Health-Conscious vehicle battery state estimation based on deep transfer learning
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DOI: 10.1016/j.apenergy.2022.119120
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Cited by:
- Fang Guo & Guangshan Huang & Wencan Zhang & An Wen & Taotao Li & Hancheng He & Haolin Huang & Shanshan Zhu, 2023. "Lithium Battery State-of-Health Estimation Based on Sample Data Generation and Temporal Convolutional Neural Network," Energies, MDPI, vol. 16(24), pages 1-15, December.
- Takyi-Aninakwa, Paul & Wang, Shunli & Liu, Guangchen & Bage, Alhamdu Nuhu & Bobobee, Etse Dablu & Appiah, Emmanuel & Huang, Qi, 2024. "Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data," Applied Energy, Elsevier, vol. 363(C).
- Zhao, Guangcai & Kang, Yongzhe & Huang, Peng & Duan, Bin & Zhang, Chenghui, 2023. "Battery health prognostic using efficient and robust aging trajectory matching with ensemble deep transfer learning," Energy, Elsevier, vol. 282(C).
- Angel Recalde & Ricardo Cajo & Washington Velasquez & Manuel S. Alvarez-Alvarado, 2024. "Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 17(13), pages 1-39, June.
- Solmaz Nazaralizadeh & Paramarshi Banerjee & Anurag K. Srivastava & Parviz Famouri, 2024. "Battery Energy Storage Systems: A Review of Energy Management Systems and Health Metrics," Energies, MDPI, vol. 17(5), pages 1-21, March.
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Keywords
Transportation electrification; Electric vehicles; Battery energy storage; Deep transfer learning; Battery management system; Battery state estimation;All these keywords.
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