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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°C with the proposed DNN-based SOC estimation method.
목차
Abstract
1. Introduction
2. Deep Neural Network for SOC Estimation
3. Data Preparation, Learning and Validation
4. Experimental Results
5. Conclusion
References
참고문헌 (10)
참고문헌 신청
C. W. Zhang / 2018 / State of charge estimation of power battery using improved back propagation neural network / Batteries 4(4)
E. Chemali / 2018 / State-of-charge estimation of li-ion batteries using deep neural networks : A machine learning approach / Journal of Power Sources 400:242~255
J. Du / 2014 / State of charge estimation for li-ion battery based on model from-extreme learning machine / Control Engineering Practice 26:11~19
J. Meng / 2016 / Lithium polymer battery state-of-charge estimation based on adaptive unscented kalman filter and support vector machine / IEEE Trans. Power Electron. 31(3):2226~2238
S. Tong / 2016 / Battery state of charge estimation using a load classifying neural network / Journal of Energy Storage 7:236~243
E. Chemali / 2018 / State-of-charge estimation of li-ion batteries using deep neural networks : A machine learning approach / Journal of Power Sources 400:242~255
J. Du / 2014 / State of charge estimation for li-ion battery based on model from-extreme learning machine / Control Engineering Practice 26:11~19
J. Meng / 2016 / Lithium polymer battery state-of-charge estimation based on adaptive unscented kalman filter and support vector machine / IEEE Trans. Power Electron. 31(3):2226~2238
S. Tong / 2016 / Battery state of charge estimation using a load classifying neural network / Journal of Energy Storage 7:236~243
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