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.

Review on Thermal Management of Lithium-Ion Batteries for Electric Vehicles: Advances, Challenges, and Outlook

“…Prior to design and modeling, the parameters of the thermal management system must be determined or optimized. These parameters optimization include air velocity in the forced air cooling, − the ambient temperature in the forced air cooling, the flow rate of the liquid, − and cooling liquid temperature. − Some studies have proposed optimization using the ML method, which is considered to be an excellent tool for optimizing and predicting parameters. − Researchers attempted to implement ML models, such as artificial neural networks (ANNs), convolutional neural networks (CNNs), long short-term memory (LSTM), deep reinforcement learning (DRL), etc., to assist the BTM system for enhanced battery thermal safety and resilience. For example, Jaliliantabar et al developed an ANN model for the prediction of LIB temperature equipped with BTMs and proved the capability of ANN to predict battery temperature in various operating conditions of BTMs.…”

“…193−196 Some studies have proposed optimization using the ML method, which is considered to be an excellent tool for optimizing and predicting parameters. 197−199 Researchers attempted to implement ML models, such as artificial neural networks (ANNs), 200 convolutional neural networks (CNNs), 201 long short-term memory (LSTM), 202 deep reinforcement learning (DRL), 203 etc., to assist the BTM system for enhanced battery thermal safety and resilience. For example, Jaliliantabar et al 204 developed an ANN model for the prediction of LIB temperature equipped with BTMs and proved the capability of ANN to predict battery temperature in various operating conditions of BTMs.…”