کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5001119 | 1460867 | 2017 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Aging prediction and state of charge estimation of a LiFePO4 battery using input time-delayed neural networks
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Aging prediction and state of charge estimation of a LiFePO4 battery using input time-delayed neural networks Aging prediction and state of charge estimation of a LiFePO4 battery using input time-delayed neural networks](/preview/png/5001119.png)
چکیده انگلیسی
This paper presents an intelligent state of charge (SOC) and state of health (SOH) estimation method for lithium-ion batteries using an input time-delayed neural network. Unlike other estimation strategies, this technique requires no prior knowledge of the battery's model or parameters. Instead, it uses ambient temperature variations and previous battery's voltage and current data to accurately predict its SOC and SOH. The presented method compensates for the nonlinear patterns in battery characteristics such as hysteresis, variance due to electrochemical properties, and battery degradation due to aging. This technique is evaluated using a LiFePO4 battery and experimental results highlight its high accuracy, simplicity, and robustness.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Electric Power Systems Research - Volume 146, May 2017, Pages 189-197
Journal: Electric Power Systems Research - Volume 146, May 2017, Pages 189-197
نویسندگان
Hicham Chaoui, Chinemerem C. Ibe-Ekeocha, Hamid Gualous,