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Lithium-Ion Battery Remaining Useful Life Prognostics Using Data-Driven Deep Learning Algorithm
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DOI：10.1109/PHM-Chongqing.2018.00193.png)
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As lithium-ion battery is widely applied, lithiumion battery reliability has received widespread attention in recent years. Remaining useful life (RUL) prediction is an effective way to ensure the battery reliability. The loss of actual capacity of a battery is usually used to reflect the battery RUL. However, the capacity degradation is complex and non-linear. For the longer capacity prediction horizon, the accuracy of traditional methods becomes lower which would cause error in RUL prognosis. To address this problem, this paper proposed a deep belief networks (DBN) method for lithium-ion battery RUL prediction. The proposed method is trained with historical battery capacity data. With the powerful fitting ability of DBN, the proposed method can track capacity degradation and predict the RUL. Experiments are conducted based on commercial lithium-ion batteries. The results show that the proposed method has high accuracy in capacity fade prediction and RUL prediction.
Keyword:
Lithium-ion battery
RUL estimation
Deep belief networks
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