Yongzhi Zhang
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Paper Publications
- [1] Wu, Ze, Yongzhi Zhang, and Huizhi Wang. "Battery degradation diagnosis under normal usage without requiring regular calibration data." Journal of Power Sources 608 (2024): 234670.
- [2] Feng, Xinhong, et al. "Comprehensive performance comparison among different types of features in data-driven battery state of health estimation." Applied Energy 369 (2024): 123555.
- [3] Luo, Guoqing, et al. "A digital twin for advancing battery fast charging based on a Bayesian optimization-based method." Journal of Energy Storage 93 (2024): 112365.
- Zhao, M., Zhang, Y. and Wang, H., 2024. Battery degradation stage detection and life prediction without accessing historical operating data. Energy Storage Materials, p.103441.
- Zhao, M., Zhang, Y. and Fang, S., 2024. Improving in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging. Journal of Energy Storage, 83, p.110768.
- Zhang, Y., Zhao, M. and Xiong, R., 2024. Online data-driven battery life prediction and quick classification based on partial charging data within 10 min. Journal of Power Sources, 594, p.234007.
- Feng, X., Zhang, Y., Xiong, R. and Tang, A., 2023. Estimating battery state of health with 10-min relaxation voltage across various charging states of charge. iEnergy.
- Zhang, Y., Wang, C., Yu, Q. and Zheng, L., 2023. Battery aging-minimal speed control of autonomous heavy-duty electric trucks in adaptation to highway topography and traffic. Science China Technological Sciences, 66(10), pp.2942-2957.
- Zhang, Yongzhi, and Mingyuan Zhao. "Cloud-based in-situ battery life prediction and classification using machine learning." Energy Storage Materials (2023).
- Zhang, Y., Feng, X., Zhao, M. and Xiong, R., 2023. In-situ battery life prognostics amid mixed operation conditions using physics-driven machine learning. Journal of Power Sources, 577, p.233246.
College of Mechanical and Vehicle Engineering ChongQing University
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