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.
作者
郑雪莹
邓晓刚
曹玉苹
Zheng Xueying;Deng Xiaogang;Cao Yuping(College of Information and Control Engineering,China University of Petroleum(East China),Qingdao 266000,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
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。
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
Gaussian process regression
empirical mode decomposition
capacity recovery phenomena
lithium-ion battery
state of health
