期刊
APPLIED SCIENCES-BASEL
卷 8, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/app8081301
关键词
state of health estimation; adaptive neuron-fuzzy inference system (ANFIS); group method of data handling (GMDH); artificial neural network (ANN); electric vehicles (EVs); capacity degradation; lithium-ion battery; time-delay input
In this article, two techniques that are congruous with the principle of control theory are utilized to estimate the state of health (SOH) of real-life plug-in hybrid electric vehicles (PHEVs) accurately, which is of vital importance to battery management systems. The relation between the battery terminal voltage curve properties and the battery state of health is modelled via an adaptive neuron-fuzzy inference system and a group method of data handling. The comparison of the results demonstrates the capability of the proposed techniques for accurate SOH estimation. Moreover, the estimated results are compared with the direct actual measured SOH indicators using standard tests. The results indicate that the adaptive neuron-fuzzy inference system with fifteen rules based on a SOH estimator has better performances over the other technique, with a 1.5% maximum error in comparison to the experimental data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据