Deep learning-based battery state of charge estimation: Enhancing estimation performance with unlabelled training samples

Publication Name

Journal of Energy Chemistry


The estimation of state of charge (SOC) using deep neural networks (DNN) generally requires a considerable number of labelled samples for training, which refer to the current and voltage pieces with knowing their corresponding SOCs. However, the collection of labelled samples is costly and time-consuming. In contrast, the unlabelled training samples, which consist of the current and voltage data with unknown SOCs, are easy to obtain. In view of this, this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples. Besides the traditional supervised network, the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current. In this way, the developed network can extract useful information from the unlabelled samples. The proposed method is validated under different drive cycles and temperature conditions. The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples. In addition, when the dataset with reduced number of labelled samples to some extent is used to test the developed network, it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training. Furthermore, the proposed method is evaluated with different recurrent neural networks (RNNs) applied to the input reconstruction module. The results indicate that the proposed method is feasible for various RNN algorithms, and it could be flexibly applied to other conditions as required.

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China Scholarship Council



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