کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8073654 | 1521439 | 2016 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
An accurate battery State of Charge estimation plays an important role in battery electric vehicles. This paper makes two contributions to the existing literature. (1) A recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly. (2) The statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty, and a novel combination algorithm of strong tracking unscented Kalman filter and adaptive unscented Kalman filter has been developed to estimate SOC (State of Charge). Experimental results indicate that the novel algorithm has a good performance in estimating the battery SOC against initial SOC errors and voltage sensor drift. A comparison with the unscented Kalman filter-based algorithms and adaptive unscented Kalman filter-based algorithms shows that the proposed SOC estimation method has better accuracy, robustness and convergence behavior.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 109, 15 August 2016, Pages 933-946
Journal: Energy - Volume 109, 15 August 2016, Pages 933-946
نویسندگان
Yanwen Li, Chao Wang, Jinfeng Gong,