Research on thermal runaway early warning algorithm of lithium battery based on improved particle swarm algorithm optimized BP neural network

• Published in: Journal of physics. Conference series Vol. 2816; no. 1; pp. 12032 - 12037
• Main Authors: Yan, Peng, Li, Shaopeng, Wang, Xiao, Cao, Zhenkun
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.08.2024
• Subjects: Adaptive algorithms; Alarm systems; Algorithms; Back propagation networks; Early warning systems; False alarms; Fire alarm systems; Fire alarms; Fire prevention; Lithium batteries; Machine learning; Neural networks; Particle swarm optimization; Thermal runaway
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/2816/1/012032

In response to the issues of false alarms and missed alarms in traditional fire alarm systems for energy storage station fire prevention and control, this study proposes a fire alarm approach employing improved PSO-BP neural networks. Firstly, we set adaptive inertial weights and asymmetric learning factors on the PSO algorithm that are automatically optimized as fitness changes to improve the optimization accuracy and convergence speed of the algorithm. Secondly, the thresholds or weights are mapped to random particles for training to obtain the optimal value. Finally, utilizing the Pyrosim software, a model of the battery cabinet was established, and the thermal runaway data samples were input into the neural network model for training. The test consequence indicates that the improved PSO-BP model achieved a 5% and 3.75% rise in precision relative to the neural network and PSO-BP model, respectively, while the frequency of iterations declined by 55% relative to the PSO-BP.