1 |
J. Du, Z. Liu, and Y. Wang, "State of charge estimation for li-ion battery based on model from- extreme learning machine," Control Engineering Practice, Vol. 26, pp. 11-19, 2014.
DOI
|
2 |
J. Meng, G. Luo, and F. Gao, "Lithium polymer battery state-of-charge estimation based on adaptive unscented kalman filter and support vector machine," IEEE Trans. Power Electron., Vol. 31, No. 3, pp. 2226-2238, 2016.
DOI
|
3 |
S. Tong, J. H. Lacap, and J. W. Park, "Battery state of charge estimation using a load classifying neural network," Journal of Energy Storage, Vol. 7, pp. 236-243, 2016.
DOI
|
4 |
EPA, "Vehicle and fuel emissions testing: dynamometer drive schedules," Jan. 2017. [Online]. Available: http://epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules/. [Accessed Sep. 17, 2019].
|
5 |
J. Laraminie and J. Lowry, Electric vehicle technology explained, 1st ed. John Wiley and sons Ltd., Ch. 7, pp. 184-212, 2003.
|
6 |
A. Emadi, "Advance electric drive vehicles," CRC Press, New York, 2015.
|
7 |
E. Chemali, M. Preindl, P. Malysz, and A. Emadi, "Electrochemical and electrostatic energy storage and management systems for electric drive vehicles: state-of-the-art review and future trends," IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol. 4, No. 3, pp. 1117-1134, 2016.
DOI
|
8 |
W. Junping, G. Jingang, and D. Lei, "An adaptive kalman filtering based state of charge combined estimator for electric vehicle battery pack," Elsevier Energy Converstion and Management, Vol. 50, No. 12, pp. 3182-3186, 2009.
DOI
|
9 |
E. Chemali, P. J. Kollmeyer, M. Preindl, and A. Emadi "State-of-charge estimation of li-ion batteries using deep neural networks: A machine learning approach," Journal of Power Sources, Vol. 400, pp. 242-255, 2018.
DOI
|
10 |
C. W. Zhang, S. R. Chen, H. B. Gao, K. Xu, and M. Y. Yang, "State of charge estimation of power battery using improved back propagation neural network," Batteries, Vol. 4, No. 4, Dec. 2018.
|