电子测量与仪器学报

丁阳征,贾建芳.改进PSO优化ELM预测锂离子电池剩余寿命[J].电子测量与仪器学报,2019,33(2):72-79

改进PSO优化ELM预测锂离子电池剩余寿命

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

DOI：

英文关键词:lithium ion battery remaining useful life stability hybrid PSO ELM

基金项目:国家自然科学基金（61573250）、山西省青年自然科学基金（201601D021075）、山西省回国留学人员科研项目（2015 083）资助

作者	单位
丁阳征	1.中北大学电气与控制工程学院
贾建芳	1.中北大学电气与控制工程学院

Author	Institution
Ding Yangzheng	1.School of Electrical and Control Engineering, North University of China
Jia Jianfang	1.School of Electrical and Control Engineering, North University of China

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中文摘要:

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。

英文摘要:

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%.

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