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CLC number: TM912.1

On-line Access: 2011-10-28

Received: 2011-05-20

Revision Accepted: 2011-09-08

Crosschecked: 2011-09-29

Cited: 24

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2011 Vol.12 No.11 P.818-825

http://doi.org/10.1631/jzus.A1100141


Recursive calibration for a lithium iron phosphate battery for electric vehicles using extended Kalman filtering


Author(s):  Xiao-song Hu, Feng-chun Sun, Xi-ming Cheng

Affiliation(s):  National Engineering Laboratory for Electric Vehicles, Department of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Corresponding email(s):   huxstank@bit.edu.cn, cxm2004@bit.edu.cn

Key Words:  Model calibration, Lithium iron phosphate battery, Electric vehicle (EV), Extended Kalman filtering


Xiao-song Hu, Feng-chun Sun, Xi-ming Cheng. Recursive calibration for a lithium iron phosphate battery for electric vehicles using extended Kalman filtering[J]. Journal of Zhejiang University Science A, 2011, 12(11): 818-825.

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author="Xiao-song Hu, Feng-chun Sun, Xi-ming Cheng",
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%A Xi-ming Cheng
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%DOI 10.1631/jzus.A1100141

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T1 - Recursive calibration for a lithium iron phosphate battery for electric vehicles using extended Kalman filtering
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DOI - 10.1631/jzus.A1100141


Abstract: 
In this paper, an efficient model structure composed of a second-order resistance-capacitance network and a simply analytical open circuit voltage versus state of charge (SOC) map is applied to characterize the voltage behavior of a lithium iron phosphate battery for electric vehicles (EVs). As a result, the overpotentials of the battery can be depicted using a second-order circuit network and the model parameterization can be realized under any battery loading profile, without a special characterization experiment. In order to ensure good robustness, extended Kalman filtering is adopted to recursively implement the calibration process. The linearization involved in the calibration algorithm is realized through recurrent derivatives in a recursive form. Validation results show that the recursively calibrated battery model can accurately delineate the battery voltage behavior under two different transient power operating conditions. A comparison with a first-order model indicates that the recursively calibrated second-order model has a comparable accuracy in a major part of the battery SOC range and a better performance when the SOC is relatively low.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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Anonymous@No address<No mail>

2011-11-18 23:41:42

very good!

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