The Lithium (Li)-ion batteries in Electric Vehicles reach their End of Life (EoL) when their capacity degrades by twenty percent. Circular economy suggests to re-purpose these EoL Li-ion batteries in less demanding applications. In the case of re-purposing, there are multiple second-life applications of a product and it is important to know the State of Health (SoH) in prior to sorting the product efficiently into the above-said applications. In this paper, we propose a data driven method for SoH estimation of Li-ion batteries. The correlation was learnt using three different machine learning models namely Linear Regression, Support vector regression, and Feed-forward Neural network. A use case is created on the NASA AMES open source battery data. The accuracy of the different models has been compared using the indicator of Root mean square error. The result concluded that the feed-forward neural network has higher accuracy compared to the other two models employed.

The global market for End of Life Lithium-Ion Batteries is growing exponentially to satisfy the needs of electric mobility and clean energy technologies. Reusing or repurposing these batteries could ensure sustainability and keep the excessive demands of raw materials in check. However, a strong commitment and trust from the various stakeholders is necessary to build such circular industrial systems. In this paper, a thorough analysis of practices and regulations allowed us to highlight the actors and processes involved in the life cycle of repurposed Lithium-Ion Batteries (LIBs). This led us to propose a conceptual framework describing a generic organization for LIB repurposing. LIB State of Health and diagnosis estimations, which can be carried out with data from LIB passports, are also underlined as essential for the functioning of the organization. This was discussed and implemented on a real case of LIBs used as a power source for electric heaters after their first life in a mobility application.

The paper deals with the definition of procedure that enables one to determine, for a given plant, if all faults can be detected and located after a finite sequence of observable events. More formally, the diagnosability is the property that every fault can be correctly detected from the observable events of the system after its occurrence no later than a bounded number of events. In this paper, the diagnosability problem of Discrete Event Systems (DESs) is studied. As modeling tool, finite-state automaton in an event-based framework is used. A necessary and sufficient condition of diagnosability of such systems is proposed. The results proposed in this paper allow checking the diagnosability of discrete event systems in an efficient way, i.e. in polynomial time.