Electric Vehicles Owners Research Articles

Evaluating EV User Behavior on Aggregator Smart Charging with ESS and Real-Time Pricing-Based Demand Response

With rising global electricity consumption, governments prioritize energy efficiency and the integration of electric vehicles (EVs) into energy markets. This study evaluates EV aggregator strategies using a smart charging method that modulates charging power rates based on user preferences. Simulations in Quito's distribution system assess various actions' impacts on aggregator costs and technical conditions. The study focuses on demand response (DR) strategies, particularly for residential areas, exploring EVs' potential as energy storage via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options. It introduces a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies, incorporating bi-directional EV and energy storage system (ESS) use. A novel mixed-integer linear programming (MILP) model for home energy management (HEM) integrates distributed renewable energy, V2H/V2G capabilities, and two-way ESS energy trading and diverse DR strategies. This comprehensive approach assesses the impact of EV owner preferences and ESS availability on reducing total electricity costs through case studies.

TokenGreen: A Versatile NFT Framework for Peer-to-Peer Energy Trading and Asset Ownership of Electric Vehicles

TokenGreen: A Versatile NFT Framework for Peer-to-Peer Energy Trading and Asset Ownership of Electric Vehicles

A charge-discharge optimization strategy considering the spatiotemporal distribution of electric vehicles and the operational safety of the power distribution network in the power-transportation coupling network

An electric vehicle (EV) charge-discharge optimization (CDO) strategy that accommodates both grid-side and user-side demands is conducive to mitigating the adverse effects of disordered EV charging on the power distribution network (PDN). To tackle the issue of inaccurate estimation of the schedulable capacity of EVs in existing research, a high temporal resolution dynamic spatiotemporal distribution simulation model for EVs is developed. Furthermore, leveraging the characteristics of the power-transportation coupling network (PTCN), a sub-districted dynamic electricity pricing (DEP) is proposed to assist in improving the PDN node voltage distribution. Subsequently, an incentive coefficient is introduced to incentivize the discharge of EVs during potential peak periods. The proposed CDO strategy, considering the non-cooperative behavior of EV owners (EVOs), can provide personalized charge-discharge plans for them. The temporal sequence experiments reveal a significant decrease in the peak-to-valley disparity of the load, achieving an optimization rate of load mean square error that exceeds 80%. The sub-districted DEP exhibits a significant advantage in maintaining PDN voltage stability compared to traditional time-of-use electricity pricing (TOUEP) and DEP, while also achieving reductions in active power losses of more than 15% and 10% for PDN, respectively. Moreover, in comparison to the conventional scenario, the CDO strategy leads to a maximum reduction of up to 942.5% and 22.3% in the total economic cost for electric private cars and electric taxis respectively. Lastly, the impact of seasonal factors is discussed. Numerical results indicate effective alleviation of load peak-valley differences, load fluctuations, and voltage drop phenomena in both winter and summer. Additionally, the maximum reduction in active power losses reaches 20.1% and 21.9%, respectively, while the total economic cost for EVOs participating in the CDO strategy is reduced by up to 97.7% and 114.7%.

Blockchain based crowdsourcing framework for Vehicle-to-Vehicle charging

Electric Vehicles (EVs) have become a promising solution to entirely replace traditional internal combustion engine vehicles. EV owners face the “Range Anxiety” problem which is the fear that their remaining charge would not be enough to reach their destination. Vehicle-to-Vehicle (V2V) charging is currently being used in exchanging energy between EVs. Most of the existing energy V2V frameworks depend on a centralized server that manages the energy exchange. However, these solutions may suffer from privacy leakage and misbehaving platforms. To overcome these limitations, the paper proposes a blockchain-based crowdsourcing framework for V2V charging. The framework features include registering users, maintaining user information, clustering requesters (charging tasks), collecting preferences lists from requesters and providers, and lastly selecting the requester-provider charging pair as well as the corresponding meeting point. These features are implemented using three types of smart contracts: User Management Contract, Task Management Contract, and Solution Management Contract while guaranteeing the transparency of the system and overcoming the main limitations of the centralized solutions. Experiment results with real-life dataset show that the proposed framework achieves a comparable performance at reasonable paid costs and execution times.

