Comparison of data-driven lithium battery state of health estimation methods

doi:10.12028/j.issn.2095-4239.2019.0103

Abstract

Abstract: This paper reviews the research progress of three main data-driven methods, artificial neural network, support vector regression and Gaussian process regression, in the estimation of state of health (SOH). Artificial neural network is suitable for simulating power batteries and can achieve high precision. Support vector regression has a small amount of calculation and perfect theoretical foundation. It is widely used in the research of power battery SOH estimation. The Gaussian process has high regression accuracy and can give a confidence interval for the prediction results. In recent years, the number of related literatures shows an increasing trend. In view of the shortcomings of the current SOH definition that fail to reflect the rated voltage decay of lithium-ion batteries, it is proposed to define SOH by using battery full charge energy. In this paper, BP neural network, support vector regression and Gaussian process regression model are established respectively. The new energy vehicle big data is used to predict the battery charging energy. Quantitative comparison results verify the characteristics of the three methods in terms of calculation volume and accuracy. Finally, the application prospects of data-driven methods and new energy vehicle big data in power battery SOH estimation research are prospected.

Key words: power battery, state of health, data-driven methods, new energy vehicle big data

CLC Number:

CHEN Yi, BAI Yunfei, HE Ying. Comparison of data-driven lithium battery state of health estimation methods[J]. Energy Storage Science and Technology, 2019, 8(6): 1204-1210.

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