Estimation Techniques for State of Charge in Battery Management Systems on Board of Hybrid Electric Vehicles Implemented in a Real-Time MATLAB/SIMULINK Environment

Author:

Tudoroiu Roxana-Elena,Zaheeruddin Mohammed,Radu Sorin-Mihai,Tudoroiuv Nicolae

Publisher

IntechOpen

Reference19 articles.

1. Xia B, Zheng W, Zhang R, Lao Z, Sun Z. Mint: A novel observer for Lithium-ion battery state of charge estimation in electric vehicles based on a second-order equivalent circuit model. Energies. 2017;10(8):1150. DOI: 10.3390/en10081150. Available from: http://www.mdpi.com/1996-1073/10/8/1150/htm. [Accessed: 2017-01-21]

2. Young K, Wang C, Wang LY, Strunz K. Electric vehicle battery technologies–chapter 2. In: Garcia-Valle R, Lopes JAP, editors. Electric Vehicle Integration into Modern Power Networks. 1st, 9, and 325 ed. New-York, USA: Springer Link: Springer-Verlag; 2013. pp. 15-26. DOI: 10.1007/978-1-4614-0134-6.ch.2

3. Plett GL. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs - part 1. In: Modeling and Identification, Power Sources. Vol. 134. Amsterdam, Netherlands: Elsevier B.V.; 2004. pp. 252-261. DOI: 10.1016/j.jpowsour.2004.02.031

4. Plett GL. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs - part 2. In: Modeling and Identification, Power Sources. Vol. 134. Amsterdam, Netherlands: Elsevier B.V.; 2004. pp. 262-276. DOI: 10.1016/j.jpowsour.2004.02.032

5. Simon JJ, Uhlmann JK. A new extension of the Kalman filter to nonlinear systems. [Internet]. In: Process of AeroSense, 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls; 1997. Available from: https://people.eecs.berkeley.edu/∼pabbeel/cs287-fa09/readings/JulierUhlmann-UKF.pdf. [Accessed: 2017-01-21]

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