کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1292868 1497949 2015 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries
ترجمه فارسی عنوان
یک رویکرد یکپارچه برای برآورد بار مبتنی بر مدل مبتنی بر مدل باتری لیتیوم یون در زمان واقعی
موضوعات مرتبط
مهندسی و علوم پایه شیمی الکتروشیمی
چکیده انگلیسی


• An auto-regression battery model is built considering hysteresis nonlinearity.
• A hybrid model training method combining TLBO and least square is proposed.
• WRLS and joint-EKF approaches are used for real-time model-based SOC estimation.
• Flat OCV problem is tackled by combining WRLS method with coulomb counting.

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Power Sources - Volume 283, 1 June 2015, Pages 24–36
نویسندگان
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