Search Results (69)

Lithium-ion batteries (LiBs) are utilized in numerous applications due to advancements in technology, and the recovery of end-of-life (EoL) LiBs is imperative for environmental and economic reasons. Pyrometallurgical and hydrometallurgical methods have been used in the recovery of metals such as Li, Co, and Ni in the EoL LiBs. Hydrometallurgical methods, which have been demonstrated to exhibit higher recovery efficiency and reduced energy consumption, have garnered increased attention in recent research. Inorganic acids, including HCl, HNO₃, and H₂SO₄, as well as organic acids such as acetic acid and citric acid, are employed in the hydrometallurgical recovery of these metals. It is imperative to acknowledge the environmental hazards posed by these acids. Consequently, solvometallurgical processes, which involve the use of organic solvents with minimal or no water, are gaining increasing attention as alternative or complementary techniques to conventional hydrometallurgical processes. In the context of solvent systems that have been examined for a range of solvometallurgical methods, deep eutectic solvents (DESs) have garnered particular interest due to their low toxicity, biodegradable nature, tunable properties, and efficient metal recovery potential. In this study, the leaching process of black mass containing graphite, LCO, NMC, and LMO was carried out in a short time using the ternary DES system. The ternary DES system consists of choline chloride (ChCl), glycolic acid (GLY), and ascorbic acid (AA). As a result of the leaching process of cathode powders in the black mass without any pre-enrichment process, Li, Co, Ni, and Mn elements passed into solution with an efficiency of over 95% at 60 °C and within 1 h. Moreover, the kinetics of the leaching process was investigated, and Density Functional Theory (DFT) calculations were used to explain the leaching mechanism. Full article

This study analyses the greenhouse gas reduction potential of different end-of-life (EoL) strategies based on a case study of light electric vehicles (LEVs). Using a shared electric moped scooter as a reference, four EoL scenarios are evaluated in a comparative life cycle assessment (LCA). The modelling of the scenarios combines different R-strategies (e.g., recycling, reusing, and repurposing) regarding both the vehicle itself and the battery. German and EU regulations for vehicle and battery disposal are incorporated, as well as EU directives such as the Battery Product Pass. The global warming potential (GWP₁₀₀) of the production and EoL life cycle stages ranges from 644 to 1025 kg CO₂ eq among the four analysed scenarios. Landfill treatment led to the highest GWP₁₀₀, with 1.47 times higher emissions than those of the base scenario (status quo treatment following EU directives), while increasing component reuse and repurposing the battery cells achieved GWP₁₀₀ reductions of 2.8% and 7.8%, respectively. Overall, the importance of implementing sustainable EoL strategies for LEVs is apparent. To achieve this, a product design that facilitates EoL material and component separation is essential as well as the development of political and economic frameworks. This paper promotes enhancing the circularity of LEVs by combining the LCA of EoL strategies with eco-design considerations. Full article

The increasing adoption of electric vehicles (EVs) has led to a surge in end-of-life (EOL) lithium-ion batteries (LIBs), necessitating efficient recycling strategies to mitigate environmental risks and recover critical materials. This study compares the EV battery recycling frameworks in China and the United States, focusing on policy effectiveness, technological advancements, and material recovery efficiencies. China’s extended producer responsibility (EPR) policies and 14th Five-Year Plan mandate strict recycling targets, achieving a 40% battery recycling rate with 90% material recovery efficiency. Hydrometallurgical methods dominate, reducing energy consumption by 50% compared to virgin material extraction. The US, leveraging incentive-based mechanisms and private sector innovations, has a 35% recycling rate but a higher 95% resource recovery efficiency, mainly due to direct recycling and AI-based sorting technologies. Despite these advancements, challenges remain, including high recycling costs, inconsistent global regulations, and supply chain inefficiencies. To enhance sustainability, this study recommends harmonized international policies, investment in next-generation recycling technologies, and second-life battery applications. Emerging innovations, such as AI-driven sorting and direct cathode regeneration, could increase recovery efficiency by 20–30%, further reducing lifecycle costs. By integrating synergistic policies and advanced recycling infrastructures, China and the US can set a global precedent for sustainable EV battery management, driving the transition toward a circular economy. Future research should explore life cycle cost analysis and battery reuse strategies to optimize long-term sustainability. Full article

