Synthetic thermal convolutional-memory network for the lithium-ion battery behaviour diagnosis against noise interruptions

Abstract

In order to meet the two global challenges of energy shortage and environmental pollution, various countries have begun to advocate the application of new energy equipment such as electric vehicles. This has also promoted the development of energy storage equipment and energy storage systems. With their high performance, lithium-ion batteries are used in a wide range of electrical equipment. But the safety of lithium-ion batteries depends on effective behaviour diagnosis. In order to better realise behaviour diagnosis, this paper combined the long and short-term memory network (LSTM) with the temporal convolution network (TCN) for the first time and established a synthetic thermal convolutional-memory network (STCMN) for lithium-ion battery behaviour diagnosis against noise interruptions. In addition, a TCN-LSTM alliance network structure is designed. The TCN-LSTM alliance network is an effective architecture applied not only to the temperature prediction of Li-ion batteries but also to the thermal diagnosis part. And these two parts finally constitute the thermal convolutional-memory network. The experimental results show the network designed in this paper was able to improve Li-ion battery behaviour detection.