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- W4213342369 abstract "The State of Charge (SOC) is the most important index in the Battery Management System (BMS). It tells us how much charge the battery is holding at a particular time. An accurate SOC estimation is essential for ensuring the safe and reliable operation of the battery. In this paper, a Neural Network approach known as Convolutional Neural Network (CNN) opted for SOC estimation. Dataset Corresponding to New York City Cycle (NYCC) driving cycle at temperatures 15° C, 250C, and 45° C is considered for evaluating our model. The NYCC dataset used for estimation purposes is a real dataset obtained from experimenting. In order to achieve the optimal result, we validated the model against a different number of hyperparameters by varying the number of filters in the convolutional layers. The proposed CNN provides a competitive estimation performance compared to other model-based methods for SOC estimation." @default.
- W4213342369 created "2022-02-24" @default.
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- W4213342369 date "2021-12-10" @default.
- W4213342369 modified "2023-09-28" @default.
- W4213342369 title "Convolution Neural Network-Based SOC Estimation of Li-ion Battery in EV Applications" @default.
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- W4213342369 doi "https://doi.org/10.1109/iceeccot52851.2021.9708055" @default.
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