Electric vehicles are regarded as key players in reducing CO₂ emissions. However, managing the end-of-life (EoL) of lithium-ion batteries (LIBs) poses significant environmental and technical challenges. This presents a daunting task for governments, companies, and academics when discussing and developing initiatives for the EoL of LIBs. As more LIBs reach the end of their vehicular use, it becomes essential to identify key challenges. This research aims to analyze possible pathways, identify LIBs’ challenges in reaching the appropriate destinations, and propose actions to overcome these obstacles. Additionally, this study addresses those responsible for each challenge. A narrative review was employed as a methodological approach to achieve the proposed objectives, utilizing available literature on EoL LIB management. The research findings highlight various challenges, including safety, commercialization, and disassembly. To address these issues, this work recommends strategies such as extended producer responsibility, automation, and regulation. The study also emphasizes the necessity for a collaborative effort, particularly highlighting the key roles of government and industry in developing regulations, implementing effective waste management strategies, and driving market expansion, while academia contributes through research and technological advancements. The research contributes to a better understanding of sustainable LIB management, advocating for responsible disposal and reducing environmental and economic impacts. Full article

The growing demand for lithium-ion batteries (LIBs), driven by their use in portable electronics and electric vehicles (EVs), has led to an increasing volume of spent batteries. Effective end-of-life (EoL) management is crucial to mitigate environmental risks and prevent depletion of valuable raw materials like lithium (Li), cobalt (Co), nickel (Ni), and manganese (Mn). Sustainable, high-volume recycling and material recovery are key to establishing a circular economy in the battery industry. This paper investigates challenges and proposes innovative solutions for high-volume LIB recycling, focusing on automation for large-scale recycling. Key issues include managing variations in battery design, chemistry, and topology, as well as the availability of sustainable raw materials and low-carbon energy sources for the recycling process. The paper presents a comparative study of emerging recycling techniques, including EV battery sorting, dismantling, discharge, and material recovery. With the expected growth in battery volume by 2030 (1.4 million per year by 2040), automation will be essential for efficient waste processing. Understanding the underlying processes in battery recycling is crucial for enabling safe and effective recycling methods. Finally, the paper emphasizes the importance of sustainable LIB recycling in supporting the circular economy. Our proposals aim to overcome these challenges by advancing automation and improving material recovery techniques. Full article

The rapid expansion of lithium-ion batteries (LIBs), largely driven by the rising demand for electric vehicles, will lead to a significant increase in end-of-life (EOL) batteries, necessitating efficient recycling processes, which must be accompanied by equally efficient purification steps. This study addresses the challenge of reusing organic electrolyte solvents from spent LIBs, a key component often overlooked in existing recycling strategies. To address this issue, we developed a gas chromatography (GC) method. A variety of spent electrolyte samples of different origin, including mechanical-thermal pretreatment or direct cell recovery, were analyzed by quantification of common solvents and identified organic impurities. Results demonstrated that the composition of the recovered electrolytes was highly variable, with concentrations fluctuating. Impurities were identified, which may originate from various sources throughout the lifespan of an LIB and have the potential to reduce the performance of second-life LIBs by reusing the electrolyte without any purification. The findings highlight the necessity for advanced purification methods like a distillation process to remove these impurities and ensure the viability of recycled electrolytes in maintaining the performance and safety standards required for LIBs. This research contributes to the broader goal of enhancing the sustainability and reuse of battery materials. Full article

To comprehensively evaluate the current and future aging states of lithium-ion batteries, namely their State of Health (SOH) and Remaining Useful Life (RUL), this paper proposes a joint prediction method based on Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) networks. First, health features (HFs) are extracted from partial charging data. Subsequently, these features are fed into the GPR model for SOH estimation, generating SOH predictions. Finally, the estimated SOH values from the initial cycle to the prediction start point (SP) are input into the LSTM network in order to predict the future SOH trajectory, identify the End of Life (EOL), and infer the RUL. Validation on the Oxford Battery Degradation Dataset demonstrates that this method achieves high accuracy in both SOH estimation and RUL prediction. Furthermore, the proposed approach can directly utilize one or more health features without requiring dimensionality reduction or feature fusion. It also enables RUL prediction at the early stages of a battery’s lifecycle, providing an efficient and reliable solution for battery health management. However, this study is based on data from small-capacity batteries and does not yet encompass applications in large-capacity or high-temperature scenarios. Future work will focus on expanding the data scope and validating the model’s performance in real-world systems, driving its application in practical engineering scenarios. Full article

