State of Health Estimations for Lithium-Ion Batteries Based on MSCNN
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- Deng, Zhongwei & Hu, Xiaosong & Lin, Xianke & Che, Yunhong & Xu, Le & Guo, Wenchao, 2020. "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, Elsevier, vol. 205(C).
- S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
- Yang, Yixin, 2021. "A machine-learning prediction method of lithium-ion battery life based on charge process for different applications," Applied Energy, Elsevier, vol. 292(C).
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Cited by:
- Cai, Hongchang & Tang, Xiaopeng & Lai, Xin & Wang, Yanan & Han, Xuebing & Ouyang, Minggao & Zheng, Yuejiu, 2024. "How battery capacities are correctly estimated considering latent short-circuit faults," Applied Energy, Elsevier, vol. 375(C).
- Bing Chen & Yongjun Zhang & Jinsong Wu & Hongyuan Yuan & Fang Guo, 2025. "Lithium-Ion Battery State of Health Estimation Based on Feature Reconstruction and Transformer-GRU Parallel Architecture," Energies, MDPI, vol. 18(5), pages 1-19, March.
- Liang Zhang & Qizhi Wu & Longfei Wang & Ling Lyu & Linru Jiang & Yu Shi, 2025. "Research on GRU-LSTM-Based Early Warning Method for Electric Vehicle Lithium-Ion Battery Voltage Fault Classification," Energies, MDPI, vol. 18(6), pages 1-25, March.
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Keywords
lithium-ion battery; state of health; health indicator; MSCNN;All these keywords.
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