摘要
为准确估算锂离子电池非线性退化过程中的健康状态(SOH),提出融合自适应变异粒子群优化器和高斯过程回归的AMPSOGPR算法。首先提取欧姆内阻增量和电压样本熵作为电池退化表征量,然后引入自适应变异粒子群(AMPSO)优化高斯过程回归(GPR)核函数的超参数,构建基于AMPSOGPR的SOH估算框架,用提取的退化表征量实现SOH估算;最后,通过对比AMPSOGPR采用不同核函数时SOH估算结果,得到最优核函数。实验结果表明,AMPSOGPR算法可以有效地估算电池SOH,且最大估算误差不超过2.08%。
In order to accurately estimate the state of health(SOH)of lithiumion battery in the process of nonlinear degradation,an AMPSOGPR algorithm is proposed by the fusion of adaptive mutation particle swarm optimizer(AMPSO)with gaussian process regression(GPR).Firstly,the increment of ohmic internal resistance and the sample entropy of voltage are extracted as degradation characterization indicators.Then AMPSO is introduced to optimize the hyperparameters of GPR kernel function,an SOH estimation framework based on AMPSOGPR is constructed,and the degradation characterization indicators are extracted to perform SOH estimation.Finally,by comparing the results of SOH estimation using AMPSOGPR with different kernel functions,the optimal kernel function is obtained.The results of experiment indicate that the AMPSOGPR algorithm can effectively estimate the SOH of battery with a maximum absolute estimation error not more than 2.08%.
作者
陈琳
刘博豪
丁云辉
吴淑孝
冯喆
潘海鸿
Chen Lin;Liu Bohao;Ding Yunhui;Wu Shuxiao;Feng Zhe;Pan Haihong(School of Mechanical Engineering,Guangxi University,Nanning 530004)
出处
《汽车工程》
EI
CSCD
北大核心
2021年第10期1472-1478,共7页
Automotive Engineering
基金
国家自然科学基金(52067003,51667006)资助。
关键词
锂离子电池
健康状态
高斯过程回归
自适应变异粒子群优化器
核函数
lithiumion battery
stateofhealth
gaussian process regression
adaptive mutation particle swarm optimizer
kernel functions
