A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging

By Xiaopeng Tang, Yujie Wang, Changfu Zou, K. Yao, Yongxiao Xia, Furong Gao. Energy Conversion and Management, 2019

175 citations recorded by the source corpus.

Abstract Temperature and cell aging are two major factors that influence the reliability and safety of Li-ion batteries. A general battery model considering both temperature and degradation is often difficult to develop, given the fact that there are many different types of cells with different shapes and/or internal chemical components. In response, a migration-based framework is proposed in this paper for battery modeling, in which the effects of temperature and aging are treated as uncertainties. An accurate model for a fresh cell is established first and then migrated to the degraded batteries through a Bayes Monte Carlo method. Experiments are carried out on both LiFePO4 batteries and Li(Ni1/3Co1/3Mn1/3) O2 batteries under various ambient temperatures and aging levels. The results indicate that the typical voltage prediction error can be limited within ± 20 mV, for the cases of temperature change up to 40  ° C, and capacity degradation up to 20%. The proposed method paves ways to an effective battery management and energy control for electric vehicles or micro grid applications.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/737e65d451c9818dc1f1d87c80bd15a0c6c94481