Materials Systems Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Research and Innovation Center, Ford Motor Company, Dearborn, Michigan 48121, United States.
Environ Sci Technol. 2021 Jul 20;55(14):10097-10107. doi: 10.1021/acs.est.1c00970. Epub 2021 Jul 2.
Modern automobiles are composed of more than 2000 different compounds comprising 76 different elements. Identifying supply risks across this palette of materials is important to ensure a smooth transition to more sustainable transportation technologies. This paper provides insight into how electrification is changing vehicle composition and how that change drives supply risk vulnerability by providing the first comprehensive, high-resolution (elemental and compound level) snapshot of material use in both conventional and hybrid electric vehicles (HEVs) using a consistent methodology. To make these contributions, we analyze part-level data of material use for seven current year models, ranging from internal combustion engine vehicles (ICEV) to plug-in hybrid vehicles (PHEVs). With this data set, we apply a novel machine learning algorithm to estimate missing or unreported composition data. We propose and apply a metric of vulnerability, referred to as exposure, which captures economic importance and susceptibility to price changes. We find that exposure increases from $874 per vehicle for ICEV passenger vehicles to $2344 per vehicle for SUV PHEVs. The shift to a PHEV fleet would double automaker exposure adding approximately $1 billion per year of supply risk to a hypothetical fleet of a million vehicles. The increase in exposure is largely not only due to the increased use of battery elements like cobalt, graphite, and nickel but also some more commonly used materials, most notably copper.
现代汽车由 2000 多种不同的化合物组成，包含 76 种不同的元素。识别这些材料中的供应风险对于确保向更可持续的交通技术平稳过渡非常重要。本文通过提供第一个全面的、高分辨率的（元素和化合物水平）使用材料的快照，深入了解了电气化如何改变车辆结构，以及这种变化如何通过提供一致的方法来驱动供应风险的脆弱性。为了实现这些贡献，我们分析了七个当前车型的零件级材料使用数据，涵盖了从内燃机车辆（ICEV）到插电式混合动力车辆（PHEV）。有了这个数据集，我们应用了一种新颖的机器学习算法来估计缺失或未报告的组成数据。我们提出并应用了一个脆弱性指标，称为暴露，它捕捉了经济重要性和对价格变化的敏感性。我们发现，对于 ICEV 乘用车，每辆车的暴露度从 874 美元增加到 SUV PHEV 每辆车的 2344 美元。向 PHEV 车队的转变将使汽车制造商的暴露度增加一倍，每年为假设的 100 万辆车队增加约 10 亿美元的供应风险。暴露度的增加不仅是由于电池元素如钴、石墨和镍的使用增加，而且还由于一些更常用的材料，尤其是铜。
