The Transactions of the Korean Institute of Power Electronics (전력전자학회논문지)

The Korean Institute of Power Electronics (전력전자학회)
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A Novel SOC Estimation Method for Multiple Number of Lithium Batteries Using a Deep Neural Network

딥 뉴럴 네트워크를 이용한 새로운 리튬이온 배터리의 SOC 추정법

  • Khan, Asad (Dept. of Electrical Engineering, Soongsil University) ;
  • Ko, Young-Hwi (Dept. of Electrical Engineering, Soongsil University) ;
  • Choi, Woo-Jin (Dept. of Electrical Engineering, Soongsil University)
  • Received : 2020.06.27
  • Accepted : 2020.09.02
  • Published : 2021.02.20
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Abstract

For the safe and reliable operation of lithium-ion batteries in electric vehicles or energy storage systems, having accurate information of the battery, such as the state of charge (SOC), is essential. Many different techniques of battery SOC estimation have been developed, such as the Kalman filter. However, when this filter is applied to multiple batteries, it has difficulty maintaining the accuracy of the estimation over all cells owing to the difference in parameter values of each cell. The difference in the parameter of each cell may increase 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 determine the nonlinear relationships of the voltage and current at different SOCs and temperatures. In the training, the voltage and current data obtained at different temperatures during charge/discharge cycles are used. After the comprehensive training with the data obtained from the cycle test with a cell, the resulting algorithm is applied to estimate the SOC of other cells. Experimental results show that the mean absolute error of the estimation is 1.213% at 25℃ with the proposed DNN-based SOC estimation method.

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References

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