Acceptance Of Electric Vehicles Research Articles

Retrofitting Battery Electric Machinery with Unchanged Hydraulic System and Enhanced Control Strategies

The push for environmental sustainability has accelerated the acceptance of electric vehicles, as well as the exploration of electrified Non-Road Mobile Machinery. This study emphasizes the challenges of electrifying off-highway machinery, which include the many machinery layouts and the presence of Small- and Medium-sized Enterprises in the market. Recognizing the barriers faced by these companies, this paper shows how modeling and simulation can be effective tools for system integration and control optimization, even when lacking extensive expertise in the topic. However, it emphasizes the need for user-friendly modeling tools and methods adaptable to the operational needs of Small- and Medium-sized Enterprises. This study presents a case study of a retrofitted battery-electric hydraulic material handler. The machinery is simulated using Simscape, and the accuracy of the model is confirmed through experimental validation. By simulating a rational duty cycle, this study proposes two solutions for performance enhancement while maintaining the integrity of the hydraulic system. These solutions offer a balanced compromise between energy consumption and productivity and a novel control algorithm to minimize energy consumption. Most importantly, the two proposed solutions can be easily switched by the operator, which can decide to favor productivity over energy saving based on driving needs.

The effect of elastic and plastic stresses on the electrical resistivity of conductive materials

The mobility sector has been responsible for large emissions of harmful greenhouse gases worldwide for years. E-mobility can contribute to a reduction of these emissions. For a faster spread and acceptance of electric vehicles, the achievable range and thus the efficiency of the individual components in the drive and charging train is essential. In addition to the battery and the electric machine, losses in the cables and bus bars are playing an increasingly important role. The cross-sectional shape, material and manufacturing technology of these conductive materials depends on the voltage and power to be transmitted and may still change in the future, especially in the field of fast charging and increased voltage.The aim of this work is to investigate the influence of the mechanical stress condition of copper, brass and aluminium materials on their electrical conductivity. The conductivity is measured by means of 4-wire measurements under defined tensile and compressive loads. The effects of elastic and plastic strains are taken into account. The findings can be used for an optimized manufacturing strategy for conductive components for future applications.

Deciphering Determinants for Adoption of E-Mobility

lectric vehicles represent a paradigm shift in transportation, offering a clean, quiet, and environmentally friendly mode of travel. India, as a significant market for electric vehicles, aims to become a predominantly electric vehicle-driven nation by 2030, marking a remarkable endeavor toward sustainability. The global automotive landscape reflects a growing trend towards electric mobility, with nearly every major manufacturer incorporating electric vehicles into their product lineup. This widespread acceptance underscores the increasing demand for eco-friendly transportation options worldwide. Governments worldwide have implemented various policies to promote electric mobility, with encouraging results. However, achieving India's ambitious goal requires a comprehensive policy framework aimed at phasing out fuel run vehicles and incentivizing the adoption of electric vehicles. To expedite the transition, policies should discourage the further proliferation of traditional gas vehicles while introducing initiatives to facilitate the widespread acceptance of electric vehicles. This includes the implementation of schemes to make electric vehicles more accessible and appealing to consumers. While the journey towards widespread adoption of electric vehicles is ambitious and challenging, with concerted policy efforts, it is indeed achievable. The implementation of effective policies and incentives is crucial in realizing India's vision of becoming an electric vehicle-centric nation by 2030. This paper aims to provide a comprehensive review of the factors influencing consumer acceptance of electric vehicles. By synthesizing existing literature and empirical research, the study seeks to shed light on the socio-economic, psychological, and technological determinants that shape consumer attitudes and behaviors towards EV adoption. Key words: E-vehicle, e-mobility, Consumer Attitude, Psychological Factors, Charging infrastructure.

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.

Synergizing Green Transitions: Exploring EV Usage Risks in South India through the UTAUT2 Model

Climate change can be combated, and sustainable transportation can be promoted by electric vehicles (EVs). Despite their versatility, they face multiple challenges when it comes to their uptake and habitual use. A comprehensive framework to clarify these factors, specifically in relation to EVs, is available through the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Due to perceived risks, many users are hesitant to use electric vehicles despite their potential benefits. to develop and empirically test a model that forecasts the elements that affect consumers' acceptance of electric vehicles, the research aims to construct and empirically validate a model. The risk factor plays a significant role in behavioral intentions and usage of EVs in the South Indian context, as assessed through the reworked United Theory of Acceptance and Use of Technology model (UTAUT). 430 electric vehicle (EV) owners living in South India were surveyed and survey data was analyzed using a variance-based structural equation model (SEM). The newly suggested model explained 67.52% of the variance in terms of behavioral intention, and 43.58% of the variance in terms of actual usage. EV behavior and actual use were primarily influenced by risk factors, with mixed relationships between them, according to the study's empirical findings.

