Simulation Models for Sustainable, Resilient, and Optimized Global Electric Vehicles Supply Chain

TL;DR

This paper develops a digital twin simulation model of the global EV supply chain to evaluate its environmental impact, resilience, and potential improvements, revealing significant vulnerabilities and proposing an optimization approach to reduce emissions and vulnerability.

Contribution

It introduces the first comprehensive digital twin model of the EV supply chain that captures global dynamics, environmental impacts, and resilience, enabling targeted optimization.

Findings

Supply chain emissions range from 6.42 to 6.94 Kg e-CO2/KWh.

Unbalanced dependencies across supply phases highlight low resilience.

Optimization reduces vulnerability and emissions by up to 80%.

Abstract

While the transition to electric vehicles (EVs) is essential for decarbonizing the transportation system, the production and distribution of EVs entail substantial carbon costs. To ensure these emissions are accurately accounted for and effectively mitigated, this research introduces a digital twin of the EV's supply chain, addressing a critical gap in current EV life cycle analyses and providing the first comprehensive quantification of its environmental sustainability and resilience. This simulation model replicates global market dynamics and captures the complexity and uncertainty of the EV supply chain, enabling a thorough evaluation of its carbon footprint, sustainability, resilience, and what-if counterfactual scenarios for alternative market structures. The results reveal that average supply chain emissions range from 6.42 to 6.94 Kg e-CO2/KWh across different battery technologies. Additionally, the mass flow analysis shows unbalanced dependencies at all supply phases, with one geographical region significantly dominating the supply chain structure, highlighting the current supply chain architecture's low resilience and high vulnerability. In light of these findings, the study introduces an optimization model for hub and resource allocation configuration, effectively reducing vulnerability levels and supply chain emissions by up to 80%.