Population Of Electric Vehicles Research Articles

The Influence of Incentives, Quality, and Product Availability on Attitude toward Purchase Decision of Battery Electric Vehicle at the Greater Jakarta

Transport and mobility have significant contributions to the emissions in urban cities in Indonesia. The Indonesian government has supported the adoption of electric vehicles (EVs) as part of the commitment to zero emissions by 2060. According to The Association of Indonesia Automotive Industries (GAIKINDO), in 2022 the electric vehicle contributes 1.49% of the total domestic passenger car sales in Indonesia. This EV population was too small compared to what the government targeted for the adoption of EVs. This study aims to analyze the influence of government incentives, product quality, and product availability on consumer’s attitudes toward purchase decisions of battery electric vehicles (BEV). A sample of 149 respondents, who have a driving license, live in the greater area of Jakarta were collected through a questionnaire-based primary data. Through a rigorous procedure of structural equation modeling (Smart-PLS), it is found that variables of government incentives and product quality have no significant effect on consumer purchase decision for BEVs. Product availability and attitude variable has positive and significant effect on consumer purchase decision for BEVs. Moreover, the product availability variable is imperative since it is found to be the most significant variable construct. Thus, as the managerial implication, BEV dealer and manufacturer could consider the availability of various choices of electric cars with the price and driving range capabilities. Future research is recommended to cover all types of EVs, collect wider respondents on different areas and also explore different variables that influence EV customer purchase decision.

Dynamic scheduling of electricity demand for decentralized EV charging systems

The rapid growth of electric vehicles (EVs) has brought forth new challenges to the power grid. Further, the simultaneous charging of EVs could lead to peak demand, potentially causing overloading, voltage swings, and other grid-related problems. To address these issues and lower the high energy costs faced by EV owners and grid operators, EV charging must be optimized. The study proposes an innovative strategy that utilizes decentralized charging systems to lessen the impact of EVs on the grid. A decentralized EV scheduling strategy offers scalability. Thus, making it suitable for a large EV population and it remains resilient to the dynamic arrivals of the EVs. The approach aims to balance the load on the grid and improve the effectiveness of charging operations. To achieve this, a convex optimization problem has been developed to effectively regulate the charging procedure, taking into account the distinct attributes of each EV. The mechanism operates by dividing time into several intervals. Each electric vehicle in the system autonomously adjusts its charging rate during the assigned time slots, with the goal of minimizing individual charging expenses. Moreover, the system demonstrates flexibility in deciding when to charge and discharge, allowing prioritization based on individual EV battery levels and power grid conditions. As a result, the cost analysis was conducted using the number of EVs and the average group size. A comparison of computational times between centralized and decentralized systems was undertaken to demonstrate the efficacy of the system.

Maximizing EV profit and grid stability through Virtual Power Plant considering V2G

The electrification of transportation through the widespread adoption of electric vehicles (EVs) has raised substantial concerns within the realm of power grid operations. This concern predominantly stems from the elevated electricity demand brought about by the surging population of EVs, consequently exerting strain on the power grid infrastructure which can be reduced with vehicle-to-grid (V2G) technology integration. To address this issue, this paper delves further into the realm of grid integration by introducing a Virtual Power Plant (VPP) concept to enhance the synergy between EVs and power grid. This study aims to compare different realistic objectives, ranging from total active power loss and voltage drop minimization to EV profit maximization and then optimize the balance between the distribution grid power quality and VPP profit through bi-level modeling. The presented model is devised as mixed-integer quadratically constrained programming (MIQCP) and incorporates Temporal Convolutional Network (TCN) based forecasting to handle the uncertain behavior of the residential loads using historical data. The experiments are conducted in IEEE 33-Bus and real-world 240-Bus distribution networks. The results indicate that enabling bidirectional power flow between the grid and VPP can yield significant profits for EV users while only marginally impacting the active power loss, approximately around 5%. This validation underscores how V2G not only presents various advantages for power system operators but also benefits EV users simultaneously.

