Why the study?
High-quality historical battery data is important for state estimation and management, but bandwidth and storage limitations restrict cloud reception to low-frequency data and few high-frequency signals.
The proposed MTL-SSL framework effectively recovers high-frequency battery voltage data from low-frequency signals, reducing transmission costs while maintaining high accuracy.
May enable low-cost EV battery data transfer to cloud; leaves open prospective validation of the MTL model.
High-quality historical data of battery is important for state estimation and management. However, limited by bandwidth of 4G network and storage capacity, the cloud only receives low-frequency (LF) data and a few high-frequency (HF) signals. This paper proposes a model-based method to transfer adequate battery data between vehicle and cloud at a low cost. Firstly, a training dataset is built from real-world vehicle data. Then, a multi-task learning (MTL) model under semi-supervised learning (SSL) framework is proposed to learn HF voltage representation of each battery cell. SSL framework uses a large number of unlabeled low-frequency voltages to improve the voltage frequency recovery accuracy. MTL framework enables the model to focus on battery aging and solve the problem of target domain drifting over time. Finally, both real-world vehicle and experimental data are used to compare the results of different methods on the voltage recovering task. The results show that the proposed method can reduce the average voltage recovery error to less than 7mV at various driving conditions.
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Zhu et al. (2023) studied this question.
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