[1] Li P*, Wu X, Grosu R, et al. Applying neural network to health estimation and lifetime prediction of lithium-Ion batteries [J]. IEEE Transactions on Transportation Electrification, 2025, 11(1): 4224-4248. (25 页长文 中科院 SCI 一区 TOP 期刊, IF=7.2) [2]Li P*, Zhang Z, Grosu R, et al. An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries[J]. Renewable and Sustainable Energy Reviews, 2022, 156: 111843. (ESI 前 1%高被引, 中科院 SCI 一区 TOP 期 刊,IF=15.9) [3]Li P*, Zhang Z, Xiong Q, et al. State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network[J]. Journal of power sources, 2020, 459: 228069. (ESI 前 1%高被引, 中科院 SCI 一区 TOP 期刊,IF=9.2) [4]Hou J, Su H, Li P*, et al. Bias-correction errors-in-variables Hammerstein model identification[J]. IEEE Transactions on Industrial Electronics, 2022, 70(7): 7268-7279. (ESI 前 1%高被引, 中科院 SCI 一区 TOP 期刊,IF=7.7) [5]Deng Z, Hu X, Li P*, et al. Data-driven battery state of health estimation based on random partial charging data[J]. IEEE Transactions on Power Electronics, 2021, 37(5): 5021-5031. (ESI 前 1%高被引, 中科院 SCI 一区 TOP 期刊,IF=6.7) [6]Gu X, See K W, Li P*, et al. A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model[J]. Energy, 2023, 262: 125501. (ESI 前 1%高被引, 中科院 SCI 一区 TOP 期刊,IF=9.0) [7]Hou J, Su H, Li P, et al. Consistent subspace identification of errors-in-variables Hammerstein systems[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 53(4): 2292-2303. (ESI 前 1%高被引, 中科院 SCI 一区 TOP 期刊,IF=8.7) [8]Li P*, Yang Y, Grosu R, et al. Driver distraction detection using octave-like convolutional neural network[J]. IEEE transactions on intelligent transportation systems, 2021, 23(7): 8823-8833. (中科院 SCI 一区 TOP 期刊,IF=8.5) [9]Li P, Liu J, Deng Z, et al. Increasing energy utilization of battery energy storage via active multivariable fusion-driven balancing[J]. Energy, 2022, 243: 122772. (中科院 SCI 一区 TOP 期刊,IF=9.0) [10]Wu X, Li P*, Deng Z, Liu Z, Kurboniyon M. S., et al. LDNet-RUL: Lightweight deformable neural network for remaining useful life prognostics of lithium-ion batteries[J]. IEEE Transactions on Power Electronics,2025,40(9):13514-13528 (中科院SCI一区TOP期刊, IF=7.9) [11]Li P, Ao Z, Hou J, et al.Physics-informed mamba neural network with potential knowledge for state-of-charge estimation of lithium-ion batteries[J]. Journal of Energy Storage, 2025 ,123:116546 (中科院SCI二区TOP期刊,IF=9.8) [12]Yang P, Deng W, Luo J, Li R, Li P*, et al. Preparation and structure optimization of 2D MXene nanocomposites for microwave absorbing application[J]. Materials Today Physics, 2023: 101291. (中科院 SCI 一区 TOP 期刊,IF=11.5) [13]Hou J, Liu J, Chen F, Li P*, et al. Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter[J]. Energy, 2023, 271: 126998. (中科院 SCI 一区 TOP 期刊,IF=9.0) [14]Yang Z, Li M, Lu G, Wang Y, Wei J, Hu X, Li Z, Li P*, Xu C. High‐Performance Composite Lithium Anodes Enabled by Electronic/Ionic Dual‐Conductive Paths for Solid‐State Li Metal Batteries[J]. Small, 2022, 18(31): 2202911. (中科院 SCI 一区 TOP 期刊,IF=13.3)
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