Techno-Economic Planning of a Fully Renewable Energy-Based Autonomous Microgrid with Both Single and Hybrid Energy Storage Systems

This paper presents both the techno-economic planning and a comprehensive sensitivity analysis of an off-grid fully renewable energy-based microgrid (MG) intended to be used as an electric vehicle (EV) charging station. Different possible plans are compared using technical, economic, and techno-economic characteristics for different numbers of wind turbines and solar panels, and both single and hybrid energy storage systems (ESSs) composed of new Li-ion, second-life Li-ion, and new lead–acid batteries. A modified cost of energy (MCOE) index including EVs’ unmet energy penalties and present values of ESSs is proposed, which can combine both important technical and economic criteria together to enable a techno-economic decision to be made. Bi-objective and multi-objective decision-making are provided using the MCOE, total met load, and total costs in which different plans are introduced as the best plans from different aspects. The number of wind turbines and solar panels required for the case study is obtained with respect to the ESS capacity using weather data and assuming EV demand according to the EV population data, which can be generalized to other case studies according to the presented modelling. Through studies on hybrid-ESS-supported MGs, the impact of two different global energy management systems (EMSs) on techno-economic characteristics is investigated, including a power-sharing-based and a priority-based EMS. Single Li-ion battery ESSs in both forms, new and second-life, show the best plans according to the MCOE and total met load; however, the second-life Li-ion shows lower total costs. The hybrid ESSs of both the new and second-life Li-ion battery ESSs show the advantages of both the new and second-life types, i.e., deeper depths of discharge and cheaper plans.

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

The rising popularity of electric vehicles (EVs) can be attributed to their positive impact on the environment and their ability to lower operational expenses. Nevertheless, the task of determining the most suitable EV types for a specific site continues to pose difficulties, mostly due to the wide range of consumer preferences and the inherent limits of EVs. This study introduces a new voting classifier model that incorporates the Al-Biruni earth radius optimization algorithm, which is derived from the stochastic fractal search. The model aims to predict the optimal EV type for a given location by considering factors such as user preferences, availability of charging infrastructure, and distance to the destination. The proposed classification methodology entails the utilization of ensemble learning, which can be subdivided into two distinct stages: pre-classification and classification. During the initial stage of classification, the process of data preprocessing involves converting unprocessed data into a refined, systematic, and well-arranged format that is appropriate for subsequent analysis or modeling. During the classification phase, a majority vote ensemble learning method is utilized to categorize unlabeled data properly and efficiently. This method consists of three independent classifiers. The efficacy and efficiency of the suggested method are showcased through simulation experiments. The results indicate that the collaborative classification method performs very well and consistently in classifying EV populations. In comparison to similar classification approaches, the suggested method demonstrates improved performance in terms of assessment metrics such as accuracy, sensitivity, specificity, and F-score. The improvements observed in these metrics are 91.22%, 94.34%, 89.5%, and 88.5%, respectively. These results highlight the overall effectiveness of the proposed method. Hence, the suggested approach is seen more favorable for implementing the voting classifier in the context of the EV population across different geographical areas.

Parameter tuning of EV drivers' charging behavioural model using machine learning techniques

The charging behaviour of electric vehicle (EV) drivers significantly influences planning of the future deployment of public charging stations (CSs). Thus, identifying the EV drivers' charging behaviour plays a major role in CS management and development. In this regard, some parametric behavioural Markov models (BMMs) introduced in the literature have acceptable performance with tuned parameters. Enjoying the benefits of these BMMs needs accurate and feasible parameter tuning. To address this challenge, we propose a machine learning-based method to tune the parameters of such a BMM dynamically. A Deep Q-Network (DQN) algorithm is an appropriate solution in which the reward function is designed based on the statistical resemblance between the EV plug-in and charging times derived from CS simulation with their equivalents derived from the CS charging data. The evaluation results based on the real charging data demonstrate the convergence of the proposed algorithm and validate the accuracy of the adapted behavioural parameters. Accurately adapting the model parameters is an essential prerequisite for designing a system that identifies the EV drivers' behaviour. This novel system helps control the CS congestion and predict the CS requirements when the EV population grows in the future.

