The frequent occurrence of electric vehicle fire accidents reveals the safety hazards of batteries. When a battery fails, its symmetry is broken, which results in a rapid degradation of its safety performance and poses a great threat to electric vehicles. Therefore, accurate battery fault diagnoses and prognoses are the key to ensuring the safe and durable operation of electric vehicles. Thus, in this paper, we propose a new fault diagnosis and prognosis method for lithium-ion batteries based on a nonlinear autoregressive exogenous (NARX) neural network and boxplot for the first time. Firstly, experiments are conducted under different temperature conditions to guarantee the diversity of the data of lithium-ion batteries and then to ensure the accuracy of the fault diagnosis and prognosis at different working temperatures. Based on the collected voltage and current data, the NARX neural network is then used to accurately predict the future battery voltage. A boxplot is then used for the battery fault diagnosis and early warning based on the predicted voltage. Finally, the experimental results (in a new dataset) and a comparative study with a back propagation (BP) neural network not only validate the high precision, all-climate applicability, strong robustness and superiority of the proposed NARX model but also verify the fault diagnosis and early warning ability of the boxplot. In summary, the proposed fault diagnosis and prognosis approach is promising in real electric vehicle applications.

中文翻译：

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一种基于非线性自回归外源神经网络和箱线图的锂离子电池故障诊断与预测方法

电动汽车火灾事故频发，暴露出电池的安全隐患。当电池出现故障时，其对称性被破坏，导致其安全性能迅速下降，对电动汽车构成巨大威胁。因此，准确的电池故障诊断和预测是确保电动汽车安全耐用运行的关键。因此，在本文中，我们首次提出了一种基于非线性自回归外生（NARX）神经网络和箱线图的锂离子电池故障诊断和预测新方法。首先在不同温度条件下进行实验，保证锂离子电池数据的多样性，进而保证不同工作温度下故障诊断和预测的准确性。根据收集到的电压和电流数据，然后使用 NARX 神经网络来准确预测未来的电池电压。然后根据预测电压使用箱线图进行电池故障诊断和预警。最后，实验结果（在新的数据集中）和与反向传播（BP）神经网络的对比研究不仅验证了所提出的 NARX 模型的高精度、全气候适用性、强鲁棒性和优越性，而且验证了错误箱线图的诊断和预警能力。总之，所提出的故障诊断和预测方法在实际电动汽车应用中很有前景。然后根据预测电压使用箱线图进行电池故障诊断和预警。最后，实验结果（在新的数据集中）和与反向传播（BP）神经网络的对比研究不仅验证了所提出的 NARX 模型的高精度、全气候适用性、强鲁棒性和优越性，而且验证了错误箱线图的诊断和预警能力。总之，所提出的故障诊断和预测方法在实际电动汽车应用中很有前景。然后根据预测电压使用箱线图进行电池故障诊断和预警。最后，实验结果（在新的数据集中）和与反向传播（BP）神经网络的对比研究不仅验证了所提出的 NARX 模型的高精度、全气候适用性、强鲁棒性和优越性，而且验证了错误箱线图的诊断和预警能力。总之，所提出的故障诊断和预测方法在实际电动汽车应用中很有前景。NARX模型具有较强的鲁棒性和优越性，同时也验证了箱线图的故障诊断和预警能力。总之，所提出的故障诊断和预测方法在实际电动汽车应用中很有前景。NARX模型具有较强的鲁棒性和优越性，同时也验证了箱线图的故障诊断和预警能力。总之，所提出的故障诊断和预测方法在实际电动汽车应用中很有前景。