Journal
PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019)
Volume -, Issue -, Pages 1712-1716Publisher
IEEE
DOI: 10.1109/itaic.2019.8785770
Keywords
Lithium-ion batteries; State of Charge; battery management systems; Recurrent Neural Networks with Long Short-Term Memory
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Lithium-ion batteries (LiBs) have been widely applied in energy storage systems, and accurate State of Charge (SOC) estimation of LiBs has become a key issue. However, SOC is an important LiBs state that cannot be directly observed. It needs to be indirectly estimated by the observable variables, and SOC estimation is a challenging task due to strong nonlinear and complex electrochemical reactions change with temperature in the battery. In this paper, a Recurrent Neural Network with Long Short-Term Memory (LSTM-RNN) for SOC estimation of LiBs is introduced. By training on the observable variables of LiBs, the voltage, current and temperature can be mapped directly to SOC. Based on the dataset of LiB measured under drive cycles of electric vehicles, the effectiveness of the LSTM-RNN for SOC estimation of LiBs proposed in this paper is validated.
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