| Abstract |
Due to the promotion of electric vehicles and new energy sources, lithium-ion batteries have been widely used. However, temperature has a great influence on the performance and safety of lithium-ion batteries during operation. Therefore, it is very important to predict the temperature of lithium-ion batteries and implement thermal early warning. In order to solve this problem, this paper designed a Sequential neural network-fuzzy thermal early warning system (SNNFT). First, the SNNFT uses a denoising autoencoder to eliminate the noise in real-time measurement. Then it combines the long short-term memory network and the temporal convolutional network that can handle the time series problem well to realize the accurate prediction of the lithium-ion battery temperature. And the SNNFT applies interpretable adaptive network-based fuzzy inference system model to build thermal early warning system. Complete experiments are conducted to verify the reliability advantages. |
