Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions

https://doi.org/10.1016/j.tra.2016.08.020Get rights and content

Highlights

  • Agent-based model simulates fleet of shared, electric, autonomous vehicles (SAEVs).

  • Vehicle and charging scenarios show tradeoffs between investment and operations.

  • Each SAEV can replace 3.7–6.8 privately owned vehicles.

  • Results suggest SAEV operational costs are most sensitive to vehicle capital costs.

  • SAEV cost-competitiveness to non-electric vehicles hinges on recharging automation.

Abstract

There are natural synergies between shared autonomous vehicle (AV) fleets and electric vehicle (EV) technology, since fleets of AVs resolve the practical limitations of today’s non-autonomous EVs, including traveler range anxiety, access to charging infrastructure, and charging time management. Fleet-managed AVs relieve such concerns, managing range and charging activities based on real-time trip demand and established charging-station locations, as demonstrated in this paper. This work explores the management of a fleet of shared autonomous electric vehicles (SAEVs) in a regional, discrete-time, agent-based model. The simulation examines the operation of SAEVs under various vehicle range and charging infrastructure scenarios in a gridded city modeled roughly after the densities of Austin, Texas.

Results based on 2009 NHTS trip distance and time-of-day distributions indicate that fleet size is sensitive to battery recharge time and vehicle range, with each 80-mile range SAEV replacing 3.7 privately owned vehicles and each 200-mile range SAEV replacing 5.5 privately owned vehicles, under Level II (240-volt AC) charging. With Level III 480-volt DC fast-charging infrastructure in place, these ratios rise to 5.4 vehicles for the 80-mile range SAEV and 6.8 vehicles for the 200-mile range SAEV. SAEVs can serve 96–98% of trip requests with average wait times between 7 and 10 minutes per trip. However, due to the need to travel while “empty” for charging and passenger pick-up, SAEV fleets are predicted to generate an additional 7.1–14.0% of travel miles. Financial analysis suggests that the combined cost of charging infrastructure, vehicle capital and maintenance, electricity, insurance, and registration for a fleet of SAEVs ranges from $0.42 to $0.49 per occupied mile traveled, which implies SAEV service can be offered at the equivalent per-mile cost of private vehicle ownership for low-mileage households, and thus be competitive with current manually-driven carsharing services and significantly cheaper than on-demand driver-operated transportation services. When Austin-specific trip patterns (with more concentrated trip origins and destinations) are introduced in a final case study, the simulation predicts a decrease in fleet “empty” vehicle-miles (down to 3–4% of all SAEV travel) and average wait times (ranging from 2 to 4 minutes per trip), with each SAEV replacing 5–9 privately owned vehicles.

Introduction

Recent transportation trends in increasing electric vehicle (EV) sales and growing carsharing membership have important impacts on greenhouse gas emissions and energy use. Incentivizing plug-in EV adoption and shared-vehicle use may be key strategies for helping regions achieve national- and state-level air quality standards for ozone and particulate matter, and ultimately carbon-emissions standards. At the same time, with the rise of the shared-use economy, carsharing is emerging as an alternative mode that is more flexible than transit but less expensive than traditional private-vehicle ownership. However, the growth of EVs and carsharing are both hindered by technological and social factors. For EVs, the most significant hindrance may be “range anxiety,” a user’s concern for being stranded with a fully discharged battery and no reasonable recharge option (Bartlett, 2012). Meanwhile, as EVs penetrate the private and commercial vehicle fleets, they are also gaining ground in the carsharing world. EVs are a natural match for carsharing operations as existing members of carsharing operations tend to drive smaller and more fuel efficient vehicles than non-carshare members (Martin and Shaheen, 2011). Cutting-edge carsharing operators (CSOs) are already employing EVs in their fleets (such as Daimler’s Car2Go and BMW’s DriveNow operations), but the manual relocation of fleets in one-way carsharing systems continues to present profitability challenges to CSOs. The introduction of autonomous driving technology would remove the challenge of manual vehicle relocation and presents a driver-free method for shared EVs to reach travelers’ origins and destinations as well as charging stations. In a carsharing setting, a fleet of shared autonomous electric vehicles (SAEVs) would automate the battery management and charging process, and take range anxiety out of the equation for growth of EVs. With the recent popularity of on-demand transportation services through transportation network companies, it is possible to imagine a future travel system where autonomous vehicle (AV) technologies merges with carsharing and EVs in a SAEV fleet. But can self-driving vehicles be shared, self-charged, and right (battery-) sized for the trip lengths that travelers desire?

