کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4916877 | 1428104 | 2016 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles
ترجمه فارسی عنوان
روش تشخیص خطا مبتنی بر مدل بر روی اتصال خارجی باتری لیتیوم یون مورد استفاده در خودروهای الکتریکی
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کلمات کلیدی
وسایل نقلیه الکتریکی، ایمنی باتری، اتصال کوتاه خارجی، تشخیص گسل، مدل مدار معادل، بهینه سازی ذرات ذرات،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
چکیده انگلیسی
This study investigates the external short circuit (ESC) fault characteristics of lithium-ion battery experimentally. An experiment platform is established and the ESC tests are implemented on ten 18650-type lithium cells considering different state-of-charges (SOCs). Based on the experiment results, several efforts have been made. (1) The ESC process can be divided into two periods and the electrical and thermal behaviors within these two periods are analyzed. (2) A modified first-order RC model is employed to simulate the electrical behavior of the lithium cell in the ESC fault process. The model parameters are re-identified by a dynamic-neighborhood particle swarm optimization algorithm. (3) A two-layer model-based ESC fault diagnosis algorithm is proposed. The first layer conducts preliminary fault detection and the second layer gives a precise model-based diagnosis. Four new cells are short-circuited to evaluate the proposed algorithm. It shows that the ESC fault can be diagnosed within 5Â s, the error between the model and measured data is less than 0.36Â V. The effectiveness of the fault diagnosis algorithm is not sensitive to the precision of battery SOC. The proposed algorithm can still make the correct diagnosis even if there is 10% error in SOC estimation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Energy - Volume 184, 15 December 2016, Pages 365-374
Journal: Applied Energy - Volume 184, 15 December 2016, Pages 365-374
نویسندگان
Zeyu Chen, Rui Xiong, Jinpeng Tian, Xiong Shang, Jiahuan Lu,