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.

The energy storage system is an important part of the energy system. Lithium-ion batteries have been widely used in energy storage systems because of their high energy density and long life. However, the temperature is still the key factor hindering the further development of lithium-ion battery energy storage systems. Both low temperature and high temperature will reduce the life and safety of lithium-ion batteries. In actual operation, the core temperature and the surface temperature of the lithium-ion battery energy storage system may have a large temperature difference. However, only the surface temperature of the lithium-ion battery energy storage system can be easily measured. The estimation method of the core temperature, which can better reflect the operation condition of the lithium-ion battery energy storage system, has not been commercialized. To secure the thermal safety of the energy storage system, a multi-step ahead thermal warning network for the energy storage system based on the core temperature detection is developed in this paper. The thermal warning network utilizes the measurement difference and an integrated long and short-term memory network to process the input time series. This thermal early warning network takes the core temperature of the energy storage system as the judgment criterion of early warning and can provide a warning signal in multi-step in advance. This detection network can use real-time measurement to predict whether the core temperature of the lithium-ion battery energy storage system will reach a critical value in the following time window. And the output of the established warning network model directly determines whether or not an early emergency signal should be sent out. In the end, the accuracy and effectiveness of the model are verified by numerous testing.

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