The impact of electric vehicle charging infrastructure on the energy demand of a city

With the increased market penetration of electric vehicles (EVs), energy consumption is growing at a quicker rate. Electric vehicles are slowly but surely gaining a foothold in India’s rapidly expanding market. With more EVs on the road, there will be more demand for charging and more energy use in some places, which can put a lot of stress on the power grid. In the absence of sufficient planning, there is a risk of power system interruption and failure. Therefore, the need for energy in cities will shift in light of the advent of EVs in the next decade or two. Accordingly, Surat has been chosen as the research region for the planning of charging infrastructure in the current study. After reviewing the literature, primary data was acquired to estimate citizen travel demands. Vehicle registration statistics were compiled from the Regional Transport Office (RTO) for the purpose of EV forecasting. After collecting data, the EV population and demand for public charging stations by 2025 and 2030 were calculated. This study aims to meet the public charging demand in the city of Surat by analyzing the electric vehicle growth rate and vehicle forecast. This anticipated data was then utilized to compute the energy demand consumption for Surat city by calculating vehicle kilometers traveled. The demand for public charging comes out to be 42.4 MWh/day in 2025 and 358.05 MWh/day in 2030, respectively. The study’s objective is to determine energy demand per region in order to better understand grid planning due to the imbalance in electricity demand.

An Extended State Space Model for Aggregation of Large-Scale EVs Considering Fast Charging

This article presents an extended state space model for aggregation of large-scale electric vehicles (EVs) for frequency regulation and peak load shaving in power systems. The proposed model systematically deals with the fast charging of EVs as an effective solution for immediate charging requirements. Furthermore, the proposed extended state space model increases the flexibility of the EV aggregator (EVA) by enabling the EVs to participate in ancillary services with both regular and fast charging/discharging rates. This will help the EVA to provide a prompt and efficient response to severe generation-consumption imbalances. A probabilistic control approach is developed which reduces the communication burden of the EVA. Furthermore, the uncertainties related to EV users' behavior are modeled in real-time. The simulations are conducted for a typical power system including a large population of EVs, a conventional generator (CG), and a wind generation system. It is shown that the proposed aggregation model can accurately describe the aggregated behavior of a large population of EVs enabling them to efficiently participate in frequency regulation and peak load shaving services. Finally, the performance of EVA is evaluated for different driving behaviors and state of charge (SOC) levels of the EV population.

Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach

Electric vehicles (EVs) penetration growth is essential to reduce transportation-related local pollutants. Most countries are witnessing a rapid development of the necessary charging infrastructure and a consequent increase in EV energy demand. In this context, power demand forecasting is an essential tool for planning and integrating EV charging as much as possible with the electric grid, renewable sources, storage systems, and their management systems. However, this forecasting is still challenging due to several reasons: the still not statistically significant number of circulating EVs, the different users’ behavior based on the car parking scenario, the strong heterogeneity of both charging infrastructure and EV population, and the uncertainty about the initial state of charge (SOC) distribution at the beginning of the charge. This paper aims to provide a forecasting method that considers all the main factors that may affect each charging event. The users’ behavior in different urban scenarios is predicted through their statistical pattern. A similar approach is used to forecast the EV’s initial SOC. A machine learning approach is adopted to develop a battery-charging behavioral model that takes into account the different EV model charging profiles. The final algorithm combines the different approaches providing a forecasting of the power absorbed by each single charging session and the total power absorbed by charging hubs. The algorithm is applied to different parking scenarios and the results highlight the strong difference in power demand among the different analyzed cases.

Multiparadigm Modeling Framework to Evaluate the Impacts of Travel Patterns on Electric Vehicle Battery Lifespan

The widespread adoption of electric vehicles (EVs) can help attain economic and environmental sustainability by reducing oil dependency and greenhouse gas emissions. However, several issues need to be addressed before EVs can become a popular vehicle choice among the general public. A key issue is the perpetual reduction in EV battery capacity caused by battery degradation over time with usage. This can lead to a reduced driving range and cause “range anxiety” for EV drivers. This becomes even more critical in developing countries where consumers are highly sensitive to battery replacement costs. Thus, to promote EVs in developing economies, policymakers and vehicle manufacturers need to develop attractive incentive schemes and warranty strategies preceded by a thorough assessment of the useable EV battery lifespan for a wide range of users. This paper develops a multiparadigm modeling framework to compute battery degradation for a large population of EVs by capturing the effects of travel patterns, traffic conditions, and ambient temperature. The proposed framework consists of four different building blocks: (i) a microscopic traffic simulation model to generate speed profiles, (ii) an EV power consumption model, (iii) a battery equivalent circuit model, and (iv) a semiempirical battery degradation model. The proposed framework can also be used to assess the battery life-cycle of electric-powered automated vehicles by adjusting their travel patterns accordingly. A case study is presented using travel diary data of around 700 households from the U.S. National Household Travel Survey of 2009 to simulate household travel patterns and corresponding battery lifespan distribution.

