Aging mechanism diagnosis of lithium ion battery by open circuit voltage analysis. (1st December 2020)

• Record Type: Journal Article
• Authors: Pan, Bin; Dong, Dong; Wang, Jionggeng; Nie, Jianbo; Liu, Shuangyu; Cao, Yaohe; Jiang, Yinzhu
• Is Part Of: Electrochimica acta. Volume 362(2020)
• Publication Date: 2020-12-01
• Subjects: Lithium ion battery -- State of health -- Degradation mode -- Diagnostic method; Electrochemistry -- Periodicals; Electrochemistry, Industrial -- Periodicals; 541.37
• DOI: 10.1016/j.electacta.2020.137101
• Languages: English
• ISSNs: 0013-4686

Abstract

Highlights: A new lithium ion battery aging modelling and diagnosing method is proposed based on open-circuit voltage (OCV) analysis. Two-stage segmented nonlinear regression algorithm is utilized to smooth incremental capacity (IC) curves for the reconstruction of universal OCV curves. The model shows a satisfied accuracy and wide applicability through a case study of a commercial battery. Abstract: The whole life aging behavior and degradation mechanism of lithium ion battery (LIB) are critical to ensure the stability and reliability during practical operation. In this work, a new LIB aging modelling and diagnosing method is proposed based on open circuit voltage (OCV) analysis, through a two-stage segmented nonlinear regression algorithm to smooth incremental capacity (IC) curves for the reconstruction of universal OCV curves. Such algorithm can well reserve the chemical features of the IC curves, which enables the quantification of the loss of active materials and lithium inventory in a nondestructive manner with this OCV model-based diagnostic method. A case study of a commercial LiFePO4 (LFP) battery shows a satisfied accuracy of the model, realizing a quick identification of aging behavior and capacity degradation modes, as well as the parameter recognition of the battery internal resistance components.