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Batteries

  1. X. Shu, S. Shen, J. Shen, Y. Zhang, G. Li, Z. Chen and Y. Liu, State of Health Prediction of Lithium-Ion Batteries Based on Machine Learning: Advances and Perspectives, iScience, 103265, Oct 2021.
  2. X. Shu, G. Li, Y. Zhang, S. Shen, Z. Chen and Y. Liu, "Stage of Charge Estimation of Lithium-Ion Battery Packs Based on Improved Cubature Kalman Filter With Long Short-Term Memory Model," in IEEE Transactions on Transportation Electrification, vol. 7, no. 3, pp. 1271-1284, Sept. 2021, doi: 10.1109/TTE.2020.3041757
  3. X. Shu, J. Shen, G. Li, Y. Zhang, Z. Chen and Y. Liu, A Flexible State of Health Prediction Scheme for Lithium-Ion Battery Packs with Long Short-Term Memory Network and Transfer Learning, accepted by IEEE Transactions on Transportation Electrification.
  4. Q. Xue, S. Shen, G. Li, Y. Zhang, Z. Chen and Y. Liu, Remaining Useful Life Prediction for Lithium-ion Batteries Based on Capacity Estimation and Box-Cox Transformation, IEEE Transactions on Vehicular Technology, Vol. 69 (12),  pp. 14765 - 14779, 2020. DOI: 10.1109/TVT.2020.3039553
  5. J. Shen, J. Xiong, X. Shu, G. Li, Y. Zhang, Z. Chen and Y. Liu, State of Charge Estimation Framework for Lithium-ion Batteries Based on Square Root Cubature Kalman Filter under Wide Operation Temperature Range, International Journal of Energy Research. 1-16, 2020.  https://doi.org/10.1002/er.6186
  6. M.S.S. Malik, G. Li and Z. Chen, An optimal charging algorithm to minimise solid electrolyte interface layer in lithium-ion battery, Vol 482, 228895, Journal of Power Sources, 2021. https://doi.org/10.1016/j.jpowsour.2020.228895
  7. X. Shu, G. Li, J. Shen Z. Lei, Z. Chen and Y. Liu, An Adaptive Multi-State Estimation Algorithm for Lithium-ion Batteries Incorporating Temperature Compensation Energy, Energy, vol 67, 118262, 2020. https://doi.org/10.1016/j.energy.2020.118262
  8. X. Shu, G. Li, Y. Zhang, J. Shen , Z. Chen and Y. Liu, Online diagnosis of state of health for Lithium-ion batteries based on short-term charging profiles, Journal of Power Sources, vol. 471, 228478, 2020. https://doi.org/10.1016/j.jpowsour.2020.228478
  9. X. Shu, G. Li J. Shen , Z. Lei , Z, Chen and Y. Liu. A Uniform Estimation Framework for State of Health of Lithium-ion Batteries Considering Feature Extraction and Parameters Optimization, Energy, vol. 204, 117957, August 2020. https://doi.org/10.1016/j.energy.2020.117957
  10. X. Shu, G. Li, J. Shen, W. Yan, Z. Chen and Y. Liu, An Adaptive Fusion Estimation Algorithm for State of Charge of Lithium-ion Batteries Considering Wide Operating Temperature and Degradation, Journal of Power Sources, Vol. 462, 228132, Mar. 2020. https://doi.org/10.1016/j.jpowsour.2020.228132
  11. J. Liu, G. Li and H. K. Fathy, An Extended Differential Flatness Approach for the Health-Conscious Nonlinear Model Predictive Control of Lithium-Ion Batteries,IEEE Transactions on Control Systems Technology, vol. 25 (5), pp. 1882 - 1889, 2017. DOI: 10.1109/TCST.2016.2624143
  12. J. Liu, G. Li and H. K. Fathy, A computationally efficient approach for optimizing lithium-ion battery charging, ASME Journal of Dynamic Systems, Measurement, and Control, Feb, 2016.
  13. J. Liu, G. Li and H. K. Fathy, Efficient Lithium-ion Battery Model Predictive Control Using Differential Flatness-Based Pseudospectral Methods, Proceedings of ASME 2015 Dynamic Systems and Control Conference, Columbus, Ohio, USA, October 28-30, 2015.
  14. H. K. Fathy and G. Li, First Observations from a Partially Inverted System Dynamics Course, American Control Conference, 2015.
  15. J. Liu, G. Li, H. K. Fathy, S. Brennan and J. Anstrom, Exploiting differential atness and pseudospectral optimization to improve the computational efficiency of health-conscious Lithium-ion battery control, ECS conference, Cancun, Mexico, 2014.