Deadline Differentiated Dynamic EV Charging Price Menu Design

The increasing number of electric vehicles (EVs) on the road brings both opportunities and challenges to the power system. For the EV charging stations (EVCSs), it is often difficult to conduct effective operations due to the incomplete information in EVs’ departure times and the opacity of their preference information. To tackle this challenge, we seek to design the optimal deadline differentiated dynamic price menu that offers multiple choice-pairs of deadlines and charging prices. We prove that such price menus can incentivize EVs to truthfully reveal their departure time. We then analyze the properties of the optimal price menu with complete EV information, i.e., social optimality and first-degree price discrimination. For the incomplete information case, we first design a systematic method to estimate the utility and demand information for a large population of EVs based on EV behavior data. Then, we employ mixed-integer quadratic programming for the efficient optimal price menu design. The numerical study based on field data in California verifies the remarkable performance of our designed price menu.

Grid-connected photovoltaic-based microgrid as charging infrastructure for meeting electric vehicle load

Concerns about charging Infrastructure have arisen as electric vehicles (EVs) gain popularity in the transportation sector. Like gas stations, the charging infrastructure is mainly used to power the EV batteries until they are fully charged. As a result, many alternative options for creating charging stations were explored in the literature. This study proposes a grid-connected photovoltaic-based microgrid for EV charging infrastructure. It has two objectives: to design and model a grid-connected photovoltaic-based microgrid and to analyze a hypothetical EV population charging. While modeling, the realistic EV loads are considered, and the simulation is carried out. Results of this study include the power generation potential from the solar power plant, the energy mix of the microgrid (i.e., grid shared energy and onsite solar generation), and supply and load mismatch relationships. The analysis results include the weather parameter influence on power produced, followed by sensitivity analysis quantifying the impact of scaled EV sessions over microgrid power balances. Overall, it is understood that grid-connected microgrids support the seamless charging of EVs even in the case of uncertainties observed with onsite solar energy generation.

The projection of electric vehicle population growth considering scrappage and technology competition: A case study in Shanghai

Electric vehicles (EV) are expected to see a rapid growth in next decades as a substantial measure to achieve global climate goal. This course will be an unprecedented event in the industry history, and it will inevitably influence and change the power grid and the transportation network. The projection of EV population is the basis to coordinate the development of EV and urban systems. This paper proposes a modified Bass diffusion model to fulfill this task. Firstly, advantages of Bass diffusion model are analyzed, while car scrappage and technology competition with internal combust engine vehicle (ICEV) are involved to improve the model. Secondly, performance of the proposed model is evaluated with actual data during 2006–2020, and the EV stock up to 2050 is projected with the assessment of uncertainties. Thirdly, the policy on license plate quota and the ban on ICEV sales are discussed through experiment simulation. In a standard growing mode, EV is expected to dominant the new sales market by 2030 in Shanghai, and to completely replace ICEV before 2050. This research can provide a reference for urban power-transportation integrated system planning, renewable energy consumption, and carbon reduction path design. • The Bass diffusion model is modified with car scrappage and technology competition. • Effects of the license plate quota and the fuel vehicle sales ban are discussed. • Data of a pilot city for electric vehicle applications are collected for modeling. • The EV population growth up to 2050 is described with discussion of uncertainties.

