Data-model alliance network for the online multi-step thermal warning of energy storage system based on surface temperature diffusion

Marui Li; Chaoyu Dong; Yunfei Mu; Xiaodan Yu; Qian Xiao; Hongjie Jia

doi:10.1016/j.patter.2021.100432

Data-model alliance network for the online multi-step thermal warning of energy storage system based on surface temperature diffusion

Patterns (N Y). 2022 Jan 26;3(2):100432. doi: 10.1016/j.patter.2021.100432. eCollection 2022 Feb 11.

Authors

Marui Li¹, Chaoyu Dong¹, Yunfei Mu¹, Xiaodan Yu¹, Qian Xiao¹, Hongjie Jia¹

Affiliation

¹ School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.

Abstract

As an important type of energy storage, battery energy storage systems have been widely used. However, there are frequent cases of battery explosion due to high temperature. To address this issue, researches have been carried out either in the model-driven or the data-driven aspects to predict the temperature of the battery. In this paper, a two-node electrothermal model and a multi-scale long short-term memory network are established formulating a data-model alliance network (DMAN) for surface temperature diffusion. An improved adaptive boosting algorithm is then employed to enhance the bridge of the two models. Integrating a data-model alliance module (DMAM) and multi-step-ahead thermal warning network (MATWN), this DMAN provides an advanced online multi-step-ahead thermal warning structure to achieve early warning of temperature crossing. Experimental results verify the progressiveness of the proposed technique.

Keywords: data-model alliance network; long short-term memory network; thermal warning.