The transition to electric vehicles (EVs) is crucial for mitigating climate change, but the lifecycle of EV batteries presents significant environmental and social challenges. Current battery production is energy-intensive and involves ethically questionable mining practices. Millions of end-of-life (EOL) batteries are projected by 2040, creating waste management issues. While EV batteries retain significant capacity after their first life and can be repurposed or recycled, challenges remain in forecasting EOL battery availability, optimizing reverse logistics, and maximizing value recovery. Information silos hinder efficient battery circularity. Existing solutions, such as the Battery Passport, lack comprehensive end-to-end management capabilities. This research addresses these gaps by developing an ecosystem value optimization approach powered by a digital solution framework. This framework aims to improve five key value drivers for battery circularity: safety, regulatory compliance, carbon footprint reduction, product quality, and financial viability.

Existing literature highlights the environmental and economic benefits of battery circularity, including the reuse of EOL batteries in second-life applications and the sustainable recovery of raw materials through recycling. Studies show that direct recycling has the lowest environmental impact compared to producing materials from primary sources. Financial viability studies demonstrate that recycling can be profitable under certain conditions. However, significant challenges persist, including high transportation costs (averaging 41% of total recycling costs), the difficulty of forecasting EOL battery availability, and information silos across the value chain. The Battery Passport initiative is mentioned as a promising data source, but its limitations in end-to-end EOL battery management are noted.

The authors propose an ecosystem value optimization approach powered by a digital solution framework. This framework consists of a blockchain-based platform for data sharing and traceability, predictive and prescriptive analytics for data-driven decision-making, and behavioral economics models to incentivize battery collection and repurposed product adoption. The framework addresses five key value drivers: 1. **Safety:** Trusted data sharing of battery handling instructions, tracking battery health, and recording proof of safe handling during transport. 2. **Regulatory Compliance:** End-to-end tracking and tracing of EOL batteries to ensure compliance with regulations. 3. **Carbon Footprint Reduction:** Estimating CO2 reduction for each EOL battery and tracking emissions savings throughout the reverse logistics and value recovery processes. 4. **Product Quality:** Leveraging behavioral economics to promote repurposed products and address consumer concerns about quality. 5. **Financials:** Simulating various reverse logistics scenarios, optimizing transportation costs, and maximizing value recovery through strategic routing of batteries to appropriate repurposing or recycling partners. Specific methods employed include: * **EOL Battery Availability Forecasting:** A binomial distribution model based on EV registration data. * **Reverse Logistics Network Simulation:** A stochastic simulation approach using Mesa to optimize transportation costs and value recovery. * **EOL Battery Health Estimation:** Bidirectional LSTM and Support Vector Regression (SVR) models using relaxation voltage time series data. * **Remaining Value and Offer Price Recommendation:** A behavioral-economics-based pricing model using logistic regression to predict offer acceptance and minimize acquisition costs. * **Collection Route Recommendation:** A heuristic search approach to optimize collection routes and minimize transportation costs. * **Blockchain Network Design:** A permissioned blockchain network based on Hyperledger Fabric for secure data sharing and traceability across the value chain. Specific implementation details are provided for the chosen framework and its integration with off-chain databases, IoT and enterprise systems, analytics services, and user micro-apps.

The proposed digital solution framework demonstrated significant potential for improving EV battery circularity. Simulation results indicate the potential to: * **Reduce transportation costs:** By 11% to 44% compared to current practices. * **Improve value recovery:** By 52% to 60% by optimizing battery routing. * **Accurately estimate battery health:** With error rates below 1% using a bidirectional LSTM model. * **Generate gross profit:** The OEM could potentially make a gross profit of $507 to $667 per EOL battery of small EVs (20kWh to 30kWh size) by optimizing offer costs and routing EOL batteries to the appropriate Re* provider. The framework also enables real-time tracking of EOL batteries, facilitates informed decision-making regarding repurposing or recycling, and promotes transparent and trusted data sharing among stakeholders. A demonstration case study involving 2100 EOL batteries is presented, showcasing the scalability and applicability of the solution. A route optimization example in Los Angeles County shows the potential for minimizing transportation distance and associated costs and emissions.

The findings demonstrate the effectiveness of the proposed digital solution framework in optimizing multiple value drivers for EV battery circularity. The significant reductions in transportation costs and improvements in value recovery highlight the economic viability of the approach. The high accuracy of the battery health estimation model enables precise matching of batteries with appropriate repurposing or recycling partners, maximizing value extraction. The integration of blockchain technology ensures data transparency and trust, facilitating collaboration among stakeholders. The results address the limitations of existing solutions and showcase the potential for creating a truly circular battery value chain. The study contributes to the broader literature on sustainable battery management and circular economy initiatives.

This research presents a comprehensive digital solution framework for managing the lifecycle of EV batteries, optimizing value recovery, and promoting circularity. The framework effectively integrates various analytical models and a blockchain-based platform to address key challenges in EOL battery management, such as forecasting availability, optimizing logistics, and maximizing value recovery. The results demonstrate significant potential for reducing costs, improving efficiency, and enhancing sustainability across the EV battery value chain. Future research could focus on expanding the framework to incorporate a wider range of battery chemistries and exploring the integration of advanced machine learning techniques for even more accurate predictions and optimized decision-making.

The accuracy of the forecasting model relies on the availability and quality of EV registration data. The simulation results are based on a specific use case and may not be directly generalizable to all contexts. The behavioral economics model's predictive power depends on the quality and representativeness of the historical data used for training. Further validation and refinement of the models may be necessary to ensure robustness and adaptability to diverse operational environments.