Battery health prognosis using improved temporal convolutional network modeling

Accurate estimation of the state of health (SOH) of lithium-ion batteries is the key to ensure the safe use of lithium-ion batteries. In practice, the application of traditional health features is hindered by incomplete charge and discharge. When the battery is stably charged, the voltage and temperature of the battery under different health states show similar spatial degradation trends. Therefore, the degradation trend of voltage and temperature can be directly used as the health feature sequence to reduce the error caused by manual feature extraction. In addition, a new model attention depthwise temporal convolutional network (AD-TCN) considering health characteristics is proposed for SOH estimation. Depthwise separable convolution operation is used to extend temporal convolutional network (TCN) to a model suitable for multivariate prediction. Depthwise convolution is used as feature extractor, and pointwise convolution recombines all features for regression prediction. In addition, the convolutional block attention module is used in the channel dimension and spatial dimension to selectively enhance or suppress the details. Experiments on NASA data sets show that this method has strong reliability and high prediction accuracy.

Automated summary

Accurate estimation of the state of health (SOH) of lithium-ion batteries is crucial for ensuring their safe use. A novel attention depthwise temporal convolutional network (AD-TCN) model is proposed for SOH estimation, considering the degradation trend of voltage and temperature as the health feature sequence. The model shows strong reliability and high prediction accuracy.