Battery Electric Vehicles Users Research Articles

Promoting Sustainable Transportation: Factors Influencing Battery Electric Vehicle Adoption Across Age Groups in Thailand

Battery Electric Vehicles (BEVs) are a crucial innovation for achieving sustainable transportation and reducing greenhouse gas emissions, which are major contributors to global warming and climate change. While previous studies have explored attitudes towards BEV technology acceptance, few have examined the interplay of external factors such as government measures and adoption barriers in promoting sustainable mobility. This study addresses this gap by investigating the roles of government policies, usage obstacles and innovation diffusion in stimulating BEV purchase intentions, while applying the Innovative Diffusion Theory (IDT). Data from 3632 respondents in Thailand were analyzed using structural equation modeling (SEM) to examine causal relationships between factors. The results indicate that government policies supporting BEV users enhance innovation diffusion in society, leading to increased adoption intentions. Furthermore, effective policies help mitigate barriers to BEV usage, further encouraging adoption. The study also reveals that causal relationships of BEV usage intentions vary across age groups, highlighting the need for targeted approaches in promoting sustainable transportation. These findings contribute to the development of evidence-based policy recommendations to accelerate BEV adoption, supporting Thailand’s Carbon Neutrality goals and broader sustainable development objectives. By elucidating the complex dynamics of BEV adoption, this research provides valuable insights for policymakers and stakeholders working towards a more sustainable and environmentally friendly transportation sector.

What do battery electric vehicle (BEV) users think of the charging infrastructures launched in expressway service areas? Evidence from China

ABSTRACT The emergence of electric vehicles (EV) presents new opportunities for transportation decarburization and sustainable transportation in cities worldwide. However, previous research has primarily focused on EV charging infrastructures within urban areas, with limited attention to those launched in expressway service areas. Addressing this gap is crucial in alleviating EV users’ range anxiety on expressway journeys. This study investigates the BEV users’ reuse behavior toward charging infrastructures on expressways. Focusing on the BEV users who had used the charging infrastructures on the expressway, the structural equation modeling and the multi-group analysis are employed to reveal the effect of psychological factors on BEV users’ reuse intention and explore the heterogeneity across different socio-demographic groups. Results reveal that Attitude and Subjective Norm drive the reuse intention. Perceived Risk has an indirect negative effect on reuse intention. Attitude has a more significant effect on reuse intention among elder users, high-frequency users, and low remaining State of Charge (SOC) users. This paper offers new insights for charging infrastructures’ planning and operation in expressway service areas.

Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation

The optimal placement of electric vehicle charging infrastructure (EVCI) is critical for meeting the charging demand of battery electric vehicles (BEVs). However, the questions of how to coordinate the stochastic charging demand and placement of charging infrastructure in an economically effective way and how to solve such a problem on a large real-world scale remain unanswered. This study addresses these questions by developing an agent-based charging station placement model (ACPM) that incorporates demand uncertainty by simulating the behaviors of BEV users. A solution algorithm based on random embedding Bayesian optimization (REMBO) is proposed and shown to significantly improve computational efficiency and solve the ACPM on a large real-world scale. The model and algorithm are applied to a case study of the Atlanta metropolitan area. In the study, the algorithm finds an optimal placement using only 2% of the runtime of existing benchmark methods. This enables the use of the algorithm on a more complex, real-world scale. Results from the case study indicate that the optimal placement of EVCI uses level-2 and direct-current fast charging (DCFC) roughly equally and distributes these relatively evenly throughout the region. This optimal EVCI placement has significant economic benefits because it can improve the expected net present value (NPV) by 50% to 100% relative to common benchmark charging placement practices, such as placing the EVCI uniform randomly or weighted by the number of parked vehicles. Finally, a sensitivity analysis demonstrates that factors such as the size of the BEV market, charging preferences, and charging price are shown to have significant impacts on the optimal placement and profitability of an EVCI project.

