电子测量与仪器学报

何畏,罗潇,曾珍,黄飞扬,徐杨.利用 QPSO 改进相关向量机的电池寿命预测[J].电子测量与仪器学报,2020,34(6):18-24

利用 QPSO 改进相关向量机的电池寿命预测

Battery life prediction based on QPSO improved relevant vector machine

DOI：

英文关键词:lithium-ion battery remaining useful life stability relevance vector machine quantum particle swarm optimization

基金项目:四川省科技支撑计划(2015GZ0159)、四川省科技支撑计划(2014GZ0153)资助项目

作者	单位
何畏	1.西南石油大学机电工程学院
罗潇	1.西南石油大学机电工程学院
曾珍	1.西南石油大学机电工程学院
黄飞扬	1.西南石油大学机电工程学院
徐杨	1.西南石油大学机电工程学院

Author	Institution
He Wei	1.School of Mechanical and Electrical Engineering, Southwest Petroleum University
Luo Xiao	1.School of Mechanical and Electrical Engineering, Southwest Petroleum University
Zeng Zhen	1.School of Mechanical and Electrical Engineering, Southwest Petroleum University
Huang Feiyang	1.School of Mechanical and Electrical Engineering, Southwest Petroleum University
Xu Yang	1.School of Mechanical and Electrical Engineering, Southwest Petroleum University

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中文摘要:

锂离子电池作为系统供能的关键部分, 其寿命终结往往导致用电设备的性能下降或故障,甚至整个系统的崩溃。因此, 研究电池剩余使用寿命(RUL),提前预知失效时间,显得日趋重要。针对锂离子电池寿命预测过程中训练时间较长、参数确定困难、输出结果不稳定等问题,提出了利用运用泛化能力更好,更稀疏,测试时间更短,更适用于在线检测的相关向量机 (RVM)进行预测,并通过量子粒子群对相关向量机进行了优化,保证了预测输出结果的稳定性。分析结果表明,量子粒子群算法改进后的相关向量机对锂电池失效时间的预测准确度高达 99%,电池寿命预测的绝对误差平均值 2%,均方根误差 3%,验证了该改进算法的可行性和优越性。

英文摘要:

As a key part of system energy supply, the end of life of lithium-ion battery often leads to the degradation or failure of the electrical equipment, or even the collapse of the whole system. Therefore, it is increasingly important to study the remaining useful life (RUL) of the battery and predict the failure time in advance. Aiming at the problems of long training time, difficult parameter determination and unstable output results during the life prediction process of lithium-ion batteries, this paper puts forward the Relevance Vector Machine (RVM) which is more suitable for on-line detection and has better generalization ability, sparser parameter and shorter test time, and optimizes relevance vector machine (RVM) through quantum particle swarm optimization (QPSO) to ensure the stability of predicted output results. The results show that the prediction accuracy of the improved relevance vector machine (QPSO-RVM) is up to 99%, the mean absolute error of battery life prediction is about 2% and the root mean square error is about 3%, which verifies the improved algorithm feasibility and superiority.

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