改进PSO优化ELM预测锂离子电池剩余寿命

针对极限学习机在预测锂离子电池剩余寿命过程中的不稳定性,提出利用混合粒子群优化算法对极限学习机预测模型优化的方法。通过改进的粒子群优化算法对极限学习机的输入端进行寻优处理,不但能够使模型的预测精度有进一步提高,而且大大增加了锂离子电池单次剩余寿命预测结果的可信度。利用NASA PCoE公开的锂离子电池数据进行仿真实验并评估该模型的预测性能,然后与标准的极限学习机预测模型预测结果进行对比,统计结果表明该方法使预测误差控制在2%左右。

Improved PSO optimized extreme learning machine predicts remaining useful life of lithium-ion battery

For the instability of the extreme learning machine in predicting the remaining useful life of lithium ion batteries, this paper proposes a hybrid particle swarm optimization algorithm to optimize the prediction model of extreme learning machines.The optimized particle swarm optimization algorithm is used to optimize the input of the extreme learning machine, which not only can significantly improve the prediction accuracy of the model, but also greatly increase the credibility of the single prediction result of the remaining useful life lithium ion battery.In this paper, the lithium ion battery data published by NASA PcoE is used to carry out simulation experiments and evaluate the prediction performance of the model, and compare it with the prediction results of standard extreme learning machine prediction model.The statistical results show that the prediction error is controlled by about 2%.