Tariff option preferences for smart and bidirectional charging: Evidence from battery electric vehicle users in Germany

Smart charging of battery electric vehicles (BEVs) can contribute to flexibility in power grids and help integrate renewable electricity. Tapping into this potential requires high user acceptance for smart charging and corresponding tariffs. In this paper, we analyze the preferences of current BEV users, representing the potential near-term adopters of smart charging, for different smart charging tariff design elements by conducting a discrete choice experiment with 689 participants in Germany. In doing so, we (1) provide an overview of current BEV users' preferences, (2) identify and characterize BEV user groups with substantial differences in their preferences, and (3) identify barriers for smart charging implementation from the perspective of current BEV users. More specifically, we find that potential cost savings along with the pricing scheme and charging mode are the most important tariff elements, whereof a pre-defined price corridor with an emergency price for grid bottlenecks and charging a safety buffer before applying smart charging are most preferred. We identify three user groups, with a large share of innovative adopters. Moreover, driving range or reluctance regarding data sharing can represent barriers for smart charging adoption. Based on our findings, we derive implications for decision-makers in policy and industry.

Accounting for BEV Users’ Risk Attitudes and Charging Inertia in En Route Charging Choice Behavior

This paper innovatively explores BEV (battery electric vehicle) users’ risk attitudes and charging inertia, examining their effects on en route charging and charging route choice behavior. An attitudinal survey was conducted to explore the two latent variables of risk attitudes and charging inertia in relation to socioeconomic and travel-related characteristics. ICLV (Integrated choice and latent variable) models are adopted to estimate the latent variables and the charging choice behavior simultaneously. Specifically, uncertainty in energy consumption is first considered in the ICLV model, which is represented by the available range (AR) uncertainty. Multinomial logit (MNL) models directly incorporating socioeconomic attributes are employed as a reference for comparison with ICLV models. Results illustrate that risk attitudes and charging inertia both play significant roles in modeling en route charging choice behavior. Risk-averse users and users having charging inertia value AR uncertainty more. Battery range, charging frequency, and income emerge as the most crucial factors influencing users’ intention to charge en route. The results show significant heterogeneity of BEV users in attitudes and charging choice behavior, underscoring the importance of accounting for the heterogeneity in en route charging demand estimation and deployment optimization of public charging stations, particularly for medium-to long-distance trips.

An Analysis of Electric Vehicle Charging Intentions in Japan

This study focuses on charging-related decisions for fast charging at highway service and parking areas, slow charging at home, fast charging at commercial facilities, and fast and slow charging at workplaces. This research contributes to the existing literature by estimating the charging behavior variables, as well as understanding the role of explanatory variables in influencing charging-related decisions. Responses from the stated preference (SP) survey in Japan in 2021 were analyzed with a mixed logit model (MXL). The results showed that, (1) when the battery level is 75% or higher, users of battery electric vehicles (BEVs) are not keen to charge their vehicles, but when the next trip is anticipated to be 50 or more kilometers, they choose to charge their vehicles; (2) individuals are not willing to tolerate any waiting time for their vehicles to be charged at each location; and (3) the recurrence of charging at the target location affects the charging decision of BEV users. We found significant relationships between socioeconomic characteristics and charging decisions. Furthermore, we examined the practical applications of the empirical findings in this study for policymaking and charging infrastructure planning.

Managing BEV Charge to Obtain a Positive Impact on a National Power System

This paper’s research question is to evaluate the potential impact of large numbers of battery electric vehicles (BEVs) on the future electric grid, and whether the flexibility of BEV charging can induce enough system benefits to remunerate BEV users for the change in their recharging pattern. The considered scenario refers to the Italian situation and what might occur through the year 2040, where a share of BEV stock of 40% can be foreseen, as well as significant increases in PV and wind generation. Although this study is focused on Italy, its results are applicable, with minor differences, to several EU countries. This paper first shows that the future impact of increasing penetration of BEVs appears to be compatible with the expected growth of generation from renewable energy sources (RES) and the corresponding reduction in fossil fuel-based generation. It also gives an estimate of the CO2 emission reduction resulting from these changes, considering an unmanaged BEV charge profile and two different managed profiles that shift the car’s charging period to hours of the day when they have no negative impact on the grid and maximize the utilization of RES. Finally, it shows an evaluation of the economic benefits of displacing private car charging ranging from 4 to 10 cEUR/kWh, which could be used as tariff incentives to stimulate this displacing in recharging time.

