Assessment of electric vehicles concerning impacts,charging infrastructure with unidirectional and bidirectional chargers,and power flow comparisons

The advancement and deployment of electric vehicle (EV) technologies are considered as an emergent solution to meet the current and future energy crises. The electrification of transportation systems is a promising approach to green the transportation systems and to reduce the issues of climate change. This paper investigates the present status, latest deployment, and challenging issues in the implementation of EV infrastructure, charging power levels, in conjunction with several charging power topologies, and analyzes EV impacts and prospects in society. In this study, the on‐board and off‐board categories of charging systems with unidirectional and bidirectional power flow comparison are addressed. Moreover, an extensive analysis of unidirectional and bidirectional chargers is presented. Unidirectional charging offers hardware limitation and reduces the interconnection issues. Bidirectional charging provides the fundamental feature of vehicle‐to‐grid technology. Furthermore, the beneficial and harmful impacts of EVs are categorized with remedial measures for harmful impacts and prolific benefits for beneficial impacts.     

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

The integration of intermittent renewable energy sources coupled with the increasing demand of electric vehicles (EVs) poses new challenges to the electrical grid. To address this, many solutions based on demand response have been presented. These solutions are typically tested only in software‐based simulations. In this paper, we present the application in hardware‐in‐the‐loop (HIL) of a recently proposed algorithm for decentralised EV charging, prediction‐based multi‐agent reinforcement learning (P‐MARL), to the problem of optimal EV residential charging under intermittent wind power and variable household baseload demands. P‐MARL is an approach that can address EV charging objectives in a demand response aware manner, to avoid peak power usage while maximising the exploitation of renewable energy sources. We first train and test our algorithm in a residential neighbourhood scenario using GridLAB‐D, a software power network simulator. Once agents learn optimal behaviour for EV charging while avoiding peak power demand in the software simulator, we port our solution to HIL while emulating the same scenario, in order to decrease the effects of agent learning on power networks. Experimental results carried out in a laboratory microgrid show that our approach makes full use of the available wind power, and smooths grid demand while charging EVs for their next day's trip, achieving a peak‐to‐average ration of 1.67, down from 2.24 in the baseline case. We also provide an analysis of the additional demand response effects observed in HIL, such as voltage drops and transients, which can impact the grid and are not observable in the GridLAB‐D software simulation.     

Optimal decentralized charging control algorithm for electrified vehicles connected to smart grid

Electrified vehicles (EV) and renewable power sources are two important technologies for sustainable ground transportation. If left unmitigated, the additional electric load could over-burden the electric grid. Meanwhile, a challenge for integrating renewable power sources into the grid lies in the fact their intermittency requires more regulation services which makes them expensive to deploy. Fortunately, EVs are controllable loads and the charging process can be interrupted. This flexibility makes it possible to manipulate EV charging to reduce the additional electric load and accommodate the intermittency of renewable power sources. To illustrate this potential, a two-level optimal charging algorithm is designed, which achieves both load shifting and frequency regulation. Load shifting can be realized through coordination of power generation and vehicle charging while reducing power generation cost and carbon dioxide emissions. To ensure practicality, a decentralized charging algorithm for load shifting is formulated by emulating the charging pattern identified through linear programming optimization solutions. The frequency regulation is also designed based on frequency droop that can be implemented in a decentralized way. The two control objectives can be integrated because they are functionally separated by time scale. Simulation results are presented to demonstrate the performance of the proposed decentralized algorithm.     

Regulatory framework and business models for charging plug-in electric vehicles: Infrastructure,agents, and commercial relationships

Electric vehicles (EVs) present efficiency and environmental advantages over conventional transportation. It is expected that in the next decade this technology will progressively penetrate the market. The integration of plug-in electric vehicles in electric power systems poses new challenges in terms of regulation and business models. This paper proposes a conceptual regulatory framework for charging EVs. Two new electricity market agents, the EV charging manager and the EV aggregator, in charge of developing charging infrastructure and providing charging services are introduced. According to that, several charging modes such as EV home charging, public charging on streets, and dedicated charging stations are formulated. Involved market agents and their commercial relationships are analysed in detail. The paper elaborates the opportunities to formulate more sophisticated business models for vehicle-to-grid applications under which the storage capability of EV batteries is used for providing peak power or frequency regulation to support the power system operation. Finally penetration phase dependent policy and regulatory recommendations are given concerning time-of-use pricing, smart meter deployment, stable and simple regulation for reselling energy on private property, roll-out of public charging infrastructure as well as reviewing of grid codes and operational system procedures for interactions between network operators and vehicle aggregators.     

