PID-based CNN-LSTM for accuracy-boosted virtual sensor in battery thermal management system
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DOI: 10.1016/j.apenergy.2022.120424
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- Xie, Jiahang & Yang, Rufan & Hui, Shu-Yuen Ron & Nguyen, Hung D., 2024. "Dual Digital Twin: Cloud–edge collaboration with Lyapunov-based incremental learning in EV batteries," Applied Energy, Elsevier, vol. 355(C).
- Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
- Zhou, Guangzhao & Guo, Zanquan & Sun, Simin & Jin, Qingsheng, 2023. "A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction," Applied Energy, Elsevier, vol. 344(C).
- Ly, Sel & Xie, Jiahang & Wolter, Franz-Erich & Nguyen, Hung D. & Weng, Yu, 2023. "T-shape data and probabilistic remaining useful life prediction for Li-ion batteries using multiple non-crossing quantile long short-term memory," Applied Energy, Elsevier, vol. 349(C).
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Keywords
Virtual sensor; PID; Convolutional Neural Network–Long Short-Term Memory networks; Battery energy storage system; Tree-structured parzen estimator; Compressed sensing;All these keywords.
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