Analysis and prediction of thermal runaway propagation interval in confined space based on response surface methodology and artificial neural network. (30th November 2022)

• Record Type: Journal Article
• Authors: Yan, Wei; Wang, Zhirong; Ouyang, Dongxu; Chen, Shichen
• Is Part Of: Journal of energy storage. Volume 55:Part D(2022)
• Publication Date: 2022-11-30
• Subjects: Lithium ion battery -- Thermal runaway propagation -- Response surface methodology -- Artificial neural network -- Prediction model; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.105822
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: Thermal runaway (TR) of lithium-ion batteries (LIBs) and its propagation in battery packs may bring significant losses and restrict the wide application of LIB. It is important to study the propagation characteristics of TR. Based on a series of experiments, this work analyses the influence of state of charge, environment temperature, and heating power on the thermal runaway propagation interval (TRPI) between two adjacent cells in a confined space. The results show that they all have a significant impact on TRPI. Furthermore, response surface methodology (RSM) is employed to study the interactions among these three factors. The minimum TRPI is predicted to be 61.08 s. Based on artificial neural network (ANN), a prediction model trained by back-propagation algorithm is constructed for temperature variations of two cells. The results show that the model is effective in prediction, with the maximum prediction error of 6.88 % and the average prediction error of 3.42 %. It is found that TR can be propagated within 37 s, which brings great challenges to the management of battery packs. This research provides effective methods for identifying the safety problems of LIB packs based on RSM and predicting the temperature variations of cells based on ANN methodology. Highlights: State of charge, environmental temperature, and heating power affect the thermal runaway propagation interval. Box-Behnken design and ANOVA are adopted to optimize parameter. 3 parameters are optimized by RSM to obtain the shortest TRPI. An ANN-based method is proposed to predict the temperature variations of two cells. Verification datasets are employed to validate the accuracy of the ANN model.