Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression

Energies

Shuai Wang¹  Lingling Zhao²  Xiaohong Su²  Peijun Ma² 

[1] Department of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Nan Gang District, Harbin 150001, Heilongjiang, China;

关键词: lithium-ion batteries;    remaining useful life (RUL);    energy efficiency;    working temperature;    flexible support vector (SV);   

DOI  :  10.3390/en7106492

来源: mdpi

【 摘 要 】

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is important for battery management systems. Traditional empirical data-driven approaches for RUL prediction usually require multidimensional physical characteristics including the current, voltage, usage duration, battery temperature, and ambient temperature. From a capacity fading analysis of lithium-ion batteries, it is found that the energy efficiency and battery working temperature are closely related to the capacity degradation, which account for all performance metrics of lithium-ion batteries with regard to the RUL and the relationships between some performance metrics. Thus, we devise a non-iterative prediction model based on flexible support vector regression (F-SVR) and an iterative multi-step prediction model based on support vector regression (SVR) using the energy efficiency and battery working temperature as input physical characteristics. The experimental results show that the proposed prognostic models have high prediction accuracy by using fewer dimensions for the input data than the traditional empirical models.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.