Accurately predicting the temperature of lithium-ion batteries is a key technology for battery management systems.A deep neural network is constructed for temperature prediction of lithium-ion batteries based on their dynamic as well as time-dependent characteristics.The model can extract the potential high-dimension features of the data and appropriately reduce their dimensionality to reduce the model complexity while capturing the long-term dependence of temperature through the layer of long short-term memory cells.In addition,the heat generation rate is calculated in real-time through the open circuit voltage,terminal voltage and current of the lithium-ion battery,thus providing additional physical information input to the deep neural network.The results show that the method has better temperature prediction performance compared to other methods.
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