The surge in electric vehicle adoption has resulted in a significant rise in end-of-life batteries, which are unsuitable for demanding EV applications. Repurposing these batteries for secondary applications presents a promising avenue to tackle environmental and economic challenges associated with their disposal. The second-life battery (SLB) approach emerges as a mechanism to manage this massive amount of retired EV batteries. However, this approach poses significant challenges in determining and monitoring battery degradation and performance. After evaluating different scenarios for reusing or recycling retired EV batteries, this paper examines the main challenges associated with SLBs, including techno-economic aspects, uncertainty from first life, safety, characterization and screening, battery-management systems, and secondary applications. A comprehensive review of current state-of-the-art SLB research and implementations is provided, particularly emphasizing battery characterization and the requisite evaluation processes for SLB eligibility. This paper explores diverse measurement techniques for assessing SLB performance, evaluating them based on accuracy, complexity, and time consumption, which are essential for achieving cost-effective SLB applications. The overarching objective is to thoroughly understand the principal challenges associated with repurposing EV batteries and delineate the research imperatives necessary for their successful implementation and prolonged lifespan.

Li-ion batteries' complex and dynamic electrochemical behavior pose a significant challenge for their diagnostics and prognostics. Electrochemical Impedance Spectroscopy (EIS) is a valuable measurement technique that provides crucial insight into battery behavior. Transforming the EIS impedance data across various frequencies into equivalent electrical circuit elements tailored to the battery's physical and chemical characteristics is valuable for interpreting this data. Distribution of Relaxation Times (DRT) analysis is such an approach and results in a distribution of time constants that effectively characterize the RC networks of the battery's equivalent circuit model (ECM). Modeling the battery with detailed consideration of EIS data and minimizing the information that is lost in this modeling stage is the crucial advantage of the DRT technique. In this work, we present a fully automated procedure for modeling a substantial volume of EIS data using DRT. This involves the automatic selection of prominent peaks that persist throughout the entire battery aging process, effectively enhancing computational efficiency. It furthermore includes data pre-processing steps and frequency range selection, making DRT analysis more efficient and robust by employing the least squares fitting (LSQF) algorithm. The findings in applying our procedure to published EIS spectra of Li-ion battery cells indicate that the presented approach yields a high level of precision in tracking the ECM parameters to analyse the degradation of these cells. Further proof of the quality of our procedure lies in the consistent low dispersion of the various fits, which remain consistent across the entire dataset.

The time-varying and complex electrochemical behaviour inherent to Li-ion batteries cause a great challenge to its diagnostics and prognostics. Electrochemical impedance spectroscopy (EIS) based mapping of the impedance datasets as a function of frequency to equivalent electrical elements specific to physical/chemical characteristics is vital to validate the equivalent circuit models (ECM). Distribution of relaxation times (DRT) is a method to convert the EIS impedance spectra as a function of frequency to a distribution of time constants representing an RC network in a battery ECM. Modeling the battery with detailed consideration of EIS data is the main advantage of the DRT technique. In this work, the whole process of modeling a large amount of EIS data has been done automatically, selecting only the dominant peaks present during the entire battery ageing to increase computational efficiency. The results show that good accuracy is achieved in monitoring the degradation of a Li-ion cell and that the dispersion of the different fits is consistent throughout the data set.