Smart Charging Systems for Plug-in Electric Vehicles

Evaluation of charging infrastructure requirements and operating costs for plug-in electric vehicles

This report addresses the fundamental question of how much plug-in electric vehicle (PEV) charging infrastructure—also known as electric vehicle supply equipment (EVSE)—is needed in the United States to support both plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs).

Abstract: The main requirement for the adoption and implementation of electric vehicles (EV) is the availability of charging facilities in public locations. In order to create an enabling EVSE ecosystem and hasten the adoption of EVs, this paper addresses several challenges relating to electric vehicle supply equipment (EVSE) or charging stations linked to legislation, standards, interoperability, and business models. Electric vehicle (EV) supply equipment (EVSE) or charging equipment are necessary for vehicle owners to adopt EVs. With varying degrees of success, several nations utilised various strategies and economic models to build the EVSE ecosystem. As India prepares to launch an EV revolution, a few crucial EVSE-related questions continue to plague the industry's players: What criteria apply to EVSE in India? whose will berun and keep up an EVSE? Utilities? Utility franchisees, perhaps? or outside parties like business owners, parking lot managers, and fleet operators? What will the electricity cost be for charging an EV? Will there be only energy charges or will there be capacity charges as well (minimum monthly fee per kW of capacity)? Who will cover the expense of the electric grid upgrade (higher capacity distribution transformers and new cables when necessary): the owner of the EVSE or the utility's usual grid improvement capex? Where will the public EVSEs be installed, and will the land be given away for free, at a discount, or at market value? This paper seeks to summarise the extensive work that has previously been done by numerous parties on the aforementioned topics and environment that will facilitate the rapid implementation of EVs.

As more battery electric vehicles and plug-in hybrid electric vehicles are connected to the microgrid, plug-in electric vehicles have a major impact on the microgrid. This paper proposes a multi-objective optimization energy management model including plug-in electric vehicles and other distributed generations. By analyzing the powertrain structure of different kinds of plug-in electric vehicles, the engine fuel consumption model, the charging model, the discharge model, and the battery state of charge model of plug-in electric vehicle in microgrids are given. The proposed model considers the plug-in electric vehicle battery state of charge constraints to prevent the battery from overcharging and over-discharging and gives the state of charge curve in microgrids. Simultaneously, an improved gray wolf algorithm, introducing optimization control factors and greedy strategies to better balance the mining and exploration capabilities of the gray wolf algorithm, is proposed to solve this multi-objective optimization energy management model. Compared with particle swarm optimization and traditional gray wolf algorithm, the improved algorithm further improves the accuracy and convergence speed. Besides, the improved algorithm is applied to three scheduling schemes, and the results show that plug-in hybrid electric vehicles have more advantages in energy economy in some special cases.

The government policies and cost benefits led to the surge in penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs) into both public and private sectors, challenging the electric grid's potential to maintain its stability and efficiently serve the purpose. The penetration of PHEVs brings technological advancements and environmental benefits to the traditional transportation and electrical power systems. Designing of appropriate charging methods to satisfy PHEV owners helps to improve system performance and avoid transmission and distribution system expansion.;The primary objective of this thesis is to model a coordinated charging algorithm to charge maximum number of PHEVs with the existing infrastructure at a Charging Station (CS) located in residential locality. The stochastic driving patterns of PHEVs are studied and different charging strategies are developed. The Optimized charging scheme is obtained for American Electric Power utility distribution feeder modeled in OpenDSS. Further, PHEV customers are modeled into price responsive consumers capable of participating in Demand Response programs to economically charge their vehicles and render support to the stabilization of power system. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices (PEMs) which are utilized to calculate the level of demand response.;This thesis also proposes a concept of converting the existing Personal Rapid Transit (PRT) system from guideway power rail propelling vehicles to Battery Electric Vehicles (BEVs) by developing a novel routing algorithm based on the data provided by West Virginia University's Department of Transportation and Parking. This turns into a feasibility analysis of implementing new EV technologies in electrification of public transportation. The routing algorithm involves Mixed Integer Linear Programming (MILP) Optimization resulting in hourly schedule of all the vehicles satisfying the passenger demand. The effect of charging load of BEVs on the electrical distribution system is analyzed by performing a time series simulation on AEP distribution feeder.;Additionally, the impact of PHEV charging on power market prices i.e. Locational Marginal Prices (LMP) in a distribution microgid scenario is presented. Locational marginal pricing is a market based wholesale pricing approach adopted by six out of ten national electricity markets. The uncoordinated charging of EVs results in unplanned peak demand causing system changes and fluctuation of LMP. The stabilizing effect of coordinated charging scheme is demonstrated on IEEE 13 Node test system.