Congestion Relief Services by Vehicle-to-Grid Enabled Electric Vehicles Considering Battery Degradation

Battery electric vehicles (BEVs) offer substantial potential to enhance the electric grid through bi-directional charging technologies. In essence, BEVs, functioning as portable battery energy storage systems, play a pivotal role in enabling the seamless integration of renewable energy, grid optimization, and ancillary services. This article sets out to explore the value of BEVs equipped with Vehicle-to-Grid (V2G) for grid operators, particularly in the context of alleviating congestion. This valuable service, though not accompanied by direct monetary compensation for users, holds significant promise in minimizing congestion and renewable energy curtailment. This study utilizes the Day-Ahead Locational Marginal Price (LMP) data obtained from various locations within California Independent System Operator (CAISO) to ascertain the financial benefits to BEVs located on either side of congestion at different grid nodes, across various months. Similar analysis is performed on some of the largest solar energy plants in California. Mixed-integer linear programs are used to optimize the charging/discharging decisions for the BEV for maximizing revenue from LMP arbitrage and for minimizing the congestion component of LMP. Additionally, we take into account the impact of battery degradation, quantified as a cost per kilowatt-hour ($/kWh), and integrate this factor into our assessment to understand the evolving discharging behavior of BEVs. The article compares the benefits from the BEVs towards congestion minimization for the two different optimization scenarios, discusses seasonality, and addresses the importance of adequately compensating BEV users and incentivizing them to prioritize congestion relief during specific time intervals.

Stochastic optimal design of a rural microgrid with hybrid storage system including hydrogen and electric cars using vehicle-to-grid technology

In this paper, the optimal sizing of a rural microgrid is studied by applying two-stage stochastic programming with a scenario-based approach considering a multi-energy system and different electric vehicle technologies with grid-vehicle-grid operations. The system components are a photovoltaic panel, wind turbine, battery, hydrogen-based storage, battery electric vehicle (BEV), and fuel cell electric vehicle (FCEV). A multi-objective optimization problem with minimizing life cycle cost and maximizing the system reliability is defined for the microgrid design. To reflect reliability, loss of power supply probability (LPSP) indice is used. The defined problem is solved with mixed-integer linear programming. Hourly real-world data for a one-year period is used for simulations. The uncertainties from renewable resources, demand, BEV, and FCEV users are considered during system design. Firstly, we analysed the impact of different storage technologies on system design. We found that a hybrid storage system is the most cost-effective option for each LPSP limit and increasing LPSP from 0 to 0.2 led to a cost reduction of up to 20% in all cases. Secondly, different cases are created to assess the impact of BEVs and FCEVs in capacity configuration considering grid-vehicle-grid operations. Simulation results demonstrate that utilizing vehicle-to-grid (V2G) mode in both types of vehicles leads to a cost reduction of approximately 2% when a hybrid storage system is in place. Furthermore, it is observed that BEVs contribute significantly, providing up to 90% of the V2G power. Also, the availability of FCEV with a larger size of fuel tank capacity affects positively the cost of the designed system.

Range Anxiety among Battery Electric Vehicle Users: Both Distance and Waiting Time Matter

Range anxiety is a major concern of battery electric vehicles (BEVs) users or potential users. Previous work has explored the influential factors of distance-related range anxiety. However, time-related range anxiety has rarely been explored. The time cost when charging or waiting to charge the BEVs can negatively impact BEV users’ experience. As a preliminary attempt, this survey study investigated time-related anxiety by observing BEV users’ charging decisions in scenarios when both battery level and time cost are of concern. We collected and analyzed responses from 217 BEV users in mainland China. The results revealed that time-related anxiety exists and could affect users’ charging decisions. Further, users’ charging decisions can be a result of the trade-off between distance-related and time-related anxiety, and can be moderated by several external factors (e.g., regions and individual differences). The findings can support the optimization of charge station distribution and EV charge recommendation algorithms.

