Impacts of technology advancements on electric vehicle charging infrastructure configuration: a Michigan case study

Kavianipour, Mohammadreza; Fakhrmoosavi, Fatemeh; Shojaei, MohammadHossein; Zockaie, Ali; Ghamami, Mehrnaz; Wang, Joy; Jackson, Robert

doi:10.1080/15568318.2021.1914789

Cited by 22 publications

(8 citation statements)

References 42 publications

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“…The authors of [58] analyzed the effect of the driving range on the required charging infrastructure and found that with increasing range, the number of serviced cars in total by a given charging network increases simultaneously. The work of Kavianipour et al [59] analyzed the effect of increasing driving range and charging power. Their results indicate that with technological developments, the required charging infrastructure investment costs sink, as fewer charging points and less charging capacity need to be installed.…”

Section: Graph-based Charging Infrastructure Allocation Optimizationmentioning

confidence: 99%

Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

2022

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Given the ongoing transformation of the transport sector toward electrification, expansion of the current charging infrastructure is essential to meet future charging demands. The lack of fast-charging infrastructure along highways and motorways is a particular obstacle for long-distance travel with battery electric vehicles (BEVs). In this context, we propose a charging infrastructure allocation model that allocates and sizes fast-charging stations along high-level road networks while minimizing the costs for infrastructure investment. The modeling framework is applied to the Austrian highway and motorway network, and the needed expansion of the current fast-charging infrastructure in place is modeled under different future scenarios for 2030. Within these, the share of BEVs in the car fleet, developments in BEV technology and road traffic load changing in the face of future modal shift effects are altered. In particular, we analyze the change in the requirements for fast-charging infrastructure in response to enhanced driving range and growing BEV fleets. The results indicate that improvements in the driving range of BEVs will have limited impact and hardly affect future costs of the expansion of the fast-charging infrastructure. On the contrary, the improvements in the charging power of BEVs have the potential to reduce future infrastructure costs.

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Section: Graph-based Charging Infrastructure Allocation Optimizationmentioning

confidence: 99%

Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

2022

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“…The urban trajectories for all major cities in Michigan are extracted for this study. It is important to note that only the urban trajectories are considered, and the intercity trajectories are not in the scope of this study, which is studied thoroughly in some recent studies in the literature ( 4, 8, 39–41 ).…”

Section: Research Frameworkmentioning

confidence: 99%

Macro Analysis to Estimate Electric Vehicles Fast-Charging Infrastructure Requirements in Small Urban Areas

Singh

Kavianipour

Soltanpour

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Electric vehicles (EVs) are known to reduce emissions and fossil fuel dependency. However, the limited range, long charging time, and inadequate charging infrastructure have hampered the adoption of EVs. The current EV charging infrastructure planning studies and tools require detailed information, extensive resources, and skills that can be a significant barrier to urban areas for finding the required charging infrastructure to support a targeted EV market share. This study generates regression models to estimate the number of direct current fast charging stations and the chargers to support the EV charging demand for urban areas. These models provide macro-level estimates of the required infrastructure investment in urban areas, which can be easily implemented by policy-makers and city planners. This study incorporates data obtained from applying a disaggregate optimization-based charger placement model, developed recently by the same authors, for multiple case studies to generate the required data to calibrate the macro-level models, in the state of Michigan. This simulated data set includes the number of charging stations and chargers for each market share, technology advancement scenario, and the transportation network topology. The results show that the number of charging stations reduces with battery size and charging power and increases with EV market share and the road network lane length. The number of chargers reduces with charging power, whereas it increases with battery size, EV market share, and vehicle miles traveled in the system. The model developed here can be applied to any state having urban characteristics and weather conditions similar to Michigan.

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“…Over the past decade, several studies have addressed BEV operation and management problems, such as planning and deploying charging infrastructures. Most studies addressing the charging infrastructure problem focus on private BEVs (5,21,22,(32)(33)(34)(35)(36)(37)(38)(39)(40). These studies have two main approaches; either the locations of charging demand are considered to be concentrated at some fixed points in the network (41) or they are modeled as traveling flows throughout the network (42,43).…”

Section: Literature Reviewmentioning

confidence: 99%

Charging Infrastructure and Schedule Planning for a Public Transit Network with a Mixed Fleet of Electric and Diesel Buses

Soltanpour

Ghamami

Nicknam³

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Promoting battery electric buses (BEBs) can reduce fuel consumption and air pollution from the transit system. A complete transition from the current diesel fleet to BEBs is costly and time-consuming. Thus, the intermediate solution is a combination of diesel, hybrid, and BEBs. Therefore, a planning framework is required that simultaneously tackles three contiguous aspects of transit electrification and their interconnections, namely charging infrastructure, fleet configuration, and scheduling. Accordingly, this study considers a mixed fleet of diesel and BEBs. It aims to concurrently find (i) the optimal location and capacity of charging infrastructure, considering micro-grid specifications, the impact of distributed energy resources, and time-of-use electricity rates and (ii) optimum operation and refueling strategies. Another objective of this study is to capture the impacts of adverse weather conditions on transit electrification. A mixed-integer problem is proposed and solved using a metaheuristic algorithm based on simulated annealing to minimize system costs, including infrastructure, fleet, and operation costs. A subnetwork of transit in Worcester, Massachusetts, is selected as a case study, including three routes, five candidate charging locations, and three bus types. Findings suggest that BEBs can operate and serve the passenger demand with sufficient charging infrastructure. Sensitivity analyses show that even though high-power chargers are more expensive per piece, they reduce the overall cost as fewer chargers are required. The cost rises for chargers with power of 350 kW or more. It is worth noting that the benefits of BEBs are more significant in smaller buses and are heavily affected by adverse weather conditions.

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Impacts of technology advancements on electric vehicle charging infrastructure configuration: a Michigan case study

Cited by 22 publications

References 42 publications

Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

Macro Analysis to Estimate Electric Vehicles Fast-Charging Infrastructure Requirements in Small Urban Areas

Charging Infrastructure and Schedule Planning for a Public Transit Network with a Mixed Fleet of Electric and Diesel Buses

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