Who is buying electric vehicles in Australia? A study of early adopters

ABSTRACT To design relevant policy measures and incentives to accelerate the uptake of electric vehicles in Australia, it is important to understand who the early adopters of electric vehicles are, their reasons for choosing an electric vehicle and their levels of satisfaction in driving, charging locations, home charging and other experiences. This article presents the results of a survey of early adopters in Queensland, Australia. The data have been analysed to understand the socio-demographic profiles of early adopters and to explore variations between two groups of adopters: Tesla owners, having the highest uptake of battery electric vehicles in Australia, and non-Tesla owners of battery electric vehicles and plug-in hybrid electric vehicles. The analysis suggests that both groups of early adopters are predominantly male, university educated and have substantially higher incomes than the state average. Peer and media influences, and having a test drive, play a significant role in owning a Tesla.

Ownership of battery electric vehicles is uneven in Norwegian households

The transition to a zero-emission passenger vehicle fleet has become imperative because of the growing concerns about climate change. Here, we investigate the trends and socioeconomic determinants influencing emitting and battery electric vehicle ownership using longitudinal data of Norwegian households with any vehicle ownership record from 2005 to 2022, accounting for over 2.7 million unique households. Intriguingly, battery electric vehicle ownership has been concentrated on the wealthiest of those owners. Moreover, almost one in ten households once owned battery electric vehicles discontinued ownership by 2022. Our population-level panel data analysis indicates that larger household size, having children, and working away from the residence municipality are positively linked to owning emitting vehicles, while demonstrating the opposite effect regarding battery electric vehicle ownership. Higher income also appears to drive vehicle ownership positively, irrespective of fuel type.

Modelling the determinants of electrical vehicles adoption in Riyadh, Saudi Arabia

This study investigates consumer acceptance of electric vehicles (EVs) in Riyadh, focusing on their response to adoption incentives. Utilising a stated preference survey, a mixed logit model was used in conjunction with survey insights to derive the final findings. Those include finding a higher sensitivity to changes in the monetary values of EVs than conventional vehicles. Also, the presence of accessible charging infrastructure and non-financial incentives (e.g., designated public parking spaces) were found to significantly increase the likelihood of EV adoption. While Saudi participants were less likely than expats to purchase EVs, females and consumers in their 40s showed a likelihood of purchasing an EV. Furthermore, shorter trip durations and personal acquaintance with EV owners in Riyadh positively influenced adoption rates. It is worth noting that the study shows that almost 60% of the targeted population indicated an interest in purchasing a vehicle in the upcoming three years, where the great majority stated that a new vehicle would replace an existing one. This creates a window of opportunity to implement optimal incentivising policies, as once a conventional vehicle is purchased, investment becomes ‘locked’ for years. The study then concludes with policy insights that aim to contribute to the policies of large-scale EV adoption in Riyadh.

WIRELESS E-VEHICLE CHARGING USING INDUCTION COIL

Wireless charging utilizes inductive technology to transfer power without the need for physical cables. Wireless (inductive) charging offers a seamless and convenient experience for EV owners. In the proposed system, receiver coils have been added to maximize charging power. The main purpose of wireless transmission in electric vehicles is to transfer power over a small distance. Wireless transmission technology used to improve efficiency, with power transfer rates comparable to traditional plug-in chargers. Key Words: electric vehicles (EVs), charging power, wireless power transmission system, wireless (inductive) charging

Quantifying the impact of electricity pricing on electric vehicle user behavior: a V2G perspective for smart grid development

ABSTRACT This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm.

Dynamic pricing strategy for electric vehicle charging stations to distribute the congestion and maximize the revenue

Electric vehicle (EV) charging station (CS) congestion is highly dependent on the EV owner’s behavior and their selected CS as charging choice. A fixed pricing strategy causes some CSs to be congested with EVs waiting in line to charge, while there are some other CSs with available electric plugs, which adversely affects both charging station operator (CSO) revenue and EV users’ welfare. To solve this problem, this paper presents a dynamic pricing strategy aimed at conducting EVs from congested CSs to the uncongested ones through controlling the charging prices of CSs at different times. The problem is formulated as a scenario-based stochastic optimization with the objective of maximizing overall revenue of the CSO. Moreover, an attraction function model is developed to quantify the charging choice of the EV owners by considering the effective parameters of CSs in EV charging choice decisions. QGIS software is used in this work to formulate a realistic modeling for CS locations and EV routes to calculate the distances from EVs to CSs. Three scenarios are designed to evaluate the performance of the proposed framework and to compare the results with the fixed pricing approach. The results indicate that the proposed dynamic pricing strategy mitigates the congestion of CSs while facilitating an increased number of charged EVs up to 48% as well as increasing the overall revenue of the CSO.

