| An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks J Wu, C Zhang, Z Chen Applied energy 173, 134-140, 2016 | 511 | 2016 |
| A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain H Dai, G Zhao, M Lin, J Wu, G Zheng IEEE transactions on industrial electronics 66 (10), 7706-7716, 2018 | 296 | 2018 |
| A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation X Liu, Z Chen, C Zhang, J Wu Applied Energy 123, 263-272, 2014 | 264 | 2014 |
| A novel state of health estimation method of Li-ion battery using group method of data handling J Wu, Y Wang, X Zhang, Z Chen Journal of Power Sources 327, 457-464, 2016 | 238 | 2016 |
| A method for state of energy estimation of lithium-ion batteries at dynamic currents and temperatures X Liu, J Wu, C Zhang, Z Chen Journal of Power Sources 270, 151-157, 2014 | 183 | 2014 |
| Particle filter-based state-of-charge estimation and remaining-dischargeable-time prediction method for lithium-ion batteries Z Chen, H Sun, G Dong, J Wei, JI Wu Journal of Power Sources 414, 158-166, 2019 | 165 | 2019 |
| A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries M Lin, D Wu, J Meng, J Wu, H Wu Journal of Power Sources 518, 230774, 2022 | 140 | 2022 |
| A novel method for lithium-ion battery state of energy and state of power estimation based on multi-time-scale filter XU Zhang, Y Wang, JI Wu, Z Chen Applied energy 216, 442-451, 2018 | 120 | 2018 |
| Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression M Lin, C Yan, J Meng, W Wang, J Wu Energy 250, 123829, 2022 | 83 | 2022 |
| State of health estimation of lithium-ion battery with improved radial basis function neural network J Wu, L Fang, G Dong, M Lin Energy 262, 125380, 2023 | 81 | 2023 |
| Energy Management Strategy for Grid-tied Microgrids considering the Energy Storage Efficiency J Wu, X Xing, X Liu, JM Guerrero, Z Chen IEEE Transactions on Industrial Electronics 65 (12), 9539 - 9549, 2018 | 80 | 2018 |
| An empirical-data hybrid driven approach for remaining useful life prediction of lithium-ion batteries considering capacity diving D Chen, J Meng, H Huang, J Wu, P Liu, J Lu, T Liu Energy 245, 123222, 2022 | 79 | 2022 |
| An extended Kalman filter based data-driven method for state of charge estimation of Li-ion batteries X Liu, K Li, J Wu, Y He, X Liu Journal of Energy Storage 40, 102655, 2021 | 79 | 2021 |
| State of health estimation of lithium-ion battery based on an adaptive tunable hybrid radial basis function network M Lin, X Zeng, J Wu Journal of Power Sources 504, 230063, 2021 | 72 | 2021 |
| Health prognosis with optimized feature selection for lithium-ion battery in electric vehicle applications J Wu, X Cui, H Zhang, M Lin IEEE Transactions on Power Electronics 36 (11), 12646-12655, 2021 | 63 | 2021 |
| State of health estimation with attentional long short-term memory network for lithium-ion batteries M Lin, J Wu, J Meng, W Wang, J Wu Energy 268, 126706, 2023 | 54 | 2023 |
| A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance M Lin, C Yan, W Wang, G Dong, J Meng, J Wu Energy 277, 127675, 2023 | 45 | 2023 |
| Data-driven state of charge estimation for power battery with improved extended Kalman filter X Liu, Q Li, L Wang, M Lin, J Wu IEEE Transactions on Instrumentation and Measurement 72, 1-10, 2023 | 45 | 2023 |
| Data-driven transfer-stacking-based state of health estimation for lithium-ion batteries J Wu, X Cui, J Meng, J Peng, M Lin IEEE Transactions on Industrial Electronics 71 (1), 604-614, 2023 | 40 | 2023 |
| An improved state of charge and state of power estimation method based on genetic particle filter for lithium-ion batteries X Liu, C Zheng, J Wu, J Meng, DI Stroe, J Chen Energies 13 (2), 478, 2020 | 40 | 2020 |
Mingqiang LinFujian Institute of Research on the Structure of Matter在 fjirsm.ac.cn 的电子邮件经过验证