基于动态规划的电动汽车用户侧充电优化方法

实现电动汽车有序充电是将电动汽车纳入智能电网的重要内容之一。从电动汽车用户需求角度出发,提出了以用户为充电过程决策主体的有序充电方法。以充电费用最低为目标建立了用户侧优化模型,并应用动态规划建立了求解方法。按照给定算例,对电动汽车的充（放）电过程进行了决策。计算结果表明,在满足功率约束和用户充电目标的前提下,通过用户优化控制策略可有效减少电动汽车充电过程产生的费用。该文提供了电动汽车有序充电的用户层方案。     

Aggregator-based demand response mechanism for electric vehicles participating in peak regulation in valley time of receiving-end power grid

With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles (EVs), an aggregator-based demand response (DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator (ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.     

A comparative review on power conversion topologies and energy storage system for electric vehicles

Fossil fuel depletion and its adverse impact on global warming is a major driving force for a recent upsurge in the development of hybrid electric vehicles technologies. This paper is a conglomeration of the recent literature in the usages of an energy storage system and power conversion topologies in electric vehicles (EVs). An EV requires sources that have high power and energy density to decrease the charging time. Commonly used energy storage devices in EVs are fuel cells, batteries, ultracapacitors, flywheel, and photovoltaic arrays. The power output from energy storage sources is conditioned to match load characteristics with the source for maximum power delivery. A DC-DC converter topology performs this task by way of transforming voltage under the condition of power invariance. In addition, power electronics is also required to power DC/AC motors efficiently with precise control as these motors provide tractive efforts and acts as prime movers. This paper therefore brings out a critical review of the literature on EV's power conversion topologies and energy storage systems with challenges, opportunities and future directions by systematic classification of EVs and energy storage.     

不同模式下电动汽车充电负荷及充电设施需求数量计算

电动汽车充电设施技术路线的选择可从多种角度出发。基于现有充电设施特性和用户需求分析,对常规充电、快速充电、电池更换3种模式下的充电负荷进行了建模。应用蒙特卡罗仿真方法计算了3种模式下一定规模电动汽车在一日内的充电负荷曲线。根据一日中最大在线充电汽车数量得出快速充电设施的数量需求,根据换电站内充满电和正在充电电池数量的计算,得出换电站电池配置数量需求。计算结果表明,由于充电相对集中,一定规模电动汽车采用慢速充电时其电力需求最大。相比于慢速充电,快速充电和电池更换模式一定程度上提高了设施的利用率,但为了保证换电服务,换电站需配置足够数量的电池。     

A price‐regulated electric vehicle charge‐discharge strategy for G2V,V2H,and V2G

Electric vehicles (EVs) and smart grids are gradually revolutionising the transportation sector and electricity sector respectively. In contrast to unplanned charging/discharging, smart use of EV in home energy management system (HEMS) can ensure economic benefit to the EV owner. Therefore, this paper has proposed a new energy pricing controlled EV charging/discharging strategy in HEMS to acquire maximum financial benefit. EV is scheduled to be charged/discharged according to the price of electricity during peak and off‐peak hours. In addition, two different types of EV operation modes, ie, grid‐to‐vehicle (G2V) in off‐peak time and vehicle‐to‐home (V2H) in on‐peak time are considered to determine comparative economic benefit of planned EV charging/discharging. The real load profile of a house in Melbourne and associated electricity pricing is selected for the case study to determine the economic gain. The simulation results illustrate that EV participating in V2H contributes approximately 11.6% reduction in monthly electricity costs compared with G2V operation mode. Although the facility of selling EV energy to the grid is not available currently, the pricing controlled EV charging/discharging presented in the paper can be used if such facility becomes available in the future.     

基于两阶段优化模型的电动汽车充电站放电策略

随着电动汽车的普及,合理制定其充放电策略,实现主动配电网和充电站的双赢成为电动汽车负荷并网的研究重点,为此提出一种充电站充放电计划的两阶段优化模型.该模型综合考虑了主动配电网和充电站双方的利益,并计及了电动汽车实际负荷与预测负荷不符的情况.在日前优化阶段,以成本分析为基础,采用Nash谈判法求解配电网和充电站的多目标优...     

An indirect RUL prognosis for lithium-ion battery under vibration stress using Elman neural network

Remaining useful life (RUL) prognosis of lithium-ion battery can appraise the battery reliability to determine the advent of failure and mitigate risk. To acquire measurement data at similar working conditions as electrical vehicles (EVs), this paper mainly conducted the experiment about battery charging and discharging under vibration stress. Indirect health indicator (HI) was extracted from the time of equal discharge voltage from the upper to the lower, and the battery capacity proved to be estimated by the adopted indirect HI through grey relational analysis. Then, the RUL prognosis model based on Elman neural network was established. Finally, the feasibility of this RUL prognosis model based on Elman neural networks in an application in predicting RUL of battery under vibration stress was verified.     

