A large number of battery pack returns from electric vehicles (EV) is expected for the next years, which requires economically efficient disassembly capacities. This cannot be met through purely manual processing and, therefore, needs to be automated. The variance of different battery pack designs in terms of (non-) solvable fitting technology and superstructures complicate this. In order to realize an automated disassembly, a computer vision pipeline is proposed. The approach of instance segmentation and point cloud registration is applied and validated within a demonstrator grasping busbars from the battery pack. To improve the sorting of the battery pack components to achieve high-quality recycling after the disassembly, a labeling system containing the relevant data (e.g., cathode chemistry) about the battery pack is proposed. In addition, the use of sensor-based sorting technologies for peripheral components of the battery pack is evaluated. For this purpose, components such as battery pack and module housings of multiple manufacturers were investigated for their variation in material composition. At the current stage, these components are usually produced as composites, so that, for a high-quality recycling, a pre-treatment may be necessary.

As the market share of electric vehicles continues to rise, the number of battery systems that are retired after their service life in the vehicle will also increase. This large growth in battery returns will also have a noticeable impact on processes such as battery disassembly. The purpose of this paper is, therefore, to examine the challenges of the battery disassembly process in relation to the required increase in the degree of automation. For this purpose, a survey of various experts along the battery value chain was conducted, and product-side hurdles, such as the wide range of variants, and process-side challenges, such as the opening of the housing cover or the removal of cables and connectors, were identified. Together with an assessment of the potential degree of automation in the context of downstream processes (reuse, repair, remanufacturing, and recycling), this results in a variety of streams for future research in the field of automated battery disassembly. The core aspect in this context is data availability consisting of product and component data as well as process-relevant parameters.

Life cycle strategies for traction batteries, such as remanufacturing, reuse, and recycling of retired automotive lithium-ion batteries (LIBs), have received growing attention, as large volumes of LIBs will retire in the near future and the demand for LIBs continues to grow. At the same time, the relevance of the sustainability of a battery system over its entire life cycle is increasing as factors such as the EU Battery Regulation provide greater market and product transparency. As a result, research and industry require forecasts in order to assess the future market situation and to make well-founded decisions. Therefore, this paper provides forecasts of the return volumes of battery systems from BEVs and PHEVs up to 2035. Additionally, a representative European battery pack for PHEVs and BEVs was evaluated for each year since 2013, based on the ten vehicles with the largest market share in each year until 2021. In addition, the battery return streams are divided into three different 3R strategies based on expert interviews in order to evaluate the upcoming workload in these areas. The term “3R” refers to the sum of the currently existing pathways around reuse, remanufacturing, and recycling. In 2030, about 38.8 GWh will return and enter the recycling process annually. For battery reuse, about 13 GWh will return every year from 2030 onwards, ready to be used in stationary storage for energy transition. Compared to this, battery remanufacturing is expected to be supplied with a slightly lower volume of approximately 11 GWh per year.

As the production of battery cells experiences exponential growth and electric vehicle fleets continue to expand, an escalating number of traction batteries are nearing the conclusion of their operational life for mobility purposes, both presently and in the foreseeable future. Concurrently, the heightened interest in sustainable energy storage solutions has spurred investigations into potential second-life applications for aging traction batteries. Nonetheless, the predominant practice remains the removal of these batteries from electric vehicles, signifying the end of their life cycle, and their subsequent incorporation into recycling processes, with limited consideration for life-extending measures. This study seeks to elucidate the reasons behind the deprioritization of battery repurposing strategies. Therefore, the research team conducted two industry studies with over 20 battery experts from Europe, revealing concerns about the economic viability of repurposing batteries for stationary storage applications. A literature review of studies published since 2016 confirmed the industry’s struggles to address this issue theoretically. In conclusion, a research question was formulated, and a solution approach was delineated to assess the economic prospects of aged traction batteries within the industry’s landscape in the future. This solution approach encompasses pertinent market analysis, the identification of representative second-life applications, as well as the formulation of a methodology for evaluating the residual value of these batteries.

Battery disassembly forms a central jumping-off point for recycling in the context of a sustainable closure of the battery loop. The main objective for economic realization in line with European recycling regulations is therefore a transformation of the battery disassembly from a manual to an automated process. Product-related influences such as design variations and process-side constraints including the selection of disassembly technologies require large amounts of data for implementation in an automated system. This article examines accessible data sources in the literature and the upcoming battery passport to build a basis for a multi-layered methodical analysis of the data required for the automation of battery disassembly. For this purpose, the disassembly sequence and depth of an Audi e-tron battery pack are first identified using a priority matrix and converted into a product and process structure. Definitions for product- and process-related elements are established, and a generalized process model is developed, which is finally converted into a data structure model approach. The result shows that much of the required data to automate the disassembly of used batteries are currently not yet available. Further efforts must be made to establish data structures and standards regarding product- and process-related disassembly data.

Zusammenfassung: Mit der zunehmenden Marktdurchdringung von Lithium-Ionen-Batterien für verschiedene Anwendungsfelder wächst die Anforderung, den Energie- und Materialeinsatz für diese Schlüsselkomponenten der Elektromobilität so weit wie möglich zu reduzieren. In der Automobilindustrie stellen hohe Kosten für den Energiespeicher heute noch ein wesentliches Hindernis in der weiteren Marktdurchdringung von Elektrofahrzeugen dar, das es zu überwinden gilt. Als Kernkomponente des E-Mobils stellt die Lithium-Ionen-Batterie (kurz: LIB oder Batterie) nicht nur das im Vergleich zu Verbrennungsmotorautos teuerste Einzelbestandteil dar, sie bestimmt außerdem wesentlich die Reichweite sowie die abrufbare Leistung respektive das Ladeverhalten. Nach einer material- und anwendungsabhängigen Zeit beziehungsweise Zyklenzahl erreichen die im Elektrofahrzeug verbauten Batteriezellen einen Zustand, in dem sie den hohen Anforderungen im Auto nicht mehr gerecht werden. Für das Ende der First-Life-Anwendung werden üblicherweise 80 % der Ausgangskapazität der Batterien angenommen. Wie Abb. 39.1 verdeutlicht, wird bei aktuellen LIB mit bis zu 4000 Zyklen (abhängig von der Zellchemie) gerechnet, bevor diese Grenze erreicht wird.