Prediction of Traffic Flow considering Electric Vehicle Market Share and Random Charging

This paper mainly studies the multiclass stochastic user equilibrium problem considering the market share of battery electric vehicles (BEVs) and random charging behavior (RCB) in a mixed transport network containing electric vehicles and gasoline vehicles (GVs). In order to analyze the random charging and path choice behaviors of BEV users and extract the differences in travel behaviors between BEV and GV users, an improved logit-based model, multilabel algorithm, and queuing theory are applied. The influencing factors of charging possibility mainly include the initial state of charge (SOC), the SOC at the beginning of charging, and the psychologically acceptable safe SOC threshold arriving at the destination. Diversity choices of user paths and charging locations will result in changes in queuing traffic and differences in queuing time. Conversely, different stations have different queuing dwell times, which will also affect the routing and charging locations for BEVs with RCB. The path-based method of successive averages (MSA) is adopted to solve the model. Through the simulation of the test network Sioux Falls, the equilibrium traffic flow and possible charging flow under different market shares and initial SOC are predicted, and the properties of the model and the feasibility of the algorithm are verified.

Road users’ attitudes towards electric vehicle incentives: Empirical evidence from Oslo in 2014–2020

This paper examines participant attitudes towards battery electric vehicle (BEV) incentives. Our case study was conducted in the greater Oslo area. Oslo has ranked as the world capital of BEV usage since 2014. The Norwegian government currently leads the comprehensive use of BEV incentives to decarbonize road transport. The data set is from a questionnaire survey conducted annually between 2014 and 2020. A total of 6363 individuals divided equally into annual random samples were asked to express their attitudes towards the different BEV incentives in place in each year. Participants were aged 18 or older and were living in the larger Oslo area. Professional data collection companies used computer-assisted telephone interviews to conduct the survey. Generalized structural equation modelling (GSEM) was used to analyse the data. The sample was 49% women and 51% men, with a mean age of 51 years, ranging from 18 to 99 years old. People in greater Oslo increasingly disagree each year with beneficial BEV incentives such as toll exemptions, access to bus lanes without passengers and free public parking. However, internal combustion engine vehicle (ICEV) users are more likely to disagree than BEV users. The results provide new knowledge about attitudes towards BEV incentives from a longitudinal perspective.

Charging Behavior Analysis Based on Operation Data of Private BEV Customers in Beijing

Charging behavior is essential to understanding the real performance and evaluating the sustainability of battery electric vehicle (BEV) development and providing the basis for optimal infrastructure deployment. However, it is very hard to obtain the rules, due to lack of the data support, etc. In this research, analyzing the charging behavior of users with private charging piles (PCPs) is carried out based on the real vehicle data of 168 BEV users in Beijing, covering 8825 charging events for a one-year duration. In this study, the charging behaviors are defined by five indexes: the starting state of charge (SOC) of batteries, charging location selection, charging start time, driving distance, and duration between two charging events. To further find the influencing rules of the PCPs owning state, we setup a method to divide the data into two categories to process further analysis and comparison. Meanwhile, in order to better observe the impact of electric vehicle charging on the power grid, we use a Monte Carlo (MC) simulation to predict the charging load of different users based on the analysis. In addition, an agent-based trip chain model (ABTCM), a multinomial logistic regression (MLR), and a machine learning algorithm (MLA) approach are proposed to analyze the charging behavior. The results show that with 40% or lower charging start SOC, the proportion of users without PCPs (weekday: 55.9%; weekend: 59.9%) is larger than users with PCPs (weekday: 45.5%; weekend: 42.6%). Meanwhile, users without PCPs have a certain decrease in the range of 60–80% charging start SOC. The median charging time duration is 51.44 h for users with PCPs and is 17.25 h for users without PCPs. The charging peak effect is evident, and the two types of users have different power consumption distributions. Due to the existence of PCPs, users have lower mileage anxiety and more diverse charging time choices. The analysis results and method can provide a basis for optimal deployment and allocation of charging infrastructure, and to make suitable incentive policies for changing the charging behavior, targeting the carbon neutral objectives.