This study attempts to answer this question through the simulation of a SAEV fleet in a discrete-time agent-based model, examining fleet operations in a 100-mile by 100-mile gridded metropolitan area. Scenarios combine short-range and long-range electric vehicles with Level II and Level III charging infrastructure to look at the impacts of vehicle range and charging time on fleet size, charging station sites, ability to meet trip demand, user wait times, and induced vehicle-miles traveled (VMT). Following the discussion of the simulation results, a financial analysis highlights the tradeoffs between capital investment in vehicles and charging infrastructure and user benefits.

Section snippets

Prior research

There is a wealth of literature examining carsharing, electric vehicles and charging infrastructure planning, and autonomous vehicles as separate topics. Studies looking at gasoline-propelled and (especially) electric AVs in a shared setting are more limited. Wang et al. (2006) proposed a dynamic fleet management algorithm for shared fully automated vehicles based on queuing theory. In a simulative environment with five stations and five vehicles, the average passenger waiting time was 3.37 min

Model setup

The discrete-time agent based model used here is an expansion of the 10-mile by 10-mile model proposed by Fagnant and Kockelman (2014). In its setup, the model generates a square 100-mile by 100-mile gridded metropolitan area, divided into 160,000 quarter-mile by quarter-mile cells. The gridded city (roughly modeled after the population density pattern of Austin, Texas) is divided into four zones as shown in Fig. 1: downtown (the innermost 2.5-mile radius), urban (the next ring 7.5-mile

Model scenario results

The agent-based model described here is run for several scenarios to examine the sensitivity of various fleet operation metrics to model inputs, as shown in Table 2. A non-electric SAV scenario (assuming 400-mile range and 15 min refueling time) is run as a reference case for comparison to the results in Fagnant and Kockelman (2014). Next, the SAEV scenario assumes the vehicle has an 80-mile range (similar to current models of the Nissan Leaf, Chevrolet Spark, Honda Fit EV, and BMW i3) and 4 h

Financial analysis

Simulation results offer some insight into how combinations of vehicles and charging infrastructure impact fleet operations, but a financial analysis is necessary to truly grasp the tradeoff between additional capital investment (into vehicles with bigger batteries or more expensive fast charging stations) and user benefits (measured in additional trips served or decreased wait times). For each vehicle and charging station type, analysis was conducted for three cost levels: low-, medium-, and

Austin, Texas case study

While the Poisson-based trip generation process modeled in the simulated monocentric city provides some variation in each cell’s trip generation rate, actual trip rates in real-city geographies are significantly less “smooth.” In exurban areas, an overall low population density is often reflected by pockets of relatively dense residential development among much larger areas of very sparse population. To offer more realism here, a case study using Austinites’ year-2010 trip patterns with U.S.

Conclusions

Motivated by natural synergies between autonomous driving technology and EVs in a shared setting, this paper employs an agent-based model to simulate the operations of a fleet of SAEVs serving 10% of all trip demand in a medium-sized metropolitan area under various vehicle and infrastructure scenarios. Simulation results show that fleet size is highly dependent on charging infrastructure and vehicle range. For the non-electric SAV scenario, each shared vehicle can replace 7.3 private vehicles.

Acknowledgements

The authors are very grateful for National Science Foundation support for this research (in the form of an IGERT Traineeship for the first author and Graduate Research Fellowship for the third author), anonymous-reviewers’ suggestions, Dr. Daniel Fagnant’s provision of the starting code, Prateek Bansal’s assembly of Austin’s regional trip data, Dr. Peter Stone’s editorial guidance, and Dave Tuttle’s continued alerts on relevant EV research.

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