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.
Bibliographical noteFunding Information:
This work is supported partly by the National Natural Science Foundation of China (Grant No. 61433005 and 61803359 ), partly by Guangdong Scientific and Technological Project (Grant No. 2017B010120002 ), and partly by CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellow (Grant No. 2017LH007 ).
© 2018 Elsevier Ltd
- Battery management system
- Bayes Monte Carlo method
- Lithium-ion batteries
- Model migration