Multi-scenario deep learning-based framework to estimate the remaining charge time of lithium-ion power batteries AITranslate

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Basic Information:

DOI：10.23919/CHAIN.2024.000002

Chinese Library Classification Number:

Citation Information:

The accurate estimation of the remaining charge time (RCT) is essential in a battery management system (BMS), because it guarantees the safety and dependability of the power battery systems of new energy vehicles. However, the direct estimation of RCT is challenging because of the variability of actual charging scenarios and the complex charging process, which complicates the estimation of RCT in actual scenarios. Hence, this paper proposes an estimation framework based on deep learning for multi-scenario charging data to estimate the remaining charging times. Through an in-depth analysis of multi-scenario charging data, the RCT of the charging process is estimated using the temporal convolutional network (TCN) model, which has a strong generalization ability. Additionally, a dynamic learning rate (DLR) mechanism and an early stopping strategy (ES) are designed in the TCN model (DLR-ES TCN) for the nonlinear characteristics of the battery system to balance the relationship between model convergence speed and accuracy. Finally, compared with the traditional TCN model and four common deep learning models under three different scenarios, the experimental results show the mean absolute percentage error (MAPE) of the proposed method is less than 2%, indicating better accuracy and stability. This research can improve the safety monitoring of power batteries when applied to various target domains.