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Lithium Iron Phosphate Battery Electric Vehicle State-of-Charge Estimation Based on Evolutionary Gaussian Mixture Regression.
- Source :
- IEEE Transactions on Industrial Electronics; Jan2017, Vol. 64 Issue 1, p544-551, 8p
- Publication Year :
- 2017
-
Abstract
- Lithium batteries have the characteristics of high energy density and charge–discharge rate, but exhibit high chemical activity. State-of-charge (SOC) estimation is critical to the lithium battery electric vehicle (EV) operation safety. In this paper, a novel SOC estimation method is proposed based on Gaussian process regression. A mixture Gaussian process is used in this model to strengthen the reliability of data description and to increase the estimation accuracy. Optimal number of Gaussian processes is obtained by a revolutionary expectation maximum method. A nonlinear correlation feature selection method is introduced to improve the model efficiency. The effectiveness of the proposed method is verified by an EV field test. Compared with other data-based approaches, this method exhibits higher estimation accuracy and computational efficiency. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 02780046
- Volume :
- 64
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Industrial Electronics
- Publication Type :
- Academic Journal
- Accession number :
- 120167633
- Full Text :
- https://doi.org/10.1109/TIE.2016.2606588