Electric vehicles are anticipated to be essential components of future energy systems, as they possess the capability to assimilate surplus energy generated by renewable sources. With the increasing popularity of plug-in hybrid electric vehicles (PHEVs), conventional internal combustion engine (ICE)-based vehicles are expected to be gradually phased out, thereby decreasing greenhouse gases and reliance on foreign oil. Intensive research and development efforts across the globe are currently concentrated on developing effective PHEV charging solutions that can efficiently cater to the charging needs of PHEVs, while simultaneously minimizing their detrimental effects on the power infrastructure. Efficient PHEV charging strategies and technologies are necessary to overcome the obstacles presented. Forecasting PHEV charging loads provides a solution by enabling energy delivery to power systems based on anticipated future loads. We have developed a novel approach, utilizing machine learning methods, for accurately forecasting PHEV charging loads at charging stations across three phases of powering (smart, non-cooperative, and cooperative). The proposed Q-learning method outperforms conventional AI techniques, such as recurrent neural and artificial neural networks, in accurately forecasting PHEV loads for various charging scenarios. The findings indicate that the Q-learning method effectively predicts PHEV loads in three scenarios: smart, non-cooperative, and cooperative. Compared to the ANN and RNN models, the forecast precision of the QL model is higher by 31.2% and 40.7%, respectively. The Keras open-source set was utilized to simulate three different approaches and evaluate the efficacy and worth of the suggested Q-learning technique.

Author(s): Muehlegger, Erich; Rapson, David | Abstract: This research project explores the plug-in electric vehicle (PEV) market, including both Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs), and the sociodemographic characteristics of purchasing households. The authors use detailed micro-level data on PEV purchase records to answer two primary research questions. Their results confirm that low-income households exhibit a lower share of PEV purchases than they do for conventional, internal combustion engine (ICE) vehicles. Households with annual income less than $50,000 comprise 33 percent of ICE purchases and only 14 percent of PEVS. By comparison, high-income households earning more than $150,000 annually comprise only 12 percent of ICE purchases and 35 percent of PEV purchases over their sample period. Similarly, unsurprising patterns can be seen across ethnicities. For example, non-Hispanic Whites represent 41 percent of ICE purchases but 55 percent of PEV purchases, as compared to Hispanics (38 percent of ICE and 10 percent of PEVs) and African Americans (3 percent of ICEs and 2 percent of PEVS). These differences naturally raise questions about barriers to PEV adoption among low-income and minority ethnic populations. By comparing outcomes in the ICE, hybrid, and PEV markets across income and ethnic groups, the authors are able to test whether price discrimination and barriers to market access are higher in PEV markets for low-income and minority ethnic groups. The authors find that, overall, they are not, although there are mixed results for the used PEV market. In general, non-white, low-income populations face higher prices in the used PEV market, relative to a baseline, than they do in the new PEV market. While some people travel farther to buy used PEVs than they do to buy used ICE vehicles, there is not a pattern that would indicate systematic discrimination (e.g. Hispanics travel farther to buy used PHEVs but less far to buy used BEVs). While the authors admit that their empirical approach cannot control for all potential vehicle composition effects, the authors view their results as being most consistent with a market that provides access to all ethnicities and income groups.View the NCST Project Webpage

This dissertation contributes to our understanding of how plug-in hybrid electric vehicles (PHEVs) and plug-in battery-only electric vehicles (EVs)—collectively termed plug-in electric vehicles (PEVs)—could be successfully integrated with the electric power system. The research addresses issues at a diverse range of levels pertaining to light-duty vehicles, which account for the majority of highway vehicle miles traveled, energy consumed by highway travel modes, and carbon dioxide emissions from on-road sources. Specifically, the following topics are investigated: (i) On-board power electronics topologies for bidirectional vehicle-to-grid and grid-to-vehicle power transfer; (ii) The estimation of the electric energy and power consumption by fleets of light-duty PEVs; (iii) An operating framework for the scheduling and dispatch of electric power by PEV aggregators; (iv) The pricing of electricity by PHEV aggregators and how it affects the decision-making process of a cost-conscious PHEV owner; (v) The impacts on distribution systems from PEVs under aggregator control; (vi) The modeling of light-duty PEVs for long-term energy and transportation planning at a national scale.

The key elements in an indirect V2G system architecture are aggregators. They act as an interface between the grid and a group of plug-in hybrid electric vehicles (PHEVs). In this paper, we design an optimal vehicle to grid (V2G) aggregator to control the charging and frequency regulation processes of a group of PHEVs. We consider a problem that an aggregator has to minimize the overall cost of PHEV fleet in a multiple time slot horizon and meet the required battery level when PHEVs plug out. We adopt summation of PHEVs' expenditure in a finite number of time slots as our objective function, which is a quadratic optimization problem. A model predictive control based (MPC-based) PHEV charging and regulation algorithm is proposed to schedule the charging and regulation processes. Through the numerical experiments, we obtain the optimal charging and frequency regulation sequences for each PHEV, the effect of price prediction error on PHEV's cost as well as the impact of penalty factor to plug-out State of Charge (SOC). It is also shown that by taking the optimal control sequences, the PHEV owner can reduce his cost and depart with desired SOC.

