Research on temperature prediction of lithium battery based on GA-Elman

Yunbo Zhang; Shuo Li

doi:10.1117/12.3015537

15 March 2024 Research on temperature prediction of lithium battery based on GA-Elman

Yunbo Zhang, Shuo Li

Proceedings Volume 13079, Third International Conference on Testing Technology and Automation Engineering (TTAE 2023); 130790J (2024) https://doi.org/10.1117/12.3015537
Event: 3rd International Conference of Testing Technology and Automation Engineering (TTAE 2023), 2023, Xi-an, China

Abstract

With the proposal of the "double carbon" goal, the traditional power system has gradually developed into a new power system with scenery as the main body, and with the change of energy structure and system form, the "renewable energy + energy storage" model will play an increasingly important role in the regulation and protection of the power system. However, with the increase of the installed scale of energy storage, it also brings safety problems. In recent years, the frequent fire accidents of energy storage power stations have brought resistance to the development of the industry. Aiming at the safety problem of energy storage power station, this paper analyzed the accident causes and fire development of energy storage power station, selected SOC(state of battery charge), discharge current, ambient temperature and other characteristic parameters as input parameters, combined with GA-Elman neural network algorithm, built a rapid prediction model of battery temperature, and verified the model. The simulation results show that this method can improve the prediction accuracy and shorten the prediction time. The system monitors the internal environment of the energy storage power station, identifies and judges the fire situation of the battery, and realizes a refined fire warning and management system of the energy storage power station.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yunbo Zhang and Shuo Li "Research on temperature prediction of lithium battery based on GA-Elman", Proc. SPIE 13079, Third International Conference on Testing Technology and Automation Engineering (TTAE 2023), 130790J (15 March 2024); https://doi.org/10.1117/12.3015537

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KEYWORDS

Fire

Neural networks

Batteries

Evolutionary algorithms

Data modeling

Lithium

Education and training

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