About the Project
Introduction:
This PhD project is part of the Dual Award arrangement between Coventry University, UK and Universitas Indonesia. The supervision team will be drawn from the two universities.
The transition to a circular economy (CE) in sustainable energy requires well-defined strategies and policies that optimize resource utilization, minimize waste, and ensure long-term energy sustainability. A key aspect of this transition is sustainable design, which is essential for improving the efficiency, longevity, and recyclability of technologies such as electric vehicle (EV) batteries. The rapid adoption of EVs has led to growing concerns about the management of end-of-life (EOL) batteries, and sustainable design plays a critical role in ensuring these batteries can be effectively recovered and repurposed, reducing environmental impact and conserving valuable materials.
Unlike the traditional linear energy model, a circular approach for EV battery recovery integrates renewable energy, efficient resource allocation, and sustainable waste management to enhance energy security and minimize environmental harm. Key strategies include optimizing the energy recovery from EOL batteries, improving the lifecycle management of EV battery technologies, and fostering industrial symbiosis through resource-sharing networks. Policy interventions, such as incentives for green technology, regulatory frameworks for battery recovery, and the development of efficient recovery processes, play a pivotal role in accelerating the shift toward a circular battery ecosystem. Effective decision-making in this context requires advanced analytical tools to evaluate trade-offs and optimize resource flows within the end-of-life EV battery supply chain.
This research employs a combination of operations research (OR) techniques to model and decision-making processes in circular energy systems, specifically focusing on EV battery recovery. Mathematical programming, network optimization, and simulation methods will be used to analyse resource allocation, battery recovery processes, and efficiency trade-offs. Among these, two-stage network Data Envelopment Analysis (DEA) is applied as one of the methodological approaches to assess design efficiency in the context of EV battery recovery strategies. The DEA model, combined with System Dynamics methodology, enables a structured evaluation of the performance and sustainability of recycling systems. Additionally, stochastic modelling is utilized to address uncertainties in the EOL battery supply chains.
The ultimate goal is to develop a robust decision-support framework that helps policymakers and industry stakeholders implement efficient, cost-effective, and scalable circular economy strategies for EV battery recovery, enhancing sustainability in the EV sector.
Other Information
Students will spend the first 2 years at Universitas Indonesia and the last 1.5 years Coventry University.