کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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1289281 | 973294 | 2011 | 6 صفحه PDF | دانلود رایگان |

The state-of-charge (SOC) of batteries and battery–supercapacitor hybrid systems is predicted using artificial neural networks (ANNs). Our technique is able to predict the SOC of energy storage devices based on a short initial segment (less than 4% of the average lifetime) of the discharge curve. The prediction shows good performance with a correlation coefficient above 0.95. We are able to improve the prediction further by considering readily available measurements of the device and usage. The prediction is further shown to be resilient to changes in operating conditions or physical structure of the devices.
Research highlights▶ State-of-charge of batteries and battery–supercapacitor hybrids is predicted. ▶ Good correspondence between prediction and observed results was demonstrated. ▶ Prediction is performed by ANN based on short initial segment of discharge curve. ▶ Prediction is resilient to changes in operating conditions and physical structure.
Journal: Journal of Power Sources - Volume 196, Issue 8, 15 April 2011, Pages 4061–4066