An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application. (1st June 2018)

• Record Type: Journal Article
• Authors: Xiong, Rui; Li, Linlin; Li, Zhirun; Yu, Quanqing; Mu, Hao
• Is Part Of: Applied energy. Volume 219(2018)
• Page Start: 264
• Page End: 275
• Publication Date: 2018-06-01
• Subjects: All-climate electric vehicles -- Aging states -- Battery manage system -- Electrochemical model -- Parameter identification -- Aging characteristic parameter; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.03.053
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: The electrochemical model has been proposed in this study. The finite analysis method and numerical computation method are used to solve PDEs. Five aging characteristic parameters are extracted to describe health state LiBs. The degradation trajectories of LiBs at different temperatures are built. An HIL test is conducted to verify the accuracy of the electrochemical model. Abstract: The Lithium-ion batteries (LiBs) are the core component of the all-climate electric vehicles. The aging state recognition is carried out based on the proposed electrochemical model (EM) instead of the traditional equivalent circuit model (ECM) and black boxes model in this paper. Firstly, a group of mathematical equations are built to describe the physical and chemical behaviors of batteries based on the electrochemical theory. Then, the finite analysis method and the numerical computation method are used to solve the mathematical equations and the model has been built. Next, the optimization algorithm is used for identifying the parameters of the model. The aging state recognition of the battery on whole lifetime is carrying out based on the ageing data. Five aging characteristic parameters are determined to describe the health state of the battery, and their degradation trajectories are obtained. Finally, a battery-in-loop approach is employed to verify the model based degradation recognition. Results show that the maximum voltage error is within 50 mV and the state of health estimation error is bounded to 3%.