Jinpeng Tian
Jinpeng Tian
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Zitiert von
Zitiert von
Towards a smarter battery management system: A critical review on battery state of health monitoring methods
R Xiong, L Li, J Tian
Journal of Power Sources 405, 18-29, 2018
A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries
R Xiong, J Tian, H Mu, C Wang
Applied energy 207, 372-383, 2017
Fractional-order model-based incremental capacity analysis for degradation state recognition of lithium-ion batteries
J Tian, R Xiong, Q Yu
IEEE Transactions on Industrial Electronics 66 (2), 1576-1584, 2018
A novel fractional order model for state of charge estimation in lithium ion batteries
R Xiong, J Tian, W Shen, F Sun
IEEE Transactions on Vehicular Technology 68 (5), 4130-4139, 2018
State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach
J Tian, R Xiong, W Shen, J Lu
Applied Energy 291, 116812, 2021
Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles
Z Chen, R Xiong, J Tian, X Shang, J Lu
Applied energy 184, 365-374, 2016
State-of-health estimation based on differential temperature for lithium ion batteries
J Tian, R Xiong, W Shen
IEEE Transactions on Power Electronics 35 (10), 10363-10373, 2020
Deep neural network battery charging curve prediction using 30 points collected in 10 min
J Tian, R Xiong, W Shen, J Lu, XG Yang
Joule 5 (6), 1521-1534, 2021
Electrode ageing estimation and open circuit voltage reconstruction for lithium ion batteries
J Tian, R Xiong, W Shen, F Sun
Energy Storage Materials 37, 283-295, 2021
Flexible battery state of health and state of charge estimation using partial charging data and deep learning
J Tian, R Xiong, W Shen, J Lu, F Sun
Energy Storage Materials 51, 372-381, 2022
A review on state of health estimation for lithium ion batteries in photovoltaic systems
J Tian, R Xiong, W Shen
eTransportation 2, 100028, 2019
Battery state-of-charge estimation amid dynamic usage with physics-informed deep learning
J Tian, R Xiong, J Lu, C Chen, W Shen
Energy Storage Materials 50, 718-729, 2022
Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning
J Lu, R Xiong, J Tian, C Wang, CW Hsu, NT Tsou, F Sun, J Li
Energy Storage Materials 50, 139-151, 2022
Application of digital twin in smart battery management systems
W Wang, J Wang, J Tian, J Lu, R Xiong
Chinese Journal of Mechanical Engineering 34 (1), 57, 2021
Co-estimation of state of charge and capacity for lithium-ion batteries with multi-stage model fusion method
R Xiong, J Wang, W Shen, J Tian, H Mu
Engineering 7 (10), 1469-1482, 2021
Semi-supervised estimation of capacity degradation for lithium ion batteries with electrochemical impedance spectroscopy
R Xiong, J Tian, W Shen, J Lu, F Sun
Journal of Energy Chemistry 76, 404-413, 2023
Online simultaneous identification of parameters and order of a fractional order battery model
J Tian, R Xiong, W Shen, J Wang, R Yang
Journal of Cleaner Production 247, 119147, 2020
Deep Neural Network Battery Impedance Spectra Prediction by Only Using Constant-Current Curve
Y Duan, J Tian, J Lu, C Wang, W Shen, R Xiong
Energy Storage Materials, 2021
Deep learning to estimate lithium-ion battery state of health without additional degradation experiments
J Lu, R Xiong, J Tian, C Wang, F Sun
Nature Communications 14 (1), 2760, 2023
A data-driven method for extracting aging features to accurately predict the battery health
R Xiong, Y Sun, C Wang, J Tian, X Chen, H Li, Q Zhang
Energy Storage Materials 57, 460-470, 2023
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