The transition to sustainable mobility is one of the most pressing and complex challenges for the automotive industry, with impacts that extend beyond the mere reduction of emissions. Electric vehicles, while at the center of this evolution, raise questions about the consumption of natural resources, such as lithium, copper, and cobalt, and their long-term sustainability. In addition, the introduction of advanced technologies, including artificial intelligence (AI) and autonomous systems, brings new challenges related to the management of components and materials needed for their production, creating a significant impact on supply chains. The growing demand for electric and autonomous vehicles is pushing the industry to rethink production models, favoring the adoption of circular economy principles to minimize waste and optimize the use of resources. To better understand the implications of this transition, this study adopts a multiple case study methodology, which allows in-depth exploration of different contexts and scenarios, and analysis of real cases of dismantling and recycling of internal combustion engines (ICEs) and electric vehicles (EVs). The research includes a financial simulation and a comparison of revenues from the dismantling of ICE and EV vehicles, highlighting differences in the value of recycled materials and the effectiveness of circular economy practices applied to the two types of vehicles. This approach provides a detailed overview of the economic benefits and challenges related to the management of the end of life of vehicles, helping to outline optimal strategies for a sustainable and cost-effective future in the automotive sector. Full article

In recent years, the rapid growth in electric vehicle ownership has resulted in a significant number of decommissioned traction batteries that will require recycling in the future. As consumer expectations for electric vehicle range continue to rise, the turnover of traction batteries has accelerated substantially. Consequently, there is an urgent need for electric vehicle manufacturers to establish an efficient, recyclable supply chain for the return of end-of-life (EOL) electric vehicle (EV) traction batteries. In this paper, we investigate the closed-loop recycling supply chain for retired power batteries in electric vehicle manufacturers, taking into account blockchain technology and the high range preferences in the electric vehicle market, which are influenced by varying demand for different levels of electric vehicle capacitance. Blockchain, as a distributed and decentralized technology, offers features such as consensus mechanisms, traceability, and security, which have been effectively applied across various fields. In this study, we construct four models involving EV battery manufacturers, EV retailers, and battery comprehensive utilization (BCU) enterprises participating in the recycling process. Through the analysis of a Stackelberg response model, we find that (1) single-channel recycling is less efficient than dual-channel recycling models, a difference driven by the diversity of recycling channels and the variability in recycling markets; (2) Recycling models incorporating blockchain technology demonstrate superior performance compared to those that do not utilize blockchain technology, particularly when the intensity of recycling competition is below 0.76; (3) Traction batteries integrated with blockchain technology exhibit higher recycling rates when the optimization index is below 0.96. Electric vehicle battery manufacturers must evaluate the benefits and costs of adopting blockchain technology; (4) With lower recycling incentive levels and EV range preferences, the single-channel recycling model yields better returns than the other three recycling models. EV manufacturers can enhance overall battery supply chain revenues by establishing varying incentive levels based on market demand for different capacitance levels. Full article

Remanufacturing, which benefits the environment and saves resources, is attracting increasing attention. Disassembly is arguably the most critical step in the remanufacturing of end-of-life (EoL) products. Human–robot collaborative disassembly as a flexible semi-automated approach can increase productivity and relieve people of tedious, laborious, and sometimes hazardous jobs. Task allocation in human–robot collaborative disassembly involves methodically assigning disassembly tasks to human operators or robots. However, the schemes for task allocation in recent studies have not been sufficiently refined and the issue of component placement after disassembly has not been fully addressed in recent studies. This paper presents a method of task allocation and sequence planning for human–robot collaborative disassembly of EoL products. The adopted criteria for human–robot disassembly task allocation are introduced. The disassembly of each component includes dismantling and placing. The performance of a disassembly plan is evaluated according to the time, cost, and utility value. A discrete Bees Algorithm using genetic operators is employed to optimise the generated human–robot collaborative disassembly solutions. The proposed task allocation and sequence planning method is validated in two case studies involving an electric motor and a power battery from an EoL vehicle. The results demonstrate the feasibility of the proposed method for planning and optimising human–robot collaborative disassembly solutions. Full article

This teaching aid aims to illustrate a range of the most common materials in end-of-life (EoL) lithium-ion batteries (LIBs) to demonstrate the usefulness of advanced organic petrography in the characterization of EoL LIB materials and to assess the efficiency of LIB recycling processes from the pre-processing stage up to the impurities of the metallurgical processes. Additionally, it may be useful for students, petrographers, and professionals in battery development and recycling. Full article