This paper deals with the Hybrid Energy Storage System (HESS) for Battery Electric, Hybrid and Plug-in Hybrid Electric Vehicles. Its performance is compared with conventional HESS design and also only Battery design, conventional design uses a bigger dc/dc converter between Battery and Ultracapacitor to satisfy the peak power demands in the real time, In this analysis a smaller dc/dc converter is used which maintains the voltage of ultracapacitor higher than the battery voltage by working as controlled energy pump. Battery will provide power directly only when voltage of the Ultracapacitor drops below the voltage of the Battery. Therefore, a constant and smooth load profile can be created for the battery. For the proposed HESS system Simulation results are presented to verify.

The penetration and spread of cyberattacks can vary depending on the structure and nature of each infrastructure. Among the infrastructures that have not been sufficiently studied and are vulnerable to cyberattacks is the electric vehicle infrastructure (EVI). This infrastructure is an ideal target for such attacks for many reasons, including the mobile nature of electric vehicles (EVs), high load demand, technologies, and communication networks of EVs. If EVs, as the moving part of smart grids, become compromised due to a cyberattack, they can spread this attack to other components of the infrastructure, such as EVs supply equipment (EVSE), which is used for recharging in a short period of time. So, without any defensive to attacks, it may spread to other parts and infrastructures of the smart grid. In this chapter, a pervasive cyberattack on EVI and propagation of the attack have been studied in three parts including EV charging infrastructure in EVSEs, EVSEs, and EVs shared communication network, considering the compromising probabilities or threat levels of EVs and EVSEs. As a result, it minimizes the maximum threat level and reduces the process of spreading the cyberattack in both EVI and also the smart grid.KeywordsSmart gridElectric vehicleCyberattacksCommunication networkAttack propagationResponse model

Plug-in hybrid electric vehicles and smart grids: Investigations based on a microsimulation

The paper presents a study of the profile of the load imposed on a power system by grid-charging of the onboard battery pack of electric and plug-in hybrid vehicles. The study uses a large database of field-recorded driving cycles stamped with parking times and locations to predict realistic driving habits of drivers in an urban setting. A fuzzy-logic inference system is designed to emulate the decision-making process of a driver when deciding to charge the vehicle's battery. The charging load is then estimated on an hourly basis for a number of electric and plug-in hybrid vehicles with different storage capacities. Level-1 and level-2 charging regimes as well as two scenarios for charging, namely charging at home and charging at home and work, are considered. The load profile is presented as an hourly probability of charging for each vehicle type. The results demonstrate how penetration of plug-in hybrid and electric vehicles affects the load on a utility network.

Photovoltaic modules have become a viable renewable energy source for energy systems in communications, commercial, and residential applications. Plug-in electric or hybrid vehicles appear in the market as an emerging technology to reduce carbon emissions and improve energy efficiency. PV modules and plug-in hybrid vehicles interact with the power grid as energy source and energy storage elements, respectively, however little was reported on energy conversion systems featuring three-way energy flow among the power grid, PV modules, and plug-in hybrid vehicles. This paper proposes a plug-in hybrid electric vehicle (PHEV) solar carport charging station concept featuring a multi-port power electronic interface among photovoltaic modules, PHEVs, and the power grid. A unique control strategy is implemented, allowing efficient energy transfer while reducing the conversion stages between the source and load. The system is designed to be modular to improve flexibility and allow for ease of expansion. In the proposed system, a single modular system will provide charging for two parking spaces.

The rising concerns over global climate change and depleting fossil fuel reserves are two of the main reasons for the ongoing efforts towards the electrification of the transportation sector. While greenhouse gases (GHGs) emissions from other sectors are generally falling, emissions from the road transport have increased over the past few decades, with both full electric vehicles (FEVs) and plug-in hybrid electric vehicles (PHEVs) being recognized as potential alternatives to combat climate change and reduce GHG emissions. However, wide-spread integration of FEVs and PHEVs will substantially increase the load on the power system which will eventually affect the reliability of existing power systems. In this paper, a probabilistic model for integrating FEVs and PHEVs with existing power grids is proposed that incorporates important FEV and PHEV characteristics, such as battery capacity, charge depleting distance, and charging rates. In addition, user behavior is taken into account through time of recharging, arrival and departure times, and daily miles driven. Furthermore, different charging strategies, i.e., opportunistic charging and controlled charging with and without vehicle-to-grid (V2G) scheme have been considered to evaluate the impact of FEVs and PHEVs on the composite power system. IEEE-RTS-79 system is used to examine the proposed probabilistic technique considering different FEV and PHEV penetration levels as well as charging strategies. Simulation results show that even a relatively low penetration level of FEVs or PHEVs might have a significant impact on the system reliability unless a proper charging and/or discharging schemes are utilized.