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Estimation of Lithium-Ion Battery State of Health in Electric Vehicles Using an Ensemble Model Based on Semi-Supervised Learning
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Yaping Qin1, Liuyang Ning2
Automobile Technology | 2026, (5) : 48 - 55
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Automobile Technology | 2026, (5): 48-55
• Special Topic on Full-Stack Environment Perception Technology for Intelligent Vehicles •
Estimation of Lithium-Ion Battery State of Health in Electric Vehicles Using an Ensemble Model Based on Semi-Supervised Learning
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Yaping Qin1, Liuyang Ning2
Affiliations
  • 1.School of Intelligent Manufacturing, Kaifeng University, Kaifeng 475000
  • 2.College of Electromechanical Engineering, Hunan Institute of Traffic Engineering, Hengyang 421001
Published: 2026-05-24 doi: 10.19620/j.cnki.1000-3703.20250545
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To address the issue that existing supervised learning methods for estimating the battery State of Health (SOH) rely on scarce labeled data while neglecting the abundance of easily obtainable unlabeled data, a joint SOH estimation method based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) within a Semi-Supervised Learning (SSL) framework is proposed. First, three interpretable health indicators are extracted from the charging voltage to accurately describe battery degradation. Then, based on SSL, pseudo-labels for unlabeled data are predicted to augment the training samples, thereby enhancing the generalization capability of the model. Analysis and experiments demonstrate that the proposed SSL-based method significantly improves SOH estimation performance, with an average Root Mean Square Error (RMSE) below 1.2%.

State of health  /  Explainable health indicators  /  Semi-Supervised learning  /  Integrated model
秦亚萍, 宁留洋. 基于半监督学习的集成模型估计电动汽车锂电池健康状态. 汽车技术, 2026 , (5) : 48 -55 . DOI: 10.19620/j.cnki.1000-3703.20250545
Yaping Qin, Liuyang Ning. Estimation of Lithium-Ion Battery State of Health in Electric Vehicles Using an Ensemble Model Based on Semi-Supervised Learning[J]. Automobile Technology, 2026 , (5) : 48 -55 . DOI: 10.19620/j.cnki.1000-3703.20250545
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Year 2026 volume Issue 5
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doi: 10.19620/j.cnki.1000-3703.20250545
  • Online Date：2026-09-23
  • Published：2026-05-24
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  • Revised：2025-08-31
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Affiliations
    1.School of Intelligent Manufacturing, Kaifeng University, Kaifeng 475000
    2.College of Electromechanical Engineering, Hunan Institute of Traffic Engineering, Hengyang 421001
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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