Research on Temperature Prediction Model for Sodium Sulfur Battery Disassembly Tools Based on Improved SSA-BP Neural Network
Sodium sulfur batteries contain a large amount of high-purity sodium,which leads safety risks such as combustion and explosion during the automated disassembly process.A modified SSA-BP neural network algorithm was proposed to predict the maximum temperature of tool processing in response to the safety issues of sodium sulfur batteries during turning and disassembly.The real-time temperature of tool machining was calculated using ABAQUS software,and the reliability of the simulation data was verified through bat-tery disassembly experiments.Then,samples were established based on simulated temperature data,and the SSA-BP neural network al-gorithm was optimized using Tent chaotic mapping to establish a tool temperature simulation prediction model.The experimental results show that the simulation prediction model has fast convergence speed,strong robustness,and small model error.
万方数据
维普