Distributed privacy-preserving electric vehicle charging control based on secret sharing

Cooperative electric vehicle (EV) charging control has emerged as a key component in grid-edge resource (GER) management. However, customers’ privacy remains a major barrier to large-scale implementation of EV charging control. In this paper, we develop a distributed privacy-preserving EV charging control protocol based on secret sharing (SS) that (1) achieves scalability over EV population size; (2) enjoys higher computation efficiency compared to homomorphic encryption (HE) based methods; and (3) secures the privacy of the participating EVs against honest-but-curious adversaries and external eavesdroppers. The cooperative EV charging control is formulated to achieve overnight valley filling and framed into the projected gradient algorithm (PGA) structure as a distributed optimization problem. SS is integrated into PGA to achieve secure updates of both primal and dual variables. Theoretical security analyses and simulations in a residential area are conducted to prove the privacy preservation guarantee as well as the efficacy and efficiency of the proposed privacy preservation method. Broadly, the proposed method can be readily extended to various GER control applications.

Estimating Potential Employment Impact of the Charging Infrastructure used to Support Transportation Electrification in the United States

Increased concern over greenhouse gas (GHG) emissions and climate change is encouraging many states, companies, and consumers to focus on zero emission vehicle technologies such as electric vehicles (EVs). A major barrier to widespread EV adoption, however, is range anxiety as there is currently insufficient electric vehicle supply equipment (EVSE) available. Although not the primary goal primary of EVSE installations, one of their side effects and a goal of President Biden’s infrastructure plans is their impact on employment, both initially as stations are developed and activated and over time and as they continue to provide charging to a growing population of EVs. This study estimates the potential employment effects of the deployment and operation of President Biden’s goal of installing 500,000 charger plugs. To do this, we develop an input-output (IO) based model called JOBS EV. Unlike existing analyses, JOBS EV includes both the employment effects caused by the front-of-meter EVSE equipment needed at a particular site and the back-of-meter or upstream equipment used to obtain power from the existing utility’s distribution network. The model results indicate that approximately 1.1 million new jobs will be created over a 10-year period. The modeling process outlined in this paper, in addition to the results presented, may be useful to stakeholders involved in transportation decarbonization efforts as another means of evaluating the costs and benefits of pursuing electrification. Further work is needed to improve the underlying IO model to better account for nascent industries, to accurately calculate local share percentages, and to capture the employment effects of the complete EVSE life cycle.

Autonomous vehicle-to-grid design for provision of frequency control ancillary service and distribution voltage regulation

We develop a system-level design for the provision of Ancillary Service (AS) for control of electric power grids by in-vehicle batteries, suitably applied to Electric Vehicles (EVs) operated in a sharing service. An architecture for cooperation between transportation and energy management systems is introduced that enables us to design an autonomous Vehicle-to-Grid (V2G) for the provision of multi-objective AS: primary frequency control in a transmission grid and voltage amplitude regulation in a distribution grid connected to EVs. The design is based on the ordinary differential equation model of distribution voltage, which has been recently introduced as a new physics-based model, and is utilized in this paper for assessing and regulating the impact of spatiotemporal charging/charging of a large population of EVs to a distribution grid. Effectiveness of the autonomous V2G design is evaluated with numerical simulations of realistic models for transmission and distribution grids with synthetic operation data on EVs in a sharing service. In addition, we present a hardware-in-the-loop test for evaluating its feasibility in a situation where inevitable latency is involved due to power, control, and communication equipments.

Electric Vehicle Deployment and Integration in the Saudi Electric Power System

The demand for electricity in Saudi Arabia has grown in the last few years due to the growth in the economy and the population. The country has invested in many solutions such as promoting renewable energy and shifting to generation mix to respond to this growing demand. However, Electric Vehicles (EVs) are used as an important factor in achieving the Saudi Vision 2030 in its environmental and economical parts. This work gives an overview on the Saudi electrical energy system and then investigates the impact EVs technology in the electricity sector in Saudi Arabia and its relevant consequences. A statistical analysis is used to quantify the number of EVs, travelled distance and traffic congestions, and State of Charge (SOC). The data were used to implement a daily load profile for EVs for a large population of vehicles. The obtained results show that the EVs peak loads occur during the late evening and early morning at different means. Interestingly, the work shows that the peak periods of EVs occur during the off-peak times of the daily load curve. This means that a large population of EVs can offer more flexibility and improvement to the electric grid, and the summative EV load of a large population of vehicles has a smooth pattern and will not affect the national electric system.

