A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity. (February 2018)

• Record Type: Journal Article
• Authors: Chen, Lin; Lü, Zhiqiang; Lin, Weilong; Li, Junzi; Pan, Haihong
• Is Part Of: Measurement. Volume 116(2018)
• Page Start: 586
• Page End: 595
• Publication Date: 2018-02
• Subjects: Lithium-ion battery -- State-of-health -- Online estimation -- Ohmic internal resistance -- Capacity -- Correlation analysis; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2017.11.016
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The correlation between ohmic internal resistance and capacity is evaluated. The initial and final ohmic internal resistances are estimated. Battery State of Health is estimated by the definition based on ohmic internal resistance. Estimation performs well on different batteries. Abstract: For secure and reliable operation of lithium-ion batteries in electric vehicles, diagnosis of the battery degradation is essential. This can be achieved by monitoring the increase of the internal resistance of the battery cells over the whole lifetime of the battery. In this paper, a method to estimate state of health (SoH) is presented through the established linear relationship between ohmic internal resistance and capacity fade. Firstly, the Thevenin model and the recursive least squares (RLS) algorithm are applied to simulate battery dynamic characteristics and identify model parameters, respectively. Secondly, based on the established linear relationship between ohmic internal resistance and capacity fade, both ohmic internal resistances at the start and the end of the battery's lifetime are estimated by only two random discharge cycles at different aging stages. Finally, an online SoH estimator is formulated and applied to estimate the SoH of a battery's remaining cycles. In addition, a series of experiments were carried out based on dynamic loading to verify the proposed method. The SoH estimates indicate that the evaluated maximum SoH errors are within ±4%. The proposed SoH estimation method is consistent with the measurement data of the battery and shows good results with very low computational effort.