Comprehensive techno-economic performance assessment of PV-building-EV integrated energy system concerning V2B impacts on both building energy consumers and EV owners

Deploying photovoltaics (PVs) for buildings is an effective solution to decrease carbon emissions but it also imposes a substantial risk of energy mismatch. Battery storage can mitigate this issue to a certain extent but its capacity is limited due to safety concerns and expensive investment. Energy boundary expansion from buildings to an integrated building-transportation is another valuable attempt. However, few studies have quantified the impacts of electric vehicles (EVs) on the PV-building-EV integrated system currently. This study, therefore, conducted a comprehensive techno-economic performance assessment of this integrated system. An energy-sharing mechanism considering EV charging-discharging and PV-building energy system integration is involved in the assessment. According to this energy-sharing mechanism, feasibility studies of this integrated system without stationary battery installation were conducted. An office building integrated with the EV parking area in Chengdu is selected in this study. Results show that the system's techno-economic performance as well as EV battery degradation were comprehensively quantified from two perspectives. From the perspective of the building consumers, renewable energy penetration is enhanced at 45.1% and the reduction of levelized cost of energy (LCOE) is about 38.9%. From the perspective of EV owners, the LCOE of EV charging can decrease by 18.1%. Extra annual EV battery degradation is quantified at 5.23% and an extra compensation of 10.45 CNY for one-time participation of EV owners is calculated. Reasonable compensation for EV owners can effectively increase their motivation to participate in the energy-sharing mechanism. The comprehensive techno-economic performance assessment can provide frontier guidelines for future work.

Collaborative Vehicle-to-Grid Operations in Frequency Regulation Markets

Problem definition: We study the operations of electric vehicles (EVs) providing frequency regulation services to the electric grid in vehicle-to-grid (V2G) systems. In particular, individually owned EVs collaboratively bid in the regulation market, coordinated by a platform that operates the network of charging equipment. We study how the platform determines optimal pricing incentives for drivers to plug in their EVs, accounting for heterogeneous driving schedules. Methodology/results: We model the platform’s pricing optimization problem as a bilevel program: At the upper level, the platform determines hourly rebates for EV owners to plug in their EVs and capacity bids in the regulation market; at the lower level, individual travelers optimize their travel and charging schedules in response to pricing incentives. To account for uncertainties and heterogeneity in regulation market prices and travel patterns, we adopt distributionally robust optimization techniques to formulate the problem as a mixed-integer second-order cone program. We conduct a computational study based on the California Household Travel Survey data set and actual frequency regulation prices. Our results show that the ability to offer time-varying rebates and install workplace chargers can significantly improve the V2G platform’s expected profits. Managerial implications: As EV adoption progresses past the nascent stage, V2G business models become more viable. Successful implementation of V2G provides economic incentive for switching to EVs, potentially helps sustain adoption growth, and complements the growth of renewable power by helping stabilize the grid. Our findings shed light on the design of driver incentives for V2G systems. Funding: R. Tang acknowledges support from the National Natural Science Foundation of China [Grant 72201206]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0133 .

Scheduling of Electric Vehicle’s Power in V2G and G2V Modes Using an Improved Charge–Discharge Opportunity-Based Approach

With rise in the popularity of Electric Vehicles (EVs), challenges related to its charging infrastructure are also soaring. In the upcoming smart grid scenario with significant renewable energy sources and distributed generation, EVs have been identified as a key element for grid stability and its optimal operation, primarily due to energy storage capacity. With a large number of EVs in parking, it has huge potential with optimal scheduling. However, EV owners should have the motivation to participate in Vehicle-to-Grid (V2G). This paper proposes a scheduling mechanism using a novel Forward-Backward energy allocation, based on an improved charge-discharge opportunity approach. The proposed method (PM) addresses the scheduling of power flow in EVs to operate in V2G and Grid-to-Vehicle (G2V) modes considering EV owners’ preferences, optimal cost, and low battery degradation. Implementation of the PM is facile and flexible with the vehicle’s time of arrival and departure. A 40 kWh and 60 kWh battery capacity EVs with multiple real-life scenarios have been considered in this study to demonstrate the effectiveness of the PM. The performance and features of the PM have been compared with existing methods to distinguish its benefits.

