基于能量加权高斯过程回归的锂离子电池健康状态预测  

State of health prediction of lithium-ion batteries based on energy-weighted Gaussian process regression

作　　者：郑雪莹 邓晓刚[1] 曹玉苹[1] Zheng Xueying;Deng Xiaogang;Cao Yuping(College of Information and Control Engineering,China University of Petroleum(East China),Qingdao 266000,China)

机构地区：[1]中国石油大学(华东)信息与控制工程学院,青岛266000

出　　处：《电子测量与仪器学报》2020年第6期63-69,共7页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(61403418,21606256);中央高校基本科研业务费专项资金(17CX02054);山东省重点研发计划(2018GGX101025);山东省自然科学基金(ZR2016FQ21,ZR2016BQ14)资助项目。

摘　　要：针对容量再生现象影响锂离子电池健康状态预测(SOH)建模精度的问题,提出一种经验模态分解(EMD)的能量加权高斯过程回归(EWGPR)方法。该方法将容量再生现象看作是锂离子电池容量衰减过程的能量凸现,利用EMD分解获得样本的能量分布情况,根据能量情况计算每个样本的权重,进而建立基于能量加权高斯过程回归的锂离子电池SOH预测模型。基于NASA锂电池数据集的仿真实验结果表明,EWGPR方法比基本GPR算法具有更高的精度和适应性,单步预测和多步预测的均方根误差(RMSE)分别减少了3%和10%。Aiming at the problem that the capacity regeneration phenomenon affects the state of health(SOH)prediction accuracy of lithium-ion batteries,an energy-weighted Gaussian process regression(EWGPR)of empirical mode decomposition(EMD)method is proposed.This method regards the capacity recovery phenomenon as the energy projection of the capacity decay process of lithium-ion battery.The energy distribution is obtained by EMD decomposition and the sample weights are calculated according to the energy distributions.Then the SOH prediction model of lithium-ion battery based on EWGPR is established.The experimental simulation results on the NASA lithium-ion battery datasets show that the EWGPR algorithm has higher accuracy and adaptability than the basic GPR algorithm,and the root mean square error(RMES)for single-step and multi-step predictions are decreased by more than 3%and 10%,respectively.

关 键 词：高斯过程回归 经验模态分解 容量再生现象 锂离子电池 健康状态 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置] TN081[自动化与计算机技术—控制科学与工程]