Estimation of Battery State⁃of⁃Health Using Particle Swarm Optimization with Gauss Process Regression

被引：0
|
作者：
Chen L. [1 ]
Liu B. [1 ]
Ding Y. [1 ]
Wu S. [1 ]
Feng Z. [1 ]
Pan H. [1 ]
机构：
[1] School of Mechanical Engineering, Guangxi University, Nanning
来源：
Pan, Haihong (hustphh@163.com) | 1600年 / SAE-China卷 / 43期
关键词：
Adaptive mutation particle swarm optimizer; Gaussian process regression; Kernel functions; Lithium⁃ion battery; State⁃of⁃health;
D O I：
10.19562/j.chinasae.qcgc.2021.10.008
中图分类号：
学科分类号：
摘要：
In order to accurately estimate the state of health (SOH) of lithium⁃ion battery in the process of nonlinear degradation, an AMPSO⁃GPR 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 AMPSO⁃GPR is constructed, and the degradation characterization indicators are extracted to perform SOH estimation. Finally, by comparing the results of SOH estimation using AMPSO⁃GPR with different kernel functions, the optimal kernel function is obtained. The results of experiment indicate that the AMPSO⁃GPR algorithm can effectively estimate the SOH of battery with a maximum absolute estimation error not more than 2.08%. © 2021, Society of Automotive Engineers of China. All right reserved.
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页码：1472 / 1478
页数：6
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