The Optimal Placement of Electric Vehicle Fast Charging Stations in the Electrical Distribution System with Randomly Placed Solar Power Distributed Generations

The growing use of electric vehicles (EVs) in today’s transport sector isgradually reducing the use of petroleum-based vehicles. However, as EVpenetration grows, the EV’s demand influences distribution network parameterssuch as power loss, voltage profile. Therefore, an improved bald eaglesearch (IBES) algorithm is suggested for the optimal placement of FCSsinto the distribution network with high penetration of randomly distributedsolar power generation (SPDG). This study suggests a two-stage approachfor placing FCSs. The charging station investor decision index (CSIDI) was introduced in the first stage, taking into account the land cost index (LCI)and the electric vehicle population index (EVPI). The CSIDI was developedto decrease land costs while increasing EV population for FCS installation.In the next one, an optimization problem is constructed to minimize totalactive power loss while taking distribution system operator (DSO) constraintsinto consideration. The IEEE-34 bus distribution system is used as theproposed network. The simulation is carried out in MATLAB to integratethe EVCSs in three cases in the distribution network with SPDGs randomlyplaced. Therefore, The IBES found the best optimal positions with a powerloss of 198.43 kW. When compared to the PSO technique, the IBES techniquehas a reduced average power loss of 2.02%.

BATTPOWER application: Large-scale integration of EVs in an active distribution grid – A Norwegian case study

With the considerable increase of Distributed Energy Resources (DER), reliable and cost-effective operation of distribution grids becomes challenging. The efficient operation relies on computationally dependable and tractable optimisation solvers, which may handle: 1) non-linear AC power flow constraints, and 2) time-linking variables and constraints and objectives of DER, over the operational horizon. In this paper, we introduce an application of a high-performance MultiPeriod AC Optimal Power Flow (MPOPF) solver, called “BATTPOWER”, to simulate active distribution grids for a near-future scenario. A large-scale Norwegian distribution grid along with a large population of Electric Vehicles (EV) are here taken as the case-study. We suggest and analyse three operational strategies (in terms of control of charge scheduling fleet of EV) for the Distribution System Operator (DSO): (a) uncoordinated charge scheduling, (b) coordinated charge scheduling with the objective of energy cost-minimisation without operational constraints of the grid, and (c) coordinated charge scheduling with the objective of energy cost-minimisation along with the operational constraints of the grid. The results demonstrate that the uncoordinated charging would lead to: 1) overloading of lines and transformers when the share of EVs is above 20%, and 2) higher operational costs than the proposed control strategies of (b) and (c). In strategy (b) operational line/transformer limits are violated when the populations of EVs are growing above 36%. This implies that current market design must be altered to allow active control of a large proportion of DERs within grid operational limits to achieve cost minimization at system level. To our knowledge, the work presented in this paper is the first ever attempt to do a comprehensive analysis of the impact of EV charging demand on a real Norwegian distribution grid. Moreover, the inference of the analysis says that the Norwegian distribution networks are more prone to congestion problems than the voltage problems for the EV demand which includes a smart charging scheme accounting for grid conditions.

Expanding electric-vehicle adoption beyond the national border: Insights for developing marketing policies for global electric-vehicle manufacturers

The widespread adoption of electric vehicles (EVs) is one of the keys to achieving carbon-neutral by mid-century. This research examined the Americans’ perception of Chinese-brand electric vehicles (CBEVs) made in the US via structural equation modeling. The authors contracted a professional web survey company, which surveyed 400 Californian respondents in December 2020. We focused on California because the state has the greatest EV registration and accounts for approximately 260,000 or 47 percent of the US’s entire EV population. There are two unique approaches to this research, (1) proposing a mediation model and (2) examining country-of-origin effects in evaluating the factors influencing EV purchase intention in California households. The study results revealed that eco-consciousness does not directly invoke consumers’ purchase intention. Instead, the influence of eco-consciousness on purchasing behavior is mediated by quality judgment and the product's perceived value. The study also found that consumers’ animosity affects purchase intention indirectly by eroding consumers’ perceived value of CBEVs. Even though many foreign EV makers are attempting to increase their market presence and product adoption in the US market, their market share is still insignificant compared to conventional vehicles. Understanding factors that increase consumer acceptability of EVs is inevitable for building a successful market penetration.