전력전자학회:학술대회논문집 (Proceedings of the KIPE Conference)
- 전력전자학회 2019년도 추계학술대회
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- Pages.70-72
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- 2019
딥 뉴럴 네트워크를 이용한 새로운 리튬이온 배터리의 SOC 추정법
A Novel SOC Estimation Method for Multiple Number of Lithium Batteries Using Deep Neural Network
- Khan, Asad (Soongsil University) ;
- Ko, Young-hwi (Soongsil University) ;
- Choi, Woojin (Soongsil University)
- 발행 : 2019.11.22
초록
For the safe and reliable operation of Lithium-ion batteries in Electric Vehicles (EVs) or Energy Storage Systems (ESSs), it is essential to have accurate information of the battery such as State of Charge (SOC). Many kinds of different techniques to estimate the SOC of the batteries have been developed so far such as the Kalman Filter. However, when it is applied to the multiple number of batteries it is difficult to maintain the accuracy of the estimation over all cells due to the difference in parameter value of each cell. Moreover the difference in the parameter of each cell may become larger as the operation time accumulates due to aging. In this paper a novel Deep Neural Network (DNN) based SOC estimation method for multi cell application is proposed. In the proposed method DNN is implemented to learn non-linear relationship of the voltage and current of the lithium-ion battery at different SOCs and different temperatures. In the training the voltage and current data of the Lithium battery at charge and discharge cycles obtained at different temperatures are used. After the comprehensive training with the data obtained with a cell resulting estimation algorithm is applied to the other cells. The experimental results show that the Mean Absolute Error (MAE) of the estimation is 0.56% at 25℃, and 3.16% at 60℃ with the proposed SOC estimation algorithm.
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