Diagnosis and remaining useful life prediction of end-of-life battery systems for second-life applications

The realization of a circular economy for lithium-ion batteries demands technological advancements capable of performing diagnosis on end-of-life batteries. This will ensure improvement of the life cycle performance of battery systems, considering the most diverse value chains. A key challenge is considering the variety of battery conditions at their end-of-life and the multiple reuse options in second-life applications. This paper presents the different approaches of software-based State of Health (SOH) determination and remaining useful life (RUL) prediction on battery cells. This includes the use and performance comparison of classical regression methods, machine learning regression methods and physical battery models in determining the SOH and degradation process of battery cells. In addition, we consider the resulting RUL conditions of typical second-life applications of electric vehicles (EVs), forklifts, grid stabilisation storage systems, and home storage systems. These results enable the selection and use of electrical and software characterisation methods for efficient battery management diagnostics and end-of-life characterisation.