The transport sector is one of the main contributors to greenhouse gas emissions worldwide. A current solution to this problem is the introduction of electric vehicles into our transport fleets. However, this solution comes with its challenges; for instance, the environmental impact of batteries once they reach their end-of-life. The circular economy is an approach to manage ecological electromobility issues while providing economic and societal wellbeing. A starting point for the implementation of a circular economy can be found in the implementation of circular business models. Because the circular economy requires its actors to think in systems, the collaboration between multiple stakeholders is essential. However, most circular business model frameworks are focusing on the focal firm, which is not sufficient to encourage cross-collaboration between organizations. Therefore, this study outlines the development of a new framework that focuses on a multi-stakeholder perspective. I propose the “framework for multi-stakeholder circular business model innovation” to explore win-win-win scenarios for the management of 2LB. I conducted a study in collaboration with Svealandstrafiken, Mälarenergi and Västerås Stad. The organizations were involved in data collection via a series of interviews and the validation of the framework through a workshop. The proposed framework consists of a process of four phases subdivided into eight steps that meant to address key challenges organizations are facing to achieve circularity. Within these four phases, the framework encourages discussions on shared values and visions between all stakeholders at an early stage. As a consequence, better collaborations and relationships are formed, which positively benefits the development of circular business models.

This thesis investigates the opportunities and challenges of using repurposed electric car batteries, so called Second-life Battery (SlB), in a residential building as an energy storage. The performance of SlBs is compared to a First-life Battery (FlB) by identifying two potential scenarios and using a battery degradation model. The first scenario involved the batteries providing ancillary services to the grid, while in the second scenario the batteries were used for peak shaving. The battery degradation model is based on typical usage from the scenarios. The thesis findings indicate that the SlB degrades at a slower rate than the FlB, but can perform fewer cycles. Economic performance varies based on the application and initial assumptions. Both batteries proved to be profitable in the two scenarios studied, with some of the SlB configurations outperforming FlB configurations and vice versa. The yearly compensation received, when providing ancillary services with a 1000 kWh battery, ranged between 3-8 MSEK, while the yearly compensation received, when peak shaving with a 200-300 kWh battery, ranged between 20-35 thousand SEK. The main challenges identified included reduced lifespan, security risks, potential price increases, and space constraints. On the other hand, the main opportunities identified included potential price decreases and sustainability benefits such as carbon footprint reduction and grid stability. These results may provide valuable insights for informed decision-making regarding investments in FlBs and SlBs on the Swedish market.

I Sverige läggs stort fokus på omställningen till ett hållbart och klimatneutralt samhälle, där avkarbonisering och elektrifiering av transportsektorn anses avgörande för att minska utsläpp av växthusgaser. En storskalig batteriproduktion i Sverige och i Europa medför nya problem kring hållbarhetsaspekter gällande utvinning av råvara som exempelvis litium, kobolt och nickel. Men även andra problem gällande avfallshanteringen av litiumjonbatterier. Återvinning och återbruk av litiumjonbatterier anses vara en viktig del i strävan mot en cirkulär batteriproduktion. Dock finns svårigheter med implementering av dessa processer på en storskalig marknad gällande ekonomiska, miljömässiga och tekniska aspekter. Syftet med arbetet är att kartlägga för- och nackdelar med återbruk av littiumjonbatterier i kontrast till återvinning av littiumjonbatterier som nått sitt förbruknings- (EoL - End of Life) tillstånd och identifiera viktiga aktörer för marknadsutökning av både återbruk och återvinning av litiumjonbatterier. En litteraturstudie gjordes tillsammans med en intervju med företaget Vattenfall. Genom detta identifierades ekonomiska, tekniska och miljömässiga aspekter gällande återvinning och återbruk av litiumjonbatterier.   Resultatet av rapporten visar att smarta automatiserade dataanalyser (smarta algoritmer som självständigt kan sortera olika typer av batterier, fatta beslut om batteriernas tillstånd och genom användning av sensorer kan mäta och kontrollera temperaturförändringar) krävs för att göra batteriåtervinning och återbruk effektivare, säkrare och mer ekonomiskt lönsamt.  Återbruk av litiumjonbatterier förlänger batteriets livslängd där metaller knyts till batterier under en längre tid och kan hjälpa till att minska flaskhalsproblematiken gällande ohållbar metallutvinning. Återbruk kan vara bättre än återvinning ur miljösynpunkt i exempelvis energilagringsapplikationer kopplade till förnybar energiproduktion. Litiumjonbatteriet står för en stor del av elbilens miljöpåverkan. Återbruk av elbilsbatterier i diverse applikationer kan därför göra att elbilens miljöpåverkan minskar då batteriets miljöpåverkan inte bara knyts till elbilen. Idag anses återvinning mer ekonomiskt lönsam och det finns fler aktörer och ekonomiska incitament för att stärka batteriåtervinning än för batteriåterbruk. / Sweden places a lot of focus on the transition to a sustainable and climate-neutral society, where decarbonisation and electrification of the transport sector are considered crucial to reducing emissions of GHG (greenhouse gas). Large-scale battery production in Sweden and in Europe brings new problems regarding waste management and the demand for raw materials used in the manufacturing of lithium-ion batteries. Recycling and reuse of lithium-ion batteries could be an important part of the effort towards a circular battery production chain. However, there are difficulties in implementing these on a large-scale market when it comes to financial, environmental and technical points of view. The purpose of this report is to map the pros and cons of reusing lithium-ion batteries in contrast to recycling the lithium-ion batteries that have reached their EoL (End of Life) and to identify important actors for both methods. A literature study was made as well as an interview with the company Vattenfall. Through this, economical, technical and environmental aspects regarding recycling and reuse of lithium-ion batteries were identified.  The results of the report show that automated data analysis (smart algorithms that can independently sort different types of batteries) is required to make battery recycling and second-life batteries more efficient, safer and more economically viable. The reuse of lithium-ion batteries has the possibility of extending the battery's lifetime and the metal usage and thereby reducing the bottleneck problem regarding unsustainable metal extraction. Second-life applications of LIBs can be better than recycling from an environmental point of view in, for example, energy storage applications linked to an increase in renewable energy. Reusing electric car batteries in other applications can reduce the electric car's environmental impact from a life cycle perspective. Today, there are more financial incentives to strengthen battery recycling than battery reuse.

With the constant evolution of Large Language Models (LLMs), methods for minimizing hallucinations are being developed to provide more truthful answers. By using Retrieval-Augmented Generation (RAG), external data can be provided to the model on which its answers should be based. This project aims at using RAG for a data extraction pipeline specified for second-life batteries. By pre-defining the prompts the user may only provide the documents that are wished to be analyzed, this is to ensure that the answers are in the correct format for further data processing. To process different document types, initial labeling takes place before more specific extraction suitable for the document can be applied. Best performance is achieved by grouping questions that allow the model to reason around what the relevant questions are so that no hallucinations occur. Regardless of whether there are two or three document types, the model performs equally well, and it is clear that a pipeline of this type is well suited to today's models. Further improvements can be achieved by utilizing models containing a larger context window and initially using Optical Character Recognition (OCR) to read text from the documents.