Research on dynamic multi-level warning method for thermal runaway charging of electric vehicles

The high-power direct current (DC) charging method for electric vehicles (EVs) can easily lead to overheating during the charging process, potentially resulting in thermal runaway accidents. Addressing this safety issue, accurately and reliably predicting and providing multi-level warnings for thermal accidents during the charging of EVs becomes an urgent problem to be solved. Therefore, this paper proposes the construction of a composite prediction model called QCNB (Q learning- CNN- BiNLSTM- BiGRU) by incorporating Convolutional Neural Networks (CNN), Bidirectional Nested Long Short-Term Memory Networks (BiNLSTM), Bidirectional Gated Recurrent Units (BiGRU), and Q learning algorithm. First, taking into account the influence of ambient temperature, three sets of charging historical data from spring/autumn, summer, and winter are selected for model training. Second, the sliding window analysis method is employed to establish the multi-level warning thresholds and rules using the historical charging data, enabling safety multi-level warnings. Lastly, the effectiveness of the QCNB model is validated using actual operating data from EVs. The experimental results demonstrate that the QCNB's prediction accuracy outperforms other models, and the abnormal characteristics of temperature and voltage can be used to identify the charging thermal runaway accident in advance and adopt the multi-level warning for effective protection. This achievement emphasizes the importance of employing a combination of predictive models to address the complex characteristics of charging data and implementing multi-level warnings for protection. It effectively mitigates the occurrence of thermal accidents, offering promising possibilities for practical applications.