基于改进粒子群优化算法-粒子滤波模型的IGBT寿命预测方法研究OA

To enhance the prediction accuracy of insulated gate bipolar transistor(IGBT)lifetime,reduce maintenance costs,and mitigate system failure risks,a novel IGBT lifetime prediction method integrating the improved particle swarm optimization(IPSO)and particle filter(PF)is proposed.By selecting the collector-emitter on-state voltage(Vce_on)as the degradation characteristic parameter,and based on the publicly available historical Vce_on dataset from NASA,the degradation model is fitted using Matlab to determine model parameters,thereby constructing the state equation and observation equation.Adaptive weights and tangent functions are employed to optimize particle swarm optimization parameters,addressing the issues of premature convergence in the early stage and proneness to local optima in the later stage.An IPSO-PF model is established,where IPSO's optimal parameter search dynamically adjusts the particle weights in both the prediction phase and resampling phase of PF,enabling particles to better approximate the posterior probability distribution of the system.The failure threshold of Vce_on is set to achieve accurate IGBT lifetime prediction.Simulation analysis indicates that the average relative accuracy of the IPSO-PF model reaches 0.971 1,which is 20.44%,6.99%,and 5.37%higher than that of the PF,unscented Kalman particle filter(UPF),and hunter-prey optimizer particle filter(HPO-PF)models,respectively,which demonstrates that the IPSO-PF model can effectively enhance the accuracy of IGBT lifetime prediction.To verify the effectiveness of each improved module on the model,ablation experiments are designed,and the results confirm that each improved module has effectively promoted the performance of the IPSO-PF model.