Optimization of shared autonomous electric vehicles operations with charge scheduling and vehicle-to-grid

Iacobucci, Riccardo; McLellan, Benjamin; Tezuka, T.

doi:10.1016/j.trc.2019.01.011

Cited by 155 publications

(80 citation statements)

References 41 publications

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“…Liu (2018) studied the optimal parking pricing scheme considering AV users' choice on departure time and parking location. Iacobucci et al (2019) optimized the charging and relocation schedule for autonomous electric vehicles through model predictive control. However, these studies do not consider the impacts of uncertainty in AV traffic demand on the management strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

Look Who’s Talking Now: Implications of AV’s Explanations on Driver’s Trust, AV Preference, Anxiety and Mental Workload

Haspiel

Zhang

et al. 2019

SSRN Journal

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This study proposes an autonomous vehicle incentive program design (AV-IPD) problem for a local government, which aims to promote the adoption of autonomous vehicles (AVs) by deploying AV lanes and subsidizing the purchase of AVs with the objective of system optimum subject to a fixed budget. As it is difficult to anticipate the changing AV market conditions in the future, we take into account the uncertainty in AV purchase price. For the AV-IPD problem, we firstly decide which regular lanes should be converted into exclusive AV lanes. Secondly, we determine the AV purchase subsidy for each realization of AV purchase price with a given AV lane deployment scheme. A binary logit model is applied to characterize the vehicle choice behavior of users. The AV-IPD problem is formulated as a two-stage stochastic programming model with equilibrium constraints. To solve the AV-IPD problem, we develop a solution method based on linear approximation techniques and duality theories. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and solution method. 1. Introduction The last few years have witnessed an explosive development on autonomous vehicle (AV) technology. Compared to the conventional vehicles (CVs), AVs can achieve a wide range of benefits in terms of traffic capacity, safety and vehicular emission. A successful implementation of AVs depends on the deployment of transport infrastructure catered for AVs and sufficient AV adoption. To promote the market penetration of AVs, a local government could design an AV incentive program to deploy exclusive AV lanes and subsidize users to purchase AVs subject to a fixed budget. The purpose of setting AV lanes is to separate AVs and CVs and exploit AV's advantage in improving road capacity. Current studies demonstrated that the road capacity could become up to triple in full AV environment by a shorter headway (Tientrakool et al., 2011). However, AVs become less beneficial under the mixed traffic with AVs and CVs. With a low AV ratio, the road capacity is possible to decrease due to speed variation and shock waves (Van Arem et al., 2006). To eliminate the issues arising from the mixed traffic, a local government can divide roads into regular lanes and AV lanes (Talebpour et al., 2017) so that AVs can travel with very short headway on AV lanes. The AV users have right of way on both regular and exclusive lanes while CV users can only travel on regular lanes. Different from conventional road construction projects, deploying AV lanes does not increase the number of lanes but converts some regular lanes into AV exclusive lanes. Some sensors are installed to facilitate the vehicle-to-vehicle (V2V) and vehicleto-infrastructure (V2I) communications (Jia et al., 2016). Some roads may require a novel traffic signal system (Li and Zhou, 2017). Besides, the users' vehicle choice between AVs and CVs is another feature needed to consider for the deployment of AV lanes. This is

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Section: Literature Reviewmentioning

confidence: 99%

Look Who’s Talking Now: Implications of AV’s Explanations on Driver’s Trust, AV Preference, Anxiety and Mental Workload

Haspiel

Zhang

et al. 2019

SSRN Journal

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“…The results suggest that by assuming a fleet of fully electric vehicles equipped with rapid charging batteries (30 minutes) and a range of 175 kilometers, the change on required fleet size remains minimal (+2%). Iacobucci et al (2019) focused on optimization of SAEV operations upon the transportation network of Tokyo considering charge scheduling and vehicle-to-grid based on the stochastic demand and simplified time-varying traffic stats. The employed optimization includes minimization of wait times and charging costs incorporating dynamic electricity pricing.…”

Section: Prior Researchmentioning

confidence: 99%

Shared autonomous electric vehicle service performance: Assessing the impact of charging infrastructure

Vosooghi

Puchinger

Bischoff

et al. 2020

Transportation Research Part D: Transport and Environment

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A B S T R A C TShared autonomous vehicles (SAVs) are the next major evolution in urban mobility. This technology has attracted much interest of car manufacturers aiming at playing a role as transportation network companies (TNCs) in order to gain benefits per kilometer and per ride. The majority of future SAVs will most probably be electric. It is therefore important to understand how limited vehicle range and the configuration of charging infrastructure will affect the performance of shared autonomous electric vehicle (SAEV) services. We aim to explore the impacts of charging station placement, charging type (rapid charging, battery swapping) as well as vehicle range onto service efficiency and customer experience in terms of service availability and response time. We perform an agent-based simulation of SAEVs across the Rouen Normandie metropolitan area in France. The simulation process features impact assessment by considering dynamic demand responsive to the network and traffic.Research results suggest that the performance of SAEVs is strongly correlated to the charging infrastructure. Importantly, faster charging infrastructure and optimized placement of charging locations in order to minimize distances between demand hubs and charging stations result in a higher performance. Further analysis indicates the importance of dispersing charging stations across the service area and how this affects service effectiveness. The results also underline that SAEV battery capacity has to be carefully selected to avoid the overlaps between demand and charging peak times. Finally, the simulation results show that by providing battery swapping infrastructure the performance indicators of SAEV service are significantly improved.

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“…The rated power P r is produced when the wind speed is produced between the rated wind speed V r and the cut-out speed V co . The generated power P i that is appropriate to the specific speed of the wind SW i is determined by the following equation [9]:…”

Section: B Loss Equationmentioning

confidence: 99%

An Improved Multicriteria Optimization Method for Solving the Electric Vehicles Planning Issue in Smart Grids via Green Energy Sources

Abedinia

Bagheri

2020

IEEE Access

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In the given research, a new multicriterion (multiobjective) optimization algorithm has been considered to solve the problem of electric vehicles (EV) scheduling in a smart network in view of sustainable energy sources based on the cost and pollution minimization. By considering the environmental and economic problems, the application of EVs as a proper charging/discharging scheduling model and green energy sources plays an important role in the power system. This study focuses on multicriteria scheduling through uncertainty factors via inexhaustible assets and EVs, presenting a battery storing framework and reducing both the operation costs and the amount of the framework's pollution while improving the procedures. For this purpose, a new optimization algorithm has been considered to address the mentioned problem taking into account some clean energy sources and emission. The proposed model is examined in smart grid environment based on real-life model by Demand Response Program (DRP) evaluation and the uncertainties in sustainable energies. The proposed optimization algorithm shows more desirable results in comparison with other models, while the efficiency of the suggested approach is studied and evaluated in two power systems, i.e., a 33-bus standard power system and a 94-bus Portugal network. The obtained results

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Optimization of shared autonomous electric vehicles operations with charge scheduling and vehicle-to-grid

Cited by 155 publications

References 41 publications

Look Who’s Talking Now: Implications of AV’s Explanations on Driver’s Trust, AV Preference, Anxiety and Mental Workload

Look Who’s Talking Now: Implications of AV’s Explanations on Driver’s Trust, AV Preference, Anxiety and Mental Workload

Shared autonomous electric vehicle service performance: Assessing the impact of charging infrastructure

An Improved Multicriteria Optimization Method for Solving the Electric Vehicles Planning Issue in Smart Grids via Green Energy Sources

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