Yunhong Che
Yunhong Che
Aalborg University \ Stanford University \ EPFL \ Chongqing University
Verified email at - Homepage
Cited by
Cited by
Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression
Z Deng, X Hu, X Lin, Y Che, L Xu, W Guo
Energy 205, 118000, 2020
Battery health prediction using fusion-based feature selection and machine learning
X Hu, Y Che, X Lin, S Onori
IEEE Transactions on Transportation Electrification 7 (2), 382-398, 2020
General discharge voltage information enabled health evaluation for lithium-ion batteries
Z Deng, X Hu, X Lin, L Xu, Y Che, L Hu
IEEE/ASME Transactions on Mechatronics 26 (3), 1295-1306, 2020
Predictive Battery Health Management with Transfer Learning and Online Model Correction
Y Che, Z Deng, X Lin, L Hu, X Hu
IEEE Transactions on Vehicular Technology, 2021
Health prognosis for electric vehicle battery packs: A data-driven approach
X Hu, Y Che, X Lin, Z Deng
IEEE/ASME transactions on mechatronics 25 (6), 2622-2632, 2020
Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering
L Hu, X Hu, Y Che, F Feng, X Lin, Z Zhang
Applied Energy 262, 114569, 2020
State of health prognostics for series battery packs: A universal deep learning method
Y Che, Z Deng, P Li, X Tang, K Khosravinia, X Lin, X Hu
Energy 238, 121857, 2022
Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects
Y Che, X Hu, X Lin, J Guo, R Teodorescu
Energy & Environmental Science, 2023
Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network
Y Che, Y Zheng, Y Wu, X Sui, P Bharadwaj, DI Stroe, Y Yang, X Hu, ...
Applied Energy 323, 119663, 2022
Joint Estimation of Inconsistency and State of Health for Series Battery Packs
Y Che, A Foley, M El-Gindy, X Lin, X Hu, M Pecht
Automotive Innovation, 1-14, 2021
A practical and comprehensive evaluation method for series-connected battery pack models
F Feng, X Hu, K Liu, Y Che, X Lin, G Jin, B Liu
IEEE Transactions on Transportation Electrification 6 (2), 391-416, 2020
Lifetime and aging degradation prognostics for lithium-ion battery packs based on a cell to pack method
Y Che, Z Deng, X Tang, X Lin, X Nie, X Hu
Chinese Journal of Mechanical Engineering 35, 1-16, 2022
Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects
K Liu, Q Peng, Y Che, Y Zheng, K Li, R Teodorescu, D Widanage, A Barai
Advances in Applied Energy 9, 100117, 2023
Spatial–temporal data-driven full driving cycle prediction for optimal energy management of battery/supercapacitor electric vehicles
Y Wu, Z Huang, Y Zheng, Y Liu, H Li, Y Che, J Peng, R Teodorescu
Energy Conversion and Management 277, 116619, 2023
Semi-supervised self-learning-based lifetime prediction for batteries
Y Che, DI Stroe, X Hu, R Teodorescu
IEEE Transactions on Industrial Informatics 19 (5), 6471 - 6481, 2022
Battery impedance spectrum prediction from partial charging voltage curve by machine learning
J Guo, Y Che, K Pedersen, DI Stroe
Journal of Energy Chemistry 79, 211-221, 2023
Battery health prognostic with sensor-free differential temperature voltammetry reconstruction and capacity estimation based on multi-domain adaptation
Y Che, SB Vilsen, J Meng, X Sui, R Teodorescu
Etransportation 17, 100245, 2023
Boosting battery state of health estimation based on self-supervised learning
Y Che, Y Zheng, X Sui, R Teodorescu
Journal of Energy Chemistry, 2023
Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities
Y Zheng, Y Che, X Hu, X Sui, DI Stroe, R Teodorescu
Progress in Energy and Combustion Science 100, 101120, 2024
Sensorless temperature monitoring of lithium-ion batteries by integrating physics with machine learning
Y Zheng, Y Che, X Hu, X Sui, R Teodorescu
IEEE Transactions on Transportation Electrification, 2023
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