Providing Active Charging Services: An Assignment Strategy With Profit-Maximizing Heat Maps for Idle Mobile Charging Stations

In Internet of Electric Vehicles (IoEV), mobile charging stations (MCSs) have been deployed to complement fixed charging stations. Typically, MCSs are assigned to charge the electric vehicles with insufficient electricity which have made charging requests (termed IEVs). Moreover, there are some electric vehicles with insufficient electricity which have not made charging requests (termed quasi-IEVs). If idle MCSs are allowed to actively track quasi-IEVs according to their potential charging demand, then more IEVs could be promptly charged, and thus the charging profits of MCSs could be increased. However, due to the private ownership of electric vehicles, some private information cannot be provided in the potential charging demand of quasi-IEVs (e.g., the destinations and residual electricity), making the potential charging profits of idle MCSs hard to be evaluated, and thereby the proper assignments of idle MCSs are difficult to decide. To this end, we introduce the profit-maximizing heat maps to depict the potential charging demand of quasi-IEVs and evaluate the potential charging profits of idle MCSs. A profit-maximizing heat map remarks the positions around quasi-IEVs and displays them as continuous areas. Specifically, the different shades of colours are used to distinguish the quantities of potential charging profits of idle MCSs, and the sizes of coloured areas are used to indicate the possibility of quasi-IEVs passing through these positions. In this paper, we propose a Profit-Maximizing Assignment Strategy of Idle MCSs (PMASIM) to properly assign the idle MCSs to charge IEVs at selected charging positions, or track some quasi-IEVs according to the profit-maximizing heat maps. Extensive simulations and comparisons demonstrate the superior performance of PMASIM, i.e., with the profit-maximizing heat maps, the charging profits of MCSs are increased, and the proportion of charged IEVs is enhanced as well.

Economic and low-carbon dispatch of industrial integrated energy system with EV load based on Stackelberg game framework

Industrial integrated energy systems (IESs) and electric vehicles (EVs) provide new solutions for addressing the increasing challenges of the energy crisis and environmental pollution. With the increasing number of EVs and smart charging stations in industrial IES, the uncoordinated charging load of EVs imposes significant pressure on IES. Therefore, a well-designed dispatch scheme is crucial for reducing the economic cost for both parties, alleviating the energy supply pressure on IES, and promoting the development of a low-carbon society. To this end, given the load characteristics of EVs in industrial IES, we propose a dispatch framework based on the Stackelberg game theory, which includes the leader and the follower. The leader IES is responsible for formulating both unit dispatch and demand response plans, as well as determining the charging pricing for the smart charging station. The follower smart charging station optimizes EVs charging power by minimizing the charging cost in order to protect the interest of EV owners. Additionally, we introduce the carbon emission flow model into charging station pricing to shift the responsibility for carbon emissions from the generation side to the EV load side. Considering that the two-layer game model is difficult to solve, we use the Karush–Kuhn–Tucker condition and duality theorem to transform it into an equivalent single-layer optimization problem, which is easily solved. Simulation results demonstrate that the proposed game framework effectively reduces the economic cost of IES and the charging cost of EVs, alleviates the pressure from charging load, and reduces the carbon emissions of industrial IES.

Does The BBN KB Incentive Policy Increase Ownership Of Battery-Based Electric Vehicles? Indonesia Case Study

Indonesia's commitment by 2060 is to reduce greenhouse gas emissions and realize net zero emissions. The transition of electric vehicles in the transportation sector with renewable energy is a solution to reduce emissions. However, the ownership rate of battery-based electric vehicles in Indonesia is still low. This study empirically discusses the effect of the BBN KB incentive policy designed to encourage ownership of battery-based electric vehicles in Indonesia. To determine the effect of the BBN KB incentive policy on battery-based electric vehicle ownership, a Moderated Regression Analysis (MRA) panel data analysis with Pooled Ordinary Least Square (POLS) estimation technique is used using secondary data from 34 provinces in Indonesia from 2019-2022. The results of the analysis found that the BBN KB incentive policy has a significant effect on increasing ownership of electric vehicles, especially battery-based electric cars in Indonesia. The factors of the number of charging infrastructure (SPKLU), consumer awareness, fuel prices and open unemployment rates have a significant influence on ownership of battery-based electric motorized vehicles. Analysis on the island of Java explains that there is a significant effect on increasing ownership of battery-based electric car vehicles after the policy is implemented, while on non-Java island the BBN KB incentive policy shows an insignificant impact on increasing ownership of battery-based electric vehicles, especially 4-wheelers.