Who will buy electric vehicles? Segmenting the young Indian buyers using cluster analysis

Market segmentation is an essential tool that can be used for the effective and extensive adoption of emerging innovations such as electric vehicles. This study uses a multi-item questionnaire to collect data from young educated Indian students, which is used to understand the factors that affect the adoption of electric vehicles among potential vehicle buyers. Confirmatory factor analysis revealed ten latent variables that impact the adoption of electric vehicles, out of which eight variables, viz. social image, social influence, anxiety (or perceived risks), perceived environmental benefits, performance expectancy, effort expectancy, facilitating conditions, and attitude, are used for segmenting and profiling potential vehicle buyers into clusters, and the other two latent factors viz. environmental enthusiasm and technological enthusiasm are used to measure and analyse the differences in personality traits across the different clusters. Segmentation technique, based on k-means clustering, revealed three clusters in the respondents which are found to show positive, neutral (slightly positive), and negative perceptions towards all clustering variables and are subsequently labelled as “innovation adoption leads”, “innovation adoption indifferents”, and “innovation adoption idlers” respectively. The findings from this research provide better knowledge and understanding to decision-makers and can help accelerate the widespread acceptance of electric vehicles.

Determining the Purchase Reasons Behind the Acceptance of Electric Vehicles in the Indian Automobile Market

Determining the Purchase Reasons Behind the Acceptance of Electric Vehicles in the Indian Automobile Market

Optimizing wireless charging infrastructure placement using genetic algorithms

Electric vehicles (EVs) play a crucial role in tackling environmental issues in the transportation industry. The incorporation of effective charging infrastructure is crucial in promoting the broad acceptance of electric vehicles (EVs). This work investigates the optimization of the location of wireless charging infrastructure in urban contexts using genetic algorithms (GAs). The location data, which includes latitude and longitude coordinates, showed a wide range of spatial distributions that are ideal for deploying charging stations. These distributions display variances that are favorable for strategically placing the infrastructure. The examination of power consumption data revealed significant variations in energy demand across different sites, ranging from 180 kWh to 300 kWh. These differences indicate that each location has its own distinct energy needs. The population density statistics exhibited a spectrum of values, ranging from 600 individuals per square unit. The population density is 1200 persons per square kilometer. The abbreviation "km" refers to kilometers, which is used to indicate different levels of prospective electric vehicle (EV) users. In addition, the distance data provided information about the lengths between prospective locations for charging stations, which varied from 400 km to 1200 km. These distances had an impact on the concerns of connection and transmission efficiency. The research highlights the intricate nature of the elements that affect the ideal location of infrastructure, underlining the need for a methodical approach to optimization. Integrating these statistics provides a foundation for developing an objective function in the GA framework to optimize the location of charging infrastructure. The study's results provide valuable understanding of the many factors that influence the location of charging infrastructure. The goal is to promote the development of efficient and easily accessible electric vehicle charging networks in metropolitan areas.

Prediction of Electric Vehicle Mileage According to Optimal Energy Consumption Criterion

In the field of electric vehicle usage, an inherent challenge lies in the restricted mileage capacity prior to requiring a recharge, hindering broader acceptance of electric vehicles. To alleviate this concern, enhancing the comprehension of vehicle energy consumption and range plays a pivotal role in easing the anxieties of electric vehicle drivers. Within this context, a novel model-based predictive approach is introduced for estimating electric vehicle energy consumption. This method considers the vehicle's specific parameters, the road network's topology, and actual traffic conditions. Through the macro model of electric vehicle energy consumption, real-time summary data can be extracted using conventional map-based web services. By representing the road network as a weighted directed graph tailored to the energy consumption model, an algorithm aids in mileage optimization by determining the optimal path for immediate use. The resultant motion range from this approach offers improved precision and dependability in contrast to conventional strategies based on average consumption and distance.