Real-world charging behavior and preferences of electric vehicles users in Germany

As energy consumption and greenhouse gas emissions in the transport sector have increased continuously in recent years, electric vehicles have become a potential solution to achieve environmental goals in this sector. The uptake of electric vehicle is strongly dependent on sufficient charging infrastructure. While much research has been conducted on electric vehicle charging simulations and projections, there is a research gap on real-world charging by actual users. Therefore, we conducted a survey on charging behavior and preferences in Germany in 2020 with around 4,000 electric vehicle users. The survey included a stated choice experiment and a willingness-to-pay-analysis. We found that participants’ charging behavior is dominated by home charging and that public infrastructure is perceived to be insufficient. Next to charging prices, measures of comfort through the occupancy rate of charging infrastructure and additional waiting time are driving factors when making the decision to charge. Participants specified an acceptable time for additional walking distances when parking at charging facilities between 5 and 10 minutes. High abstention rates in answering some questions indicate the need for sufficient education strategies considering existing and new charging technologies. Drivers with high annual mileage and battery electric vehicle users are willing to pay more for additional charging power. Users are open to smart charging, as long as flexibility for spontaneous trips is maintained. This research offers timely insights into real-world charging behavior and preferences, while providing recommendations for the future roll out of charging infrastructure in Germany.

Understanding the generation mechanism of BEV drivers' charging demand: An exploration of the relationship between charging choice and complexity of trip chaining patterns

In the context of the rapid popularization of battery electric vehicles (BEV), it has become a key problem to deeply understand the charging demand and reasonably configure charging facilities. Exploring the relationship between trip chaining patterns and charging choice of battery electric vehicle users may aid in understanding the mechanism of charging demand. Based on the recursive simultaneous bivariate probit model, we integrate the impact of BEV users’ risk aversion attitude and develop two causal structures: one is that the charging choice is determined first and influences trip chaining pattern, the other is that trip chaining pattern is determined first and influences charging choice. Next, a stated preference survey is conducted through online platform and field survey, and a total of 494 valid questionnaires are collected. The model results show that the fitting effect of the causal structure model where trip chaining pattern precedes charging choice is better than another causal structure model. Moreover, integrating the influence of risk aversion attitude in the form of latent variable into the RSBP model can significantly improve the fit goodness of the model. These findings will help to further understand the mechanism of charging demand and have some implications on estimating charging demand.

Using rooftop photovoltaic generation to cover individual electric vehicle demand—A detailed case study

The introduction of battery electric vehicles (BEV) and the expansion of rooftop photovoltaic (PV) power generation are both progressing at a fast pace to decarbonize the transport and the energy sector in Switzerland. These parallel developments have an enormous synergy potential as the actual decarbonization impact of BEVs depends heavily on the carbon footprint of the power source and the PV expansion requires local storage as a buffer to reduce negative impacts on the distribution grid. We present an empirical analysis based on a detailed 10-month data set of the charging and mobility behavior of 78 BEV users in Switzerland. It is combined with a fine-grained digital surface model of Switzerland to extract the detailed roof geometry and the corresponding rooftop PV generation capacity of each of the BEV owner’s houses.We test four different smart charging strategies with a varying degree of complexity and find that when charging uncontrolled (the strategy used during the study), BEV owners can only cover 15 % of their BEV’s demand using PV generated from the roofs of their own houses. A simple controlled charging approach greatly increases the average coverage to 56 % and up to 90 % or 99 % when using an optimized charging strategy without or with a home battery storage. All charging strategies ensure that the individual mobility behavior of the BEV owners is not affected.We further show that using rooftop PV power generation for BEV charging has a large potential to further decrease the climate impact of BEVs and propose simple adjustments to consider in charging strategies that help to increase the owners’ PV consumption.