Risk-based dynamic pricing by metaheuristic optimization approach for electric vehicle charging infrastructure powered by grid integrated microgrid system

This paper presents a risk-based dynamic pricing strategy for the microgrid based electric vehicle charging station. The microgrid is composed of renewable resources such as solar energy, wind energy and fuel cells. The proposed microgrid-based charging station operates off-grid and on-grid depending on the charging demand and trades the electricity with the main grid when needed. In this work, to increase the reliability and to decrease the detrimental impact of electric vehicle load on the distribution network electric vehicles act as a source of flexibility through grid to vehicle and vehicle to grid provisions. The uncertainty of solar and wind sources is a key issue that is efficiently addressed in this work along with one of the electric vehicles load uncertain factors such as state of charge variation using a probabilistic method. Two types of competent time-based dynamic pricing strategies are employed in this work to enhance the collective profit and to curtail the peak-to-valley ratio of charging station and electricity purchase cost of the electric vehicle owner. To deal with the uncertainty in the electricity market and its associated risk, the average value-at-risk is applied. The proposed multi-objective problem is tested on an IEEE 33 bus-modified system using MATLAB software. A metaheuristic fire hawk optimization technique is employed to implement dynamic pricing on charging station for two cases. To observe the efficiency of the applied algorithm, the findings of this work are compared with the other techniques described in the literature.

Variability in costs of electrifying passenger cars in Canada

The high cost of purchasing electric vehicles (EVs) compared to internal combustion engine vehicles (ICEVs) is a major barrier to their widespread adoption. Additionally, the price disparity is not the same for all households. We conducted a total cost of ownership (TCO) analysis to compare the net present value of EV versus ICEV ownership for various household categories across Canada. We observed spatial and behavioral factors, including variations in costs of electricity, temperature, household archetypes and their purchase decisions, and access to charging infrastructure. We found that EVs are more cost-effective than ICEVs for certain daily driving distances, but typical households in Canada generally do not drive enough for lifecycle costs of EVs to be less than ICEVs. The province of Quebec has the most favorable conditions for EV ownership due to high purchase subsidies and low electricity prices. Variability in costs across other provinces and territories is mainly due to differences in rebates, electricity and gasoline prices, and tax rates. Our findings have implications for policymakers and consumers. For consumers comparing ICEVs to EVs based on a fixed budget, which may be consistent with how many households frame their purchase decision, willingness to accept smaller, non-luxury EVs can result in large cost savings. We also find that although temperature variation has a minimal effect on TCO, it does impact the ‘number of charge-ups’—a metric that we introduce to compare how many charging cycles a user may expect over the lifetime of a vehicle. The policy implication of this would be a need to consider regional differences in cold weather patterns when planning charging infrastructure deployment and the extent to which households in shared dwellings may face additional costs. Lastly, our findings strengthen the argument that equitably decarbonizing transportation will also require investment in strategies other than electrifying personal vehicles.

Estimating of energy consumption of electric vehicles under different road characteristics: a case study for Nanjing, China

ABSTRACT With the continuous growth of electric vehicle ownership, energy consumption has become an important factor restricting its development. Real-time and accurate energy consumption estimation is essential to alleviate range anxiety and help with energy-efficient route planning. This study aims to explore the factors affecting energy consumption, and estimate the energy consumption of electric vehicles under different road characteristics. Based on the real-world driving data of electric vehicles in Nanjing over a long period, this study analyzed the influencing factors of energy consumption, including trip conditions, weather, driving behavior, and road characteristics. Based on feature selection and correlation analysis, an energy consumption estimation framework for electric vehicles was proposed to estimate energy consumption under different road characteristics using extreme gradient boosting. The results show that energy consumption is significantly affected by trip conditions, weather, driving behavior, and road characteristics. The modeling of different road characteristics helps improve the accuracy of model estimation. The error of the model proposed in this paper is within the reasonable range (−0.25–0.25 kWh/km), which is better than the random forest and k-nearest neighbor models.

Electric Vehicle Adoption Barriers in Thailand

Adopting electric vehicles (EVs) is a potential solution to reduce emissions and protect the environment. Although countries encourage people to use EVs to replace gasoline and diesel vehicles, the application of EVs still needs to overcome many barriers, especially in developing countries such as Thailand. This study aims to identify critical barriers that hinder the adoption of EVs not only among EV owners but also among non-EV owners. First, two rounds of the modified Delphi method were used to identify significant barriers through expert opinions. The decision-making trial and evaluation laboratory (DEMATEL) method was then applied to determine their relationship. Among financial, technical, infrastructure, and policy dimensions, industrial experts agree about 14 significant barriers to the adoption of EVs. The results show that the long charging duration, limited driving range, and lack of charging stations are the most critical barriers affecting Thai customers adopting EVs. This study’s findings will help manufacturers and policymakers understand customer requirements and develop appropriate strategies to improve the adoption of EVs.