Demystifying the barriers for electric vehicle acceptance: Multiple stakeholders' perspective

Increasing awareness about global warming, and the unsteadiness of oil prices has increased interest in sustainable transportation among consumers as well as researchers. Electric vehicles (EVs) are considered as one of the sustainable road transportations for coping with these challenges. This paper presents a study of the economical, technical, social & attitudinal, infrastructural, and policy and regulatory barriers to the adoption of EVs within an Indian context. Additionally, the study also considered different stakeholders' perspectives on adopting EVs. Various stakeholders viz. consumers/public, businesses, governments, and the scientific community will boost the supply and demand of EVs. All stakeholders' coordinated efforts are creating the groundwork for a stronger EV environment, which will result in increasing acceptability. This study aims to identify the significant barriers to EV adoption. Based on an extensive literature review first, we identified the various barriers, and after that, we prioritized the identified barriers through the Fuzzy Analytical Hierarchy Process (FAHP). FAHP is employed to study the relevance and prioritize the barriers with the help of expert opinions from different stakeholders. The results strongly suggest that a technical barrier is the most critical one among all the stakeholders. The ranking of EV barriers provides a decision-making framework for managers and experts to pay more attention to the critical barriers/sub-barriers in order to eradicate them from the system for the successful adoption of EVs. It recognizes the value of a robust theoretical contribution to the area and intends to investigate theories or models that might offer a more solid foundation for the study in order to support the theoretical framework of this research. The study provides a stronger rationale for the research's importance and potential impact by introducing a comprehensive theoretical framework.

Examining the Determinants of Electric Vehicle Acceptance in Jordan: A PLS-SEM Approach

Recently, technologies for electric mobility have developed rapidly. Since the introduction and spread of Electric Vehicles (EVs), several studies have attempted to investigate the benefits and risks that impact on the growth of the EV market by evaluating data gathered from various drivers. However, some variables were disregarded such as: Public Involvement, Knowledge of EVs, Perceived Risk, Behavioural Intention, and EV acceptance. These variables are considered vital when analysing the intention to use EVs. Therefore, this study compiles the above mentioned variables to evaluate their effect on the intention to use EVs in Jordan. 501 collected responses were examined using the Smart PLS-Structural Equation Model algorithm. In general, the analysis revealed high levels of EV acceptance. The study proposed twelve direct relationship hypotheses. Out of these hypotheses, ten hypotheses were supported and two were rejected. The final conclusions are that an increase in public involvement is associated with an increase in knowledge of EVs, and an increase in their perceived risk. Moreover, the knowledge of EVs has positively and significantly influenced the perceived usefulness and perceived ease of use, along with EV acceptance. However, no relationships were found between the following: 1. the knowledge of EVs and perceived risk; and 2. perceived risk and behavioural intention.

EMC of Inductive Automotive Charging Systems According to Standard SAE J2954

To increase the acceptance of electric vehicles (EVs), inductive charging technology can be an important tool because of the simplified charging process for the user. This paper presents the fundamentals of wireless power transfer (WPT) for EVs, while focusing on electromagnetic compatibility (EMC). This work deals with the investigation of the conducted and field-bound interference emissions using a WPT system with a max. input power of 3.6 kW. During the research, a new frequency-tracking algorithm is developed, to find the optimal operating frequency at any coil misalignment. The impedance behavior as well as the possible interference paths are investigated, showing the great geometric influence of the test bench setup. The conducted interference currents are analyzed and subsequently filtered. The filter shows good performance in attenuating common mode currents. The measured radiated magnetic field is directly rated against the proposed limits of various standards. Finally, the EMC influence of the direct current (DC) power supply line to the inverter is examined, which is not defined precisely in the standard. This underlines the significance of a standardized test setup, since the limit values can be met under different geometric circumstances of the DC cable.

Distribution Transformer Remaining Useful Life Estimation Considering Electric Vehicle Penetration

A major portion of a power system's asset portfolio comprises distribution transformers on residential premises. The rapid and massive acceptance of electric vehicles is posing challenges for distribution transformers to operate over their expected lifespan. This work proposes a four-layer framework to assess the real-time and anticipated aging of a distribution transformer and estimate the remaining useful life of a distribution transformer. The first layer stores residential smart meter data to be utilized for the kVA load estimation of a distribution transformer in the second layer. The performance of two powerful forecasting tools, i.e., Time Series Decomposition and Hidden Markov Model, is compared in the third layer. The historical and forecast data, along with the distribution transformer's thermal parameters, are used for its remaining useful life assessment. Numerical validation is conducted on real-world data utilizing electricity consumption and ambient temperature of fifteen households in London, Ontario, Canada. This work also includes the penetration of the most popular electric vehicles in Canada, along with service drop cable data and practical secondary distribution circuit configuration.

PERBANDINGAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE DAN NAÏVE BAYES PADA ANALISIS SENTIMEN KENDARAAN LISTRIK

Electric vehicles are one of the solutions that can be used to deal with the problem of greenhouse gas emissions. Switching to electric vehicles can be an effective solution because electric vehicles have many advantages. However, the acceptance of electric vehicles in Indonesia depends on the opinions or sentiments given by the community. Sentiment analysis can provide a specific picture of how the sentiment or opinion given by the Indonesian people towards electric vehicles. The sentiment analysis process is carried out using the Python programming language. This research compares SVM and Naïve Bayes methods in sentiment analysis in terms of accuracy and time efficiency. A total of 717 data were used as test data and SVM correctly classified 150 negative data, 152 neutral data, and 277 positive data. Meanwhile, Naïve Bayes correctly classified 166 negative data, 143 neutral data, and 282 positive data. training time required for the SVM method is 37.42 seconds while Naïve Bayes is 0.10 seconds. Naïve Bayes is the best method in this study because of its high accuracy and fast training time.

Reliability oriented techno- economic assessment of fast charging stations with photovoltaic and battery systems in paired distribution & urban network

Installing fast charging electric vehicle stations (FCEVS) is crucial for increasing public acceptance of electric vehicle (EV) adoption. The enormous energy demands of FCEVS, as well as the inclusion of renewable energy resources (RES) into utility grids, may have a significant influence on system reliability and pose challenges owing to uncertainty. Therefore, a new reliability-oriented analysis-based two-stage planning framework for optimal deployment of solar photovoltaic (SPV) powered FCEVS and battery energy storage systems (BESS) in coupled transportation and distribution networks is proposed in this article, while encapsulating the complex interactions between EVs, SPVs, and BESS. A novel composite metric, the average voltage deviation reliability index (AVDRI), has been developed for the deployment of FCEVS infrastructure considering the goals and limitations of both distribution and transportation networks. Models such as the demand for charging electric vehicles, the reliability evaluation system, and the economic analysis model are offered to provide essential inputs for the study of distribution network reliability and profitability. In the first stage, AVDRI, active power loss, captured EV flow, and capital, along with operating and maintenance costs of the FEVCS and SPV system, are all taken into consideration as objectives when optimizing the placement and sizing of SPV assisted FCEVS using Multi Objective Particle Swarm Optimization (MOPSO) and a fuzzy satisfaction-based hybrid technique. Then, the optimal resource sizing, such as the count of charging spaces, the capacity of the SPV system at each optimal FCEVS, and captured EV flow, are estimated. In the second step, a bi-section approach is used to compute the capacity of the BESS and the extra SPV rating necessary to power it at each optimal FCEVS location, taking into account the irradiance profile for all conceivable seasons within a planning year. A 25-node transportation network coupled to an IEEE 33 bus distribution system is used to validate the proposed methodology under a range of seasonal scenarios.

Integrating Vehicle-to-Home Unit with Smart Home Energy Management Systems: A Unified Energy Management Framework

The increasing acceptance of electric vehicles (EVs) and renewable energy sources (RES) has led to the need for a unified energy management framework that can integrate multiple energy systems, including Vehicle-to-Home (V2H) units, Smart Home Energy Management Systems (SHEMS), Solar PV systems, and the grid. The framework uses a centralized control strategy that makes decisions based on real-time integration of a Vehicle-to-Home (V2H) unit with a Smart Home Energy Management System (SHEMS), which can provide a solution to these challenges by enabling bidirectional power flow between the EV battery and the home. We proposed a unified energy management framework that integrates a Ve- hicle-to-Home (V2H) unit with a SHEMS using a Perturb and Observe (P-&-O) algorithm. The proposed framework allows for the optimization of energy usage in the home by utilizing the battery of an EV, which can be charged during off-peak hours and discharged during peak hours. The P&O algorithm is a well-known algorithm used in photovoltaic systems to track the Maximum Power Point (MPP) of a solar panel. The algorithm is also applicable to EV charging and discharging systems. The algorithm uses a per- turbation signal to change the charging rate of the EV battery and observes the resulting change in the bat- tery voltage. The simulation results can also provide benefits to EV owners, such as reduced electricity costs and increased flexibility in managing their EV charging, helping to achieve efficient and sustainable energy management in residential areas. Keywords: Solar panel, Electric Vehicle Battery, Vehicle-to-Home (V2H), Smart Home Energy Manage- ment Systems (SHEMS), Demand side management.