Battery electric vehicle adoption in Denmark and Sweden: Recent changes, related factors and policy implications

Countries worldwide are trying to increase the share of battery electric vehicles (BEVs) to reach environmental goals. As part of these efforts, the EU project GREAT installed new fast chargers in Denmark and Sweden and provided possibilities to test BEVs and inform about them. To monitor changes in attitudes and behaviours and to estimate the impact of project activities, surveys with BEV and conventional car users were conducted in 2017, 2018, and 2019. We found that attitudes and driving behaviour of BEV users and non-users remained quite stable. While car users in Denmark and Sweden showed similar profiles, one exception was a more negative evaluation of and higher uncertainty about political support for BEVs by Danes. Modelling purchase intention, we found a significant effect of the new fast chargers in Denmark but not in Sweden, while the opposite was the case for information campaigns. Lifestyle compatibility and symbolic-affective attitudes were relevant for BEV adoption in both countries. For cross-border trips, the most relevant factor was whether people had a Tesla or not, reflecting the better driving range and fast charging infrastructure. An increase of charging infrastructure, clearer policy signals and symbolic-affective marketing are discussed as ways to increase BEV adoption.

An empirical study on the relationship among perceived value, satisfaction and behavioral intentions of battery electric vehicle user in China

With the rapid development of battery electric vehicle, it has a profound impact on the driving behavior and habits of users. However, studies on the evaluation and users’ behavioral intentions of battery electric vehicle are rare. To promote the further development of battery electric vehicle, a better understanding of battery electric vehicle users is needed. This paper aims to develop a model to investigate the relationship among perceived value, user satisfaction and behavioral intentions of battery electric vehicle. Specifically, we divide perceived value into two dimensions of functional value and symbolic value. Data was collected through questionnaires from battery electric vehicle users in major cities in China. The analysis results of the structural equation model indicate that: 1. the functional value and symbolic value have a direct positive impact on battery electric vehicle user satisfaction and the symbolic value palys a greater role; 2. the functional value and symbolic value have a direct positive effect on the behavioral intentions of battery electric vehicle users; 3 battery electric vehicle user satisfaction and behavioral intentions have a high positive correlation.

Optimal positioning of dynamic wireless charging infrastructure in a road network for battery electric vehicles

Dynamic wireless charging (DWC) offers a plausible solution to extending Battery Electric Vehicle (BEV) driving range. DWC is costly to deploy and thus its locations need to be optimized. This raises a question often encountered in practice for infrastructure investment: how to determine the optimal locations of DWC facilities in a network. In this paper, we propose a sequential two-level planning approach considering the objectives of both the public infrastructure planning agency and the BEV users. Two different planners’ objectives namely, total system travel time and total system net energy consumption are considered. Besides these objectives, constraints such as agency budget, range reassurance, and equity in resource distribution are also addressed at the planner’s level. For each objective, BEV drivers respond by choosing their preferred route based on the location of DWC facilities implemented by the planner. An effective solution algorithm is utilized that has the capability of solving relatively large-scale real-world networks within a reasonable computational time. The numerical experiment and case study results provide useful insights on optimally positioning DWC infrastructure to minimize societal cost and energy.

Optimal Driving Range for Battery Electric Vehicles Based on Modeling Users’ Driving and Charging Behavior

This paper proposes a simulation approach for the optimal driving range of battery electric vehicles (BEVs) by modeling the driving and charging behavior. The driving and charging patterns of BEV users are characterized by reconstructing the daily travel chain based on the practical data collected from Shanghai, China. Meanwhile, interdependent behavioral variables for daily trips and each trip are defined in the daily trip chain. To meet the goal of the fitness of driving range, a stochastic simulation framework is established by the Monte Carlo method. Finally, with consideration of user heterogeneity, the optimal driving range under different charging scenarios is analyzed. The findings include the following. (1) The daily trip chain can be reconstructed through the behavioral variables for daily trips and each trip, and there is a correlation between the variables examined by the copula function. (2) Users with different daily travel demand have a different optimal driving range. When choosing a BEV, users are recommended to consider that the daily vehicle kilometers traveled are less than 34% of the battery driving range. (3) Increasing the charging opportunity and charging power is more beneficial to drivers who are characterized by high daily travel demand. (4) On the premise of meeting travel demand, the beneficial effects of increased fast-charging power will gradually decline.