| 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 | 735 | 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 | 255 | 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 | 239 | 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 | 233 | 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 | 213 | 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 | 208 | 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 | 201 | 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 | 189 | 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 | 155 | 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 | 112 | 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 | 106 | 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 | 103 | 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 | 95 | 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 | 85 | 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 | 77 | 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 | 63 | 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 | 63 | 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 | 55 | 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 | 51 | 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 | 41 | 2023 |
Weixiang Shen (沈维祥)Professor of Electrical Engineering, Swinburne University of Technology, Australia在 swin.edu.au 的电子邮件经过验证
