Optimizing Battery Lifecycle Management through Digital Twin Simulation
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I3P124Keywords:
Digital Twin, Battery Lifecycle Management, Circular Economy, Predictive Analytics, Sustainability, Lithium-Ion BatteriesAbstract
The rapid proliferation of lithium-ion batteries in electric vehicles, consumer electronics, and grid-scale energy storage systems has created unprecedented challenges for sustainable resource management. This paper presents a comprehensive framework for optimizing battery lifecycle management through digital twin simulation technology. By integrating real-time monitoring, predictive analytics, and decision optimization algorithms, digital twins enable stakeholders to maximize battery value across manufacturing, first-use, second-life, and recycling phases. We examine how digital twin frameworks support circular economy principles by facilitating data-driven decisions on battery health assessment, repurposing strategies, and end-of-life processing. The proposed approach addresses critical gaps in current battery management practices, including limited visibility into degradation patterns, suboptimal second-life deployment, and inefficient recycling processes. Case studies demonstrate that digital twin-enabled lifecycle optimization can extend effective battery lifespan by 30-50%, reduce environmental impact by up to 40%, and improve economic returns throughout the value chain. This research contributes to sustainable engineering systems by providing a systematic methodology for implementing digital twin technology in battery lifecycle management.
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