Model-based State-of-energy Estimation of Lithium-ion Batteries in Electric Vehicles. (June 2016)

• Record Type: Journal Article
• Authors: Wang, Yujie; Zhang, Chenbin; Chen, Zonghai
• Is Part Of: Energy procedia. Volume 88(2016)
• Page Start: 998
• Page End: 1004
• Publication Date: 2016-06
• Subjects: Electric vehicle -- Battery modeling -- State-of-energy -- EKF.; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2016.06.125
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: With the increasing application of lithium-ion batteries, the function of battery management system (BMS) comes to be more sophisticated. The state-of-energy (SOE) of lithium-ion batteries is a critical index for energy optimization and management in electric vehicles. The conventional power integral methods are easy to cause accumulated error due to current or voltage drift of sensors. Therefore the EKF method is employed in this study. A data-driven model is established to describe the relationship between the open-circuit voltage (OCV) and SOE based on the experimental data of a Li(Ni1/3 Co1/3 Mn1/3 )O2 battery. The dynamic urban driving schedule of Wuhui city in China has been conducted on the lithium-ion battery to verify the accuracy of the proposed method. The results show that accurate SOE estimation results can be obtained by the proposed method.