کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7730587 | 1497936 | 2015 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
الکتروشیمی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares](/preview/png/7730587.png)
چکیده انگلیسی
This paper deals with the contradiction between simplicity and accuracy of the LiFePO4 battery states estimation in the electric vehicles (EVs) battery management system (BMS). State of charge (SOC) and state of health (SOH) are normally obtained from estimating the open circuit voltage (OCV) and the internal resistance of the equivalent electrical circuit model of the battery, respectively. The difficulties of the parameters estimation arise from their complicated variations and different dynamics which require sophisticated algorithms to simultaneously estimate multiple parameters. This, however, demands heavy computation resources. In this paper, we propose a novel technique which employs a simplified model and multiple adaptive forgetting factors recursive least-squares (MAFF-RLS) estimation to provide capability to accurately capture the real-time variations and the different dynamics of the parameters whilst the simplicity in computation is still retained. The validity of the proposed method is verified through two standard driving cycles, namely Urban Dynamometer Driving Schedule and the New European Driving Cycle. The proposed method yields experimental results that not only estimated the SOC with an absolute error of less than 2.8% but also characterized the battery model parameters accurately.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Power Sources - Volume 296, 20 November 2015, Pages 215-224
Journal: Journal of Power Sources - Volume 296, 20 November 2015, Pages 215-224
نویسندگان
Van-Huan Duong, Hany Ayad Bastawrous, KaiChin Lim, Khay Wai See, Peng Zhang, Shi Xue Dou,