Jilin Agricultural University | China
Prof. Dr. Haiwei Wu is an emerging multidisciplinary researcher whose contributions span energy systems, machine learning, spectroscopy, and intelligent diagnostics. His recent research focuses on advanced computational methods applied to energy storage and electric vehicle systems, including the development of an attention-based multi-feature fusion physics-informed neural network for accurate state-of-health estimation of lithium-ion batteries and the application of queuing-theoretic models for sustainable EV charging infrastructure planning. Beyond energy research, he has contributed significantly to the use of mid-infrared spectroscopy combined with machine learning and support vector machines for the authentication and identification of biological and agricultural products, reflecting strong capabilities in analytical modeling and pattern recognition. His publications from 2022 to 2025 highlight expertise in spectral analysis, counterfeit detection, and quality assessment. In addition, he has explored applications of improved YOLOv8 for mechanical part inspection and contributed to research on task-driven cooperative inquiry learning in education. His innovative work is supported by several patents related to electric vehicle charging technologies, demonstrating a commitment to advancing practical, technology-driven solutions across sectors.
Profile : Scopus | Orcid
Featured Publications
Wu, H., Liu, J., Wang, Z., & Li, X. (2025). An attention-based multi-feature fusion physics-informed neural network for state-of-health estimation of lithium-ion batteries. Energies.
Wang, Z., Zou, J., Tu, J., Li, X., Liu, J., & Wu, H. (2025). Towards sustainable EV infrastructure: Site selection and capacity planning with charger type differentiation and queuing-theoretic modeling. World Electric Vehicle Journal.
He, T., Kaimin, W., & Wu, H. (2025). Research on the construction and implementation of a task-driven cooperative inquiry learning model for postgraduate students majoring in music education. Chinese Music Education, (05), 47–53.
Yang, C.-E., Wu, H., Yuan, Y., et al. (2025). Efficient recognition of plum blossom antler hats and red deer antler hats based on support vector machine and mid-infrared spectroscopy. Journal of Jilin Agricultural University, 1–7.
Yang, C.-E., Su, L., Feng, W.-Z., Zhou, J.-Y., Wu, H.-W., Yuan, Y.-M., & Wang, Q. (2023). Identification of Pleurotus ostreatus from different producing areas based on mid-infrared spectroscopy and machine learning. Spectroscopy and Spectral Analysis.
Yang, C.-E., Su, L., Feng, W., et al. (2023). Identification of Pleurotus ostreatus from different origins by mid-infrared spectroscopy combined with machine learning. Spectroscopy and Spectral Analysis, 43(02), 577–582.
Yang, C.-E., Wu, H.-W., Yang, Y., Su, L., Yuan, Y.-M., Liu, H., Zhang, A.-W., & Song, Z.-Y. (2022). A model for the identification of counterfeited and adulterated Sika deer antler cap powder based on mid-infrared spectroscopy and support vector machines. Spectroscopy and Spectral Analysis.
Yang, C.-E., Wu, H., Yang, Y., et al. (2022). Identification model of counterfeiting and adulteration of plum blossom antler cap powder based on mid-infrared spectroscopy and support vector machine. Spectroscopy and Spectral Analysis, 42(08), 2359–2365.