Title :
A data-driven bias correction method based lithium-ion battery modeling approach for electric vehicles application
Author :
Xianzhi Gong ; Rui Xiong ; Mi, Chunting Chris
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Michigan, Dearborn, MI, USA
Abstract :
Due to the inconsistency and varied characteristics of lithium-ion battery cells, the battery pack modeling remains a challenging problem. To model the operation behaviors of each cell in the battery pack, considerable work effort and computation time is needed. This paper proposes a data-driven bias correction based lithium-ion battery modeling method, which can significantly reduce the computation work and remain good model accuracy.
Keywords :
electric vehicles; secondary cells; battery pack modeling; data-driven bias correction method; electric vehicle application; lithium-ion battery cell; lithium-ion battery modeling approach; Aging; Batteries; Computational modeling; Integrated circuit modeling; Mathematical model; Resistance; System-on-chip;
Conference_Titel :
Transportation Electrification Conference and Expo (ITEC), 2014 IEEE
Conference_Location :
Dearborn, MI
DOI :
10.1109/ITEC.2014.6861807