Disassembly is a key step in remanufacturing, especially for end-of-life (EoL) products such as electric vehicle (EV) batteries, which are challenging to dismantle due to uncertainties in their condition and potential risks of fire, fumes, explosions, and electrical shock. To address these challenges, this paper presents a robotic teleoperation system that leverages augmented reality (AR) and digital twin (DT) technologies to enable a human operator to work away from the danger zone. By integrating AR and DTs, the system not only provides a real-time visual representation of the robot’s status but also enables remote control via gesture recognition. A bidirectional communication framework established within the system synchronises the virtual robot with its physical counterpart in an AR environment, which enhances the operator’s understanding of both the robot and task statuses. In the event of anomalies, the operator can interact with the virtual robot through intuitive gestures based on information displayed on the AR interface, thereby improving decision-making efficiency and operational safety. The application of this system is demonstrated through a case study involving the disassembly of a busbar from an EoL EV battery. Furthermore, the performance of the system in terms of task completion time and operator workload was evaluated and compared with that of AR-based control methods without informational cues and ‘smartpad’ controls. The findings indicate that the proposed system reduces operation time and enhances user experience, delivering its broad application potential in complex industrial settings. Full article

The rapidly increasing adoption of electric vehicles (EVs) globally underscores the urgent need for effective management strategies for end-of-life (EOL) EV batteries. Efficient EOL management is crucial in reducing the ecological footprint of EVs and promoting a circular economy where battery materials are sustainably reused, thereby extending the life cycle of the resources and enhancing overall environmental sustainability. In response to this pressing issue, this review presents a comprehensive analysis of the role of artificial intelligence (AI) in improving the disassembly processes for EV batteries, which is integral to the practical echelon utilization and recycling process. This paper reviews the application of AI techniques in various stages of retired battery disassembly. A significant focus is placed on estimating batteries’ state of health (SOH), which is crucial for determining the availability of retired EV batteries. AI-driven methods for planning battery disassembly sequences are examined, revealing potential efficiency gains and cost reductions. AI-driven disassembly operations are discussed, highlighting how AI can streamline processes, improve safety, and reduce environmental hazards. The review concludes with insights into the future integration of electric vehicle battery (EVB) recycling and disassembly, emphasizing the possibility of battery swapping, design for disassembly, and the optimization of charging to prolong battery life and enhance recycling efficiency. This comprehensive analysis underscores the transformative potential of AI in revolutionizing the management of retired EVBs. Full article

Lithium-ion batteries are the most widely used as energy storage devices in electric mobility applications. However, due to complex electrochemical processes of battery degradation, it is challenging to predict accurately the battery end-of-life (EOL) to ensure their reliability, safety, and extended usage. In this context, the introduction of machine learning techniques can provide relevant solutions based on data collection and analysis. Indeed, we compared in this study the prediction performance of numerous machine learning approaches that predict if the battery EOL bypasses a predefined threshold. Based on the variation of different indicators during the first several hundred cycles, such as charge and discharge capacity, internal resistance, and energy efficiency, extensive numerical tests have been executed and compared in terms of accuracy score, precision score, recall score, etc. All the studied machine learning approaches are trained and validated using an open-access database of 124 commercial lithium iron phosphate/graphite cells cycled under different fast-charging conditions. As a result, the classification prediction performance score reached up to 98.74% depending on the percentage of data and cycles used for training and validation as well as the predefined EOL threshold. The comparative results can be used to improve the existing health-aware energy management strategy by taking the state-of-health (SOH) of batteries into consideration. Overall, the presented research findings are relevant to battery system reliability and safety engineering. Full article

In the literature, the hierarchy of value retention strategies (R-strategies) is utilized to describe the impacts on various circular economy (CE) factors. However, this approach is not suitable for batteries, such as the vanadium flow battery (VFB), due to its technical complexity. The presented model primarily focuses on VFBs, as a deep technical understanding is identified as a fundamental prerequisite for a comprehensive CE analysis. Based on the R-strategies, a new model called the dynamic multi-dimensional value retention strategy model (DDS) is developed accordingly. The DDS divides the R-strategies into three dimensions, as changes in the studied object each have a unilateral influence on the underlying dimensions. In addition, interactions among the R-strategies within the dimensions are observed. Moreover, the model enables the transparent and comprehensible examination of various CE objective factors. Through the model, future adjustments to CE for batteries can be analyzed and quantified. In particular, the analysis yields new insights into individual end-of-life (EoL) strategies, based on new findings regarding the VFB. Consequently, important new perspectives on the VFB are also illuminated. The DDS model is applicable to other complex technologies as well as simple product systems. Full article