Abstract
With the development of renewable energy, energy storage system has been endowed with more imperative role. As an important way of energy storage, battery energy storage system has been widely used. However, there are frequent cases of battery explosion due to high temperature, which hinders the further development of battery energy storage systems. 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 and short-term memory network are established formulating a data-model alliance network (DMAN) for the surface temperature diffusion. An improved adaptive boosting algorithm is then employed to enhance the bridge of two models. Integrating the data-model alliance module (DMAM) and multi-step ahead thermal warning network (MATWN), this data-model alliance network provides an advanced online multi-step ahead thermal warning structure to achieve the early warning of temperature crossing. Experimental results verify the progressiveness of the proposed technique.
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