Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles

Xiaoyu Zhao; Zuolu Wang; Eric Li; Haiyan Miao

doi:10.37965/jdmd.2024.475

Authors

Xiaoyu Zhao Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK https://orcid.org/0000-0003-2574-2791
Zuolu Wang Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK https://orcid.org/0000-0003-0076-613X
Eric Li School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, TS1 3BX, UK
Haiyan Miao Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK https://orcid.org/0000-0001-8125-3111

DOI:

https://doi.org/10.37965/jdmd.2024.475

Keywords:

Electric Vehicles, Lithium-ion battery;, Rapid capacity estimation;, Electrochemical impedance spectroscopy;, Pulse impedance technique;

Abstract

With the dramatic increase in electric vehicles (EVs) globally, the demand for lithium-ion batteries has grown dramatically, resulting in many batteries being retired in the future. Developing a rapid and robust capacity estimation method is a challenging work to recognize the battery ageing level on service and provide regroup strategy of the retied batteries in secondary use. There are still limitations on the current rapid battery capacity estimation methods, such as direct current internal resistance (DCIR) and electrochemical impedance spectroscopy (EIS), in terms of efficiency and robustness. To address the challenges, this paper proposes an improved version of DCIR, named pulse impedance technique (PIT), for rapid battery capacity estimation with more robustness. First, PIT is carried out based on the transient current excitation and dynamic voltage measurement using the high sampling frequency, in which the coherence analysis is used to guide the selection of a reliable frequency band. The battery impedance can be extracted in a wide range of frequency bands compared to the traditional DCIR method, which obtains more information on the battery capacity evaluation. Second, various statistical variables are used to extract ageing features and Pearson correlation analysis is applied to determine the highly correlated features. Then a linear regression model is developed to map the relationship between extracted features and battery capacity. To validate the performance of the proposed method, the experimental system is designed to conduct comparative studies between PIT and EIS based on the two 18650 batteries connected in series. The results reveal that the proposed PIT can provide comparative indicators to EIS, which contributes higher estimation accuracy of the proposed PIT method than EIS technology with lower time and cost.

Conflict of Interest Statement

The authors declare no conflicts of interest.