Life cycle assessment and tempo-spatial optimization of deploying dynamic wireless charging technology for electric cars. (March 2019)

• Record Type: Journal Article
• Authors: Bi, Zicheng; Keoleian, Gregory A.; Lin, Zhenhong; Moore, Michael R.; Chen, Kainan; Song, Lingjun; Zhao, Zhengming
• Is Part Of: Transportation research. Volume 100(2019)
• Page Start: 53
• Page End: 67
• Publication Date: 2019-03
• Subjects: Wireless charging -- Dynamic wireless power transfer -- Optimization -- Deployment plan -- Life cycle assessment -- Electric vehicle; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2019.01.002
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Optimal DWPT rollout reduces life cycle GHG & energy by up to 9% & 7% respectively. Roadside solar panels are essential to reduce DWPT life cycle energy and GHG. Electrifying up to 3% of roadways downsizes EV battery capacity by up to 48% GHG and energy burdens can break even within 20 years, but costs beyond 20 years. The cost of GHG mitigation by DWPT is $556/tonne of mitigated GHG. Abstract: Dynamic wireless power transfer (DWPT), or dynamic wireless charging technology, enables charging-while-driving and offers opportunities for eliminating range anxiety, stimulating market penetration of electric vehicles (EVs), and enhancing the sustainability performance of electrified transportation. However, the deployment of wireless charging lanes on highways and urban road networks can be costly and resource-intensive. A life cycle assessment (LCA) is conducted to compare the sustainability performance of DWPT applied in a network of highways and urban roads for charging electric passenger cars. The assessment compares DWPT to stationary wireless charging and to conventional plug-in charging using a case study of Washtenaw County in Michigan, USA over 20 years. The LCA is based on three key sustainability metrics: costs, greenhouse gas (GHG) emissions, and energy burdens, encompassing not only the use-phase burdens from electricity and fuel, but also the upfront deployment burdens of DWPT infrastructure. A genetic algorithm is applied to optimize the rollout of DWPT infrastructure both spatially and temporally in order to minimize life cycle costs, GHG, and energy burdens: (1) spatial optimization selects road segments to deploy DWPT considering traffic volume, speed, and pavement remaining service life (RSL); (2) temporal optimization determines in which year to deploy DWPT on a particular road segment considering EV market share growth as a function of DWPT coverage rate, future DWPT cost reduction, and charging efficiency improvement. Results indicate that optimal deployment of DWPT electrifying up to about 3% of total roadway lane-miles reduces life cycle GHG emissions and energy by up to 9.0% and 6.8%, respectively, and enables downsizing of the EV battery capacity by up to 48%, compared to the non-DWPT scenarios. Roadside solar panels and storage batteries are essential to significantly reduce life cycle energy and GHG burdens but bring additional costs. Breakeven analysis indicates a breakeven year for solar charging benefits to pay back the DWPT infrastructure burdens can be less than 20 years for GHG and energy burdens but longer than 20 years for costs. A monetization of carbon emissions of at least $250 per metric tonne of CO2 is required to shift the optimal "pro-cost" deployment to the optimal "pro-GHG" deployment. A roadway segment with volume greater than about 26, 000 vehicle counts per day, speed slower than 55 miles per hour (1 mile ≈ 1.609 km), and pavement RSL shorter than 3 years should be given a high priority for early-stage DWPT deployment.