Motivations and Barriers Associated with the Adoption of Battery Electric Vehicles in Beijing: A Multinomial Logit Model Approach

The recent surge of the Chinese Plug-in Hybrid Electric Vehicle (PEV) market makes China the world’s largest PEV stock. A series of supportive policies in China contributed greatly to the rapid PEV adoption by limiting regular vehicles and reducing the price of PEVs. However, the role these policies play in changing references and encouraging consumers to purchase PEVs rather than conventional vehicles is not fully known. Other factors, rather than incentives, that could help maintain the current adoption trend are still unclear. The latter is especially critical in understanding how the market reacts to a gradually decreasing level of incentives to achieve the next goal of 5 million PEVs on the road by 2020 in China. Therefore, in this study the authors explored these research questions through a cross-sectional study of the current PEV market on consumers in Beijing by employing a multinomial logit model. Beijing has high levels of PEV adoptions in addition to a specific policy stimulus. The model results show significant influences of stimuli, individual socio-demographics, attitudes, charging infrastructure, and charging experiences on the adoption of PEVs over conventional vehicles. The results may help find out key interventions for policy makers to promote more PEV adoptions in China as well as other countries

Charging Electric Vehicles Using Opportunistic Stopovers

The diffusion of electric vehicles asks for efficient energy replenishment, which requires geographical and temporal coordination of shared charging resources. We introduce a novel charging methodology that exploits users\u2019 opportunistic mobility. This paper focuses on vehicle stopovers detecting potential charg- ing opportunities. Our mobility-assisted methodology protects users privacy and permits a hybrid centralized/distributed ap- proach avoiding clashes with other potential users. A preliminary analysis on our charging system, obtained with mobility data from the field, shows that among the available charging stations, some are more relevant and have a key role in serving electric vehicle recharge. This can be useful for further investigation on designing charging networks and aggregating electric vehicles towards charging stations

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV, in prep. for journal submission. In V3, we add a proof that the socially-optimal solution can be enforced as a general equilibrium, a privacy-preserving distributed optimization algorithm, a description of the receding-horizon implementation and additional numerical results, and proofs of all theorem

Market based intelligent charging system for electric vehicles

The existing electrical infrastructure is very unlikely to expand overnight. Therefore, a smart solution is certainly needed to integrate the additional load which electric vehicles (EV) bring to the network. The aim of the thesis is to study the electricity market, different intelligences related to electric vehicle charging and establish an algorithm that produces an optimized charging schedule for electric vehicles. The algorithm ensures a cost profit for user and takes part in demand response by shifting the timing of charging loads based on energy prices. The core intelligences integrated to the EV charging system in the thesis are cost optimization, peak shaving and load shifting. The algorithm follows the hourly unit cost related to the energy consumption and distribution fee in order to find the cheapest time slot for charging operation. It allocates as high charging power as possible to the cheapest time slots and then start selecting the expensive time slots until the battery is charged to desired state of charge. Along this process, the algorithm continuously calculates the maximum charging power available after other house-hold usage. The Elspot area price of Finland for 2018 added with 0.3 cents/kWh margin and 24% VAT are used as energy prices. Distribution unit prices include time-of-use pricing for day and nighttime energy use in addition to the fixed fuse-based fee. By following these unit prices, the algorithm shifts the load from high demand to low demand hours in order to minimize the total costs. The algorithm is implemented in MATLAB and tested through a case study on different type of Finnish detached houses. Detached houses with different load profile data are used as input for charging a 75 kWh EV with a 10 kW and 7.5 kW charger in different cases, where the other inputs remain same for all the test cases. The Elspot area price of Finland for 2018 added with 0.3 cents/kWh margin and 24% VAT are used as energy prices. Different day and night-time distribution prices are applied depending on the consumption. The simulation results are compared to regular EV charging, where the charging operation starts right after the EV is plugged in and finishes charging within shortest time. The results from the simulation are investigated from user’s and grid’s point of view. From user’s perspective, all the charging events with intelligent charging have costs savings over regular charging. The monetary profit is higher for higher charger rating (10 kW). In cases where the household usage is low, the proportional profit is high. From grid point of view, over 99% of the load gets shifted to night-time for 10 kW charger cases. For the 7.5kW charger, the amount of shifted load is over 97%, which is a little lower than 10 kW charger cases because of longer charging time. The findings of the case study validate the use of smart charging algorithm in order to ensure cost savings for the user

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV and accepted by TCNS. In Version 4, the body of the paper is largely rewritten for clarity and consistency, and new numerical simulations are presented. All source code is available (MIT) at https://dx.doi.org/10.5281/zenodo.324165

Have Recent Tax Credits and Benefits Affected The Adoption of Plug-In Electric Vehicles in The United States?

Looking at data from recent surveys showing that electric vehicles take up a meager 2.2% of the market share for passenger vehicles around the world, (Gilbert, 2021) it is safe to say that there is a lot of progress to be made if world governments hope to become carbon neutral within the half-century. This paper focuses on the accumulation of opinions and research regarding the adoption of plug-in electric vehicles in the United States, while focusing primarily on how tax benefits or credits could affect the adoption trends. We will provide information on the current climate of the electric vehicle industry both in the United States and abroad in order to give the reader a perspective on how other countries, whom may be first movers, are tackling this new-age dilemma. By analyzing the past attempts of the U.S. government’s electric vehicle incentives, we hope to show the feasibility and effectiveness of proposed and possible incentives on convincing the American public to change their buying habits and make our automotive industry run on electricity

Multiple Vickrey Auctions for Sustainable Electric Vehicle Charging

Electric vehicles (EVs) are important contributors to a sustainable future. However, uncontrolled EV charging in the smart grid is expected to stress its infrastructure, as it needs to accommodate extra electricity demand coming from EV charging. We propose an auction mechanism to optimally schedule EV charging in a sustainable manner so that the grid is not overloaded. Our solution has lower computational complexity, compared to state-of-the-art mechanisms, making it easily applicable to practice. Our mechanism creates electricity peak demand reduction, which is important for improving sustainability in the grid, and provides optimized charging speed design recommendations so that raw materials are not excessively used. We prove the optimal conditions that must hold, so that different stakeholder objectives are satisfied. We validate our mechanism on real-world data and examine how different trade-offs affect social welfare and revenues, providing a holistic view to grid stakeholders that need to satisfy potentially conflicting objectives