کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7739332 | 1497985 | 2014 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sequential Monte Carlo filter for state estimation of LiFePO4 batteries based on an online updated model
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
الکتروشیمی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Battery state monitoring is one of the key techniques in battery management systems e.g. in electric vehicles. An accurate estimation can help to improve the system performance and to prolong the battery remaining useful life. Main challenges for the state estimation for LiFePO4 batteries are the flat characteristic of open-circuit-voltage over battery state of charge (SOC) and the existence of hysteresis phenomena. Classical estimation approaches like Kalman filtering show limitations to handle nonlinear and non-Gaussian error distribution problems. In addition, uncertainties in the battery model parameters must be taken into account to describe the battery degradation. In this paper, a novel model-based method combining a Sequential Monte Carlo filter with adaptive control to determine the cell SOC and its electric impedance is presented. The applicability of this dual estimator is verified using measurement data acquired from a commercial LiFePO4 cell. Due to a better handling of the hysteresis problem, results show the benefits of the proposed method against the estimation with an Extended Kalman filter.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Power Sources - Volume 247, 1 February 2014, Pages 156-162
Journal: Journal of Power Sources - Volume 247, 1 February 2014, Pages 156-162
نویسندگان
Jiahao Li, Joaquin Klee Barillas, Clemens Guenther, Michael A. Danzer,