In operando detection of Lithium plating via electrochemical impedance spectroscopy for automotive batteries

Reducing the charging time is one of the main goals towards increasing the acceptance of electric vehicles. One of the limits of fast charging lithium-ion batteries with graphite-based anodes is lithium plating, a process which occurs mainly at high charging rates, low temperatures and high state of charge. Detecting the onset of lithium plating during a charging procedure is essential in order to optimize fast-charging profiles. This paper introduces a new method that allows the detection of the onset of lithium plating in automotive lithium-ion batteries using on-line electrochemical impedance spectroscopy. Compared to similar approaches, the measurement itself does not result in a significant increase in the charging time. The detection is based on observing the real part of the impedance across the state of charge during the charging procedure. The onset of lithium plating can be detected by a deviation from the nominal behavior in the form of a drop towards lower impedance. The behavior is both explained theoretically using electrochemical equivalent circuits and shown experimentally. The impedance measurements are carried out using an integrated small-scale device to allow on-line impedance analysis in an automotive environment and an integration into future battery management systems.

Heterogeneous Factors Influencing Electric Vehicle Acceptance: Application of Structural Equation Modeling

Since electric vehicle (ELV) deployment can contribute to overall renewable energy sources, exploration of the heterogeneous influence factors (HIFs) affecting the willingness to accept ELVs can assist in the realization of sustainable development goals, particularly universal access to affordable energy for all. In this research, we explored the HIFs that influence the willingness of individuals to accept ELVs (WAELV) within an integrated decision-making (IDM) framework. We established the IDM conceptual framework through the incorporation of HIFs, notably including the environmental and health benefits of ELVs, knowledge about innovation, and the benefits regarding the built environment and creating a comprehensive structure. We analyzed data gathered through questionnaires from urban and peri-urban areas of the Shandong province (China) by employing the partial least square structural equation modeling technique, which is an appropriate tool for analyzing data measured on a Likert scale. The key findings were as follows. Firstly, the capital cost of ELVs was found to be a significant barrier to the WAELV of individuals. Secondly, among other factors, the societal aspect of ELVs and the environmental awareness aspect were drivers of the WAELV of individuals across all the data samples. However, benefits for the built environment, knowledge about innovation, and the environmental and health benefits of ELVs only positively drove the WAELV of individuals in the urban setting and for the overall sample. Thirdly, these three HIFs were identified as neutral factors in the peri-urban areas. Thus, a clear disparity was detected between the urban and peri-urban areas in terms of factors influencing the WAELV of individuals. Finally, the social aspect of ELVs was revealed as the strongest driver, while benefits for the built environment turned out to be the weakest factor. Based on these findings, some crucial policies are here extracted.

Factors affecting electric vehicle acceptance, energy demand and CO2 emissions in Pakistan

This work aims to investigate the factors accelerating electric vehicle (EV) acceptance at the consumer end in Pakistan and analyzes the implications for policymakers for a fast-track EV transition. The study further investigates the high EV penetration scenario resulting from the technology acceptance model (TAM's 80% EV) and its impact on energy demand and CO2 emissions. The study design used a quantitative analysis method with the survey as an instrument for data collection regarding EV acceptance. The model under investigation was adapted from the famous Technology-Acceptance Models (TAMs) and modified with other significant predictors evidenced in the literature. Correlation and stepwise regression were performed with a multicollinearity check for model hypothesis testing. Out of six predictors, only four factors were significant in accelerating the EV transition. Financial policies were found to be highly significant, followed by environmental concern, facilitating conditions and perceived ease of use. The research then used exponential smoothing forecasts for transport demand and developed an EV penetration scenario based on modified TAM results. The results highlight the significant increase in transport demand and the opportunity for Pakistan to limit passenger transport emissions to 36.6 ​MT instead of 61.6 ​MT by 2040.

Analysis of Consumer Behavior towards Electric Vehicles: Intentions, Concerns, and Policies

Despite the acceptance of electric vehicles (EVs) by consumers in developed countries, consumers' intentions towards these smart devices (SD) and the steps that can be taken to expand in this market continue to be investigated in developing countries such as Turkey. In this study, policies and incentives for the purchase of Electric Vehicles in different countries were examined, consumer concerns before the adoption of SDs were evaluated, and then consumer intentions in adopting EVs with models such as reasoned action theory, planned behavior theory, and technology acceptance model were evaluated with bibliometric analysis through conducted studies. Data from 63 publications accessed from Scopus, Web of Science, and DergiPark databases were used in the field mapping process. The results provide insights into increasing the market share of electric vehicles, which are critical in reducing the carbon footprint, by recommending the issues that need to be highlighted to the industry and researchers.