کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6946439 | 1450545 | 2015 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
سخت افزارها و معماری
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
State of health (SOH) estimation of lithium-ion batteries is significant for safe and lifetime-optimized operation. In this study, support vector regression (SVR) is employed in battery SOH prognostics, and particle swarm optimization (PSO) is employed in obtaining the SVR kernel parameter. Through a new validation method, the proposed PSO-SVR model in this paper can well grasp the global degradation trend of SOH and is little affected by local regeneration and fluctuations. The case study shows that compared with the eight published methods, the proposed model can obtain more accurate SOH prediction results. Even SOH prediction starts from the cycle near capacity regeneration, the proposed model still can grasp the global degradation trend. Furthermore, the improved PSO-SVR model has great robustness when the training data contain noise and measurement outliers, which makes it possible to get satisfactory prediction performance without pre-processing the data manually.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Microelectronics Reliability - Volume 55, Issues 9â10, AugustâSeptember 2015, Pages 1280-1284
Journal: Microelectronics Reliability - Volume 55, Issues 9â10, AugustâSeptember 2015, Pages 1280-1284
نویسندگان
Taichun Qin, Shengkui Zeng, Jianbin Guo,