A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging
Journal article, 2019

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.

Model migration

Battery management system

Lithium-ion batteries

Bayes Monte Carlo method


Xiaopeng Tang

Hong Kong University of Science and Technology

Yujie Wang

University of Science and Technology of China

Changfu Zou

Chalmers, Electrical Engineering, Systems and control

Ke Yao

Guangzhou HKUST Fok Ying Tung Research Institute

Yongxiao Xia

Guangzhou HKUST Fok Ying Tung Research Institute

Furong Gao

Hong Kong University of Science and Technology

Guangzhou HKUST Fok Ying Tung Research Institute

Energy Conversion and Management

0196-8904 (ISSN)

Vol. 180 162-170

Driving Forces

Sustainable development

Subject Categories

Other Engineering and Technologies not elsewhere specified

Other Chemical Engineering

Electrical Engineering, Electronic Engineering, Information Engineering



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