules for risk-cell locking and power replenishment. We construct an SOH prediction model that integrates interactive convolution and adaptive spectral modules, and we use the Dream Optimization Algorithm to optimize its hyperparameters and address multi-scale feature entanglement and limited prediction accuracy. The results show that the strategy replenishes SOC and increases cell voltage in stationary batteries, and that the risk score is significantly correlated with SOH. On the CALCE and Oxford cycling datasets, the model yields lower errors than the evaluated baselines, although the reduction depends on the battery and comparator. Ablation tests confirm the superiority of each module. This study provides a field risk-management scheme for stationary batteries and a high-precision SOH prediction method validated on public cycling datasets.
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BibTeX
@article{yuan2026considering,
title = {{Considering Calendar-Aging High-Precision State of Health Estimation and Active Management Technology for Energy-Storage Batteries}},
author = {Tianqing Yuan and Bin Han and Guangtao Peng and Jingwen Fan},
journal = {Batteries},
year = {2026},
month = oct,
doi = {10.3390/batteries12100432},
url = {https://www.mdpi.com/2313-0105/12/10/432},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Considering Calendar-Aging High-Precision State of Health Estimation and Active Management Technology for Energy-Storage Batteries
AU - Tianqing Yuan
AU - Bin Han
AU - Guangtao Peng
AU - Jingwen Fan
PY - 2026
DA - 2026/10/16
JO - Batteries
DO - 10.3390/batteries12100432
UR - https://www.mdpi.com/2313-0105/12/10/432
AB - As a core component of the new power system, energy-storage batteries undergo capacity degradation, increasing internal resistance and declining state of health (SOH) as a result of calendar aging. These changes compromise the operational safety and life-cycle economic performance of energy-storage stations. To address the need for calendar-aging risk management in stationary batteries and accurate SOH estimation from cycling data, this study develops two complementary components: a field priority-charging strategy for stationary cells and a data-driven SOH prediction model evaluated on public cycling datasets. The strategy defines criteria for low-energy stationary batteries and establishes weighted priority-charging rules for risk-cell locking and power replenishment. We construct an SOH prediction model that integrates interactive convolution and adaptive spectral modules, and we use the Dream Optimization Algorithm to optimize its hyperparameters and address multi-scale feature entanglement and limited prediction accuracy. The results show that the strategy replenishes SOC and increases cell voltage in stationary batteries, and that the risk score is significantly correlated with SOH. On the CALCE and Oxford cycling datasets, the model yields lower errors than the evaluated baselines, although the reduction depends on the battery and comparator. Ablation tests confirm the superiority of each module. This study provides a field risk-management scheme for stationary batteries and a high-precision SOH prediction method validated on public cycling datasets.
SN - 2313-0105
ER -