Optimal operation of an all-in-one EV station with photovoltaic system including charging,battery swapping and hydrogen refueling

The issue of electrification of transportation is discussed due to the possibility of depletion of conventional resources in the near future and environmental problems caused by carbon emissions. For this purpose, different options have been proposed for the electrification of electric vehicles (EVs). Each potential EV user can choose a different EV type according to his desire, so different EV types can be seen in the environment. However, one of the most important reasons why the prevalence of EVs has not increased is the scarcity of EV charging, swapping, or refueling stations. In this respect, there is a need for an all-in-one EV station (AiOEVS) that can serve all types of EVs around and that all users know to be able to meet their energy needs easily and in line with their wishes. In this study, the economically optimum energy management model via mixed-integer linear programming (MILP) approach of an AiOEVS including a photovoltaic (PV) system as well electrolyzer and consisting of three different parts (charging for plug-in EVs, swapping for swappable EVs, and refueling for hydrogen fuel-cell EVs (HFCEVs)) is proposed. Besides, energy is purchased from the grid with time-of-use electricity prices. The proposed optimum operating framework is beneficial for each party. Furthermore, the hydrogen tank, swappable batteries, and long-parking plug-in EVs provide operational flexibility. The AiOEVS owner obtains a net profit of 33.12% at the end of the day. Furthermore, when the capacity of the PV is doubled or tripled, the gain increases by 11.69% or 23.41%, respectively.     

Multicriterion optimal electric drive vehicle selection based on lifecycle emission and lifecycle cost

This research paper examines the optimal choice among conventional gasoline vehicles, hybrid electric vehicles (HEVs), plug‐in HEVs (PHEV), and full‐battery EVs taking into account the different characteristics of these vehicles, such as cost, emissions per mile, and vehicle miles to be traveled between refueling and acceleration time. The existing challenges for wide‐spread deployment of EVs are availability of charging infrastructure, higher cost, long time for charging, and lower travel millage compared with conventional vehicles. Statistical data are considered for determining the spatially varying average daily vehicle miles traveled (VMT) across the United States, which, together with charging behavior, can influence the optimal choice among EV with different travel ranges. Two alternative cases for charging are examined: (1) home‐only charging and (2) home plus work charging. The motivation of this work is to select the optimal EV among their types when lifecycle cost and lifecycle emission are considered. The optimization model seeks to minimize total lifecycle cost and emissions for each level of VMT per day. It is found that when lifecycle cost is the sole objective, HEV is usually the best choice, especially for higher VMT levels. When lifecycle greenhouse gas emission is the sole objective, PHEV1 (PHEV with 1 charging station) is the optimal solution over a wide range of VMTs. The outcome of this provides a roadmap for the selection of EVs based on their annual VMT to reduce both lifecycle emission and lifecycle cost.     

Methodology for modelling plug-in electric vehicles in the power system and cost estimates for a system with either smart or dumb electric vehicles

The article estimates the costs of plug-in electric vehicles (EVs) in a future power system as well as the benefits from smart charging and discharging EVs (smart EVs). To arrive in a good estimate, a generation planning model was used to create power plant portfolios, which were operated in a more detailed unit commitment and dispatch model. In both models the charging and discharging of EVs is optimised together with the rest of the power system. Neither the system cost nor the market price of electricity for EVs turned out to be high (36-263 €/vehicle/year in the analysed scenarios). Most of the benefits of smart EVs come from smart timing of charging although benefits are also accrued from provision of reserves and lower power plant portfolio cost. The benefits of smart EVs are 227 €/vehicle/year. This amount has to cover all expenses related to enabling smart EVs and need to be divided between different actors. Additional benefits could come from the avoidance of grid related costs of immediate charging, but these were not part of the analysis.     

Electric vehicle charging in stochastic smart microgrid operation with fuel cell and RES units

Plug-in electric vehicles increasingly augment their share in the global market as they appear to be an economic and emission-free alternative to modern means of transportation. As their presence strengthens, ways that will ensure economic charge along with uninterrupted grid operation are necessary to be found. This paper aims to approach the economic optimization problem that includes several Electric Vehicles (EVs) within a Low Voltage (LV) network comprising various Distributed Energy Resources (DER) as fuel cell, Renewable Energy Sources (RES), (photovoltaics, wind turbine) etc. via a scenario based simulation. The purpose is to investigate the main variables of the grid, such as its operating cost, charging patterns, power injection from the upstream network, resulting from the coordinated control of DER in Smart Microgrid operation in conjunction to the flexible load the controlled EV charging introduces. The base case study is that of absence of EVs, and therefore the demand is met only by the upstream network and the DER units. Subsequently, EVs are introduced as controllable loads and finally as dispatchable storage units incorporating a Vehicle to Grid (V2G) capability to the Smart Microgrid. Furthermore, the problem is not tackled deterministically and although forecasts for all network parameters are assumed to be known, forecasting errors and stochastic driver patterns cannot be ignored. Thus, for each imposed policy, a scenario based approach is implemented to determine operating cost in various cases along to DER utilization and the effect EVs bear on these results.     

