کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6945518 | 1450516 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries
ترجمه فارسی عنوان
یک روش پیش بینی جدید بر اساس رگرسیون بردار پشتیبانی برای عمر باقیمانده باتری های لیتیوم یون
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کلمات کلیدی
باتری لیتیوم یون، وضعیت سلامت، عمر مفید دیگر، پیش آگهی، انتخاب بردار ویژگی، رگرسیون بردار پشتیبانی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
سخت افزارها و معماری
چکیده انگلیسی
Traditional approaches to lithium-ion battery health management mostly focus on the state of charge (SOC) estimation issues, whereas the state of health (SOH) estimation is also critical to lithium-ion batteries for safe operation. For online battery prognostics, it is critical to make timely and accurate response to SOH. The loss of rated capacity of a battery is usually used to determine the battery SOH, whereas the measurement of the capacity of an operating battery is quite challenging. Normally, the rated capacity fading largely relies on laboratory measurements and offline analysis. In this paper, two real-time measurable health indicators (HI) - one is the time interval of an equal charging voltage difference (TIECVD), and the other is the time interval of an equal discharging voltage difference (TIEDVD) - are extracted. A novel method which combines feature vector selection (FVS) with SVR is utilized to model the relationship between these two HIs and capacity, then the online capacity can be evaluated, more accurate prognostics of SOH and remaining useful life (RUL) can be made. Besides, compared to standard SVR, the proposed method takes FVS to cut down the training data size, which improves the efficiency of model training and prediction. In the end, two datasets demonstrated this approach performs both well in accuracy and efficiency.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Microelectronics Reliability - Volume 85, June 2018, Pages 99-108
Journal: Microelectronics Reliability - Volume 85, June 2018, Pages 99-108
نویسندگان
Qi Zhao, Xiaoli Qin, Hongbo Zhao, Wenquan Feng,