Impact assessment of electric vehicle charging/discharging strategies on the operation management of grid accessible and remote microgrids

This article deals with impact analysis of different electric vehicle (EV) charging/discharging strategies (CDS) on the operation and pollutant treatment cost of both grid accessible and remote microgrid (MG) modes. In this regard, EV demand is developed under four different scenarios, namely, uncoordinated charging model (UCM), load leveling model (LLM), maximum renewable model (MRM), and charging discharging model (CDM). A comprehensive study is performed to see the effect of these different EV charging/discharging behaviors in optimizing MG's operation. A 2m scheme of Hong's point estimate method (PEM) is applied to examine the effect of uncertainties linked with the forecasted errors in load demand, solar energy, wind energy, and grid price respectively on MG operation problem. Finally, a sensitivity analysis is performed to investigate the effect of variations in battery parameters on economics of remote MG. The study results indicate that controlled charging of EVs can substantially improve operation of MG.     

GGlueVaR‐based participation of electric vehicles in automatic demand response for two‐stage scheduling

Due to the uncertainty of the external situation and the varied ability of electric vehicle (EV) owners to understand and process information, the demand response optimization method is not timely and flexible enough. This article puts forward a two‐stage electric vehicle automatic demand response (ADR) optimization method based on generalized Glue value‐at‐risk (GGlueVaR) to solve existing problems. First, a two‐stage electric vehicle ADR optimization method is proposed considering both the EV owner ' s benefit and network load fluctuation. In the process of ADR, different risk preferences of electric vehicle owners affect the EV owner participation in ADR. Second, the GGlueVaR‐based EV owner willingness decision model is adopted to measure an EV group's risk attitude. Finally, a case study is provided to verify the effectiveness of the proposed method. Results show that the proposed model reduced the average charging cost of EV owners by 45% and increased the profit resulting from DR by 91% compared with the price‐based demand response model. Therefore, the proposed model is more efficient than disorder charging model. The method is timelier and more flexible compared with other prior demand response optimization methods.     

A critical review of electric vehicle charging using solar photovoltaic

The application of renewable sources such as solar photovoltaic (PV) to charge electric vehicle (EV) is an interesting option that offers numerous technical and economic opportunities. By combining the emission‐free EV with the low carbon PV power generation, the problems related to the greenhouse gases due to the internal combustion engines can be reduced. Over the years, numerous papers, including several review work, have been published on EV charging using the grid electricity. However, there seems to be an absence of a review paper on EV charging using the PV as one of the energy sources. With growing interest in this topic, this review summarizes and updates some of the important aspects of the PV‐EV charging. For the benefit of a wider audience, it provides the background on the EV fundamentals, batteries and a brief overview on the PV systems. Two types of PV‐EV charging, namely the PV‐grid and the PV‐standalone, are comprehensively covered. Moreover, a case study is carried out in comparison to the grid‐only charging to critically analyse the technical and the economical feasibilities of both types using Matlab simulation. At the end, recommendations and future directions are presented. It is envisaged that the material gathered in this paper will be a valuable source of information for the researchers working on this topic. Copyright © 2015 John Wiley & Sons, Ltd.     

电动汽车电池的充电状态和健康状况评估：关键问题和挑战

使用电动汽车（EV）进行运输被视为实现可持续发展和解决环境问题的必要组成部分。当前对环境的关注,例如化石燃料的快速消耗,空气污染的增加,能源需求的加速增长,全球变暖和气候变化,为交通运输部门的电气化铺平了道路。电动汽车可以解决上述问题。电源已成为电动汽车发展的关键,尤其是锂离子（Li-ion）电池。由于其能量密度、功率...     

A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system

This paper describes a new adaptive neuro-fuzzy inference system (ANFIS) model to estimate accurately the battery residual capacity (BRC) of the lithium-ion (Li-ion) battery for modern electric vehicles (EVs). The key to this model is to adopt newly both the discharged/regenerative capacity distributions and the temperature distributions as the inputs and the state of available capacity (SOAC) as the output, which represents the BRC. Moreover, realistic EV discharge current profiles are newly used to formulate the proposed model. The accuracy of the estimated SOAC obtained from the model is verified by experiments under various EV discharge current profiles.