Battery Electric Vehicles Users Research Articles

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.

User satisfaction with battery electric vehicles in South Korea

Battery electric vehicles (BEVs) have been promoted by the government over the last several years, driven by public concern over pollutant emissions from internal combustion engines. However, the conditions related to driving BEVs are not yet satisfactory for many BEV users, as evident from sluggish market growth compared with general market forecasts. Thus, a fundamental aspect of diagnosing the current conditions of BEV operation is to evaluate BEV user satisfaction. This study establishes hypothetical links between potential factors and BEV user satisfaction, and between BEV use satisfaction and intention to repurchase and recommend. The hypothetical links are specified using a partial least squares structural equation model (PLS-SEM) and estimated based on a survey of actual BEV owners (N=160) who had driven BEVs for at least six months. The outcomes of PLS-SEM suggest that seven relations out of nine hypothetical links were statistically significant. In particular, it is noticeable that the intention for cost-saving during operation is a key factor for BEV user satisfaction and that user satisfaction with range and charging has a positive effect on the overall satisfaction of BEV users. Furthermore, those who are satisfied with BEVs have the intention to repurchase and recommend BEVs to others. Because this study was conducted based on actual experience of BEV users, the findings could enhance understanding of the BEV driving environment and, thus, pave the way to provision of better service for BEV users.

Two-stage stochastic sizing and packetized energy scheduling of BEV charging stations with quality of service constraints

The expected deployment of battery electric vehicles (BEVs) strongly depends on the development of an adequate charging station (CS) infrastructure that guarantees a certain level of quality of service (QoS) to the BEV users. This paper proposes a two-stage method to select the number and type of CSs in parking areas (PAs) and schedule the charging sessions of the incoming BEVs ensuring a predetermined QoS level while minimizing the cost for the CS manager. In particular, stage one solves the CS sizing problem while stage two involves a probabilistic simulation procedure able to solve the charging scheduling problem by using a packetized energy approach. We also take into account the typical charging current and voltage characteristic of a BEV battery pack, and the real statistical distribution of BEV arriving times and expected parking times. A case study based on the PA at the University of Salerno Campus is used to demonstrate the effectiveness of the proposed method.

Under Pressure—Users’ Perception of Range Stress in the Context of Charging and Traditional Refueling

Although electric drives can locally reduce the environmental impact of traffic, the penetration rates of battery electric vehicles (BEV) are far below expectations, not least because the charging infrastructure network is still considered insufficient by potential users. Therefore, the planning of charging infrastructure that considers both needs and user requirements is essential to remove an important barrier to widespread adaptation of e-vehicles, but it is also a challenge. A better understanding of the charging behavior and the underlying usage motivation is therefore needed. A frequently mentioned factor is the so-called range stress. While there are many studies on this subject with new BEV users, there is a lack of approaches that also include experienced e-vehicle users and at the same time allow a comparison with drivers of cars with internal combustion engines (ICE). In this paper, this is realized with the help of a questionnaire study ( n = 204 ). The results show that ICE and BEV users at different experience levels hardly differ regarding the perceived range stress; BEV users even perceive less stress. BEV users also showed more trust in the vehicle and in the tank/battery indicators, while this trust depends only marginally on the type of information provided by the car. Furthermore, there is a correlation between users’ technology commitment and risk-taking, on the one hand, and range stress, on the other. However, for the prediction of range stress, gender, experience with e-cars, and the question of whether cars are privately owned, or car-sharing is used, are more relevant.

순서형로짓모형을 이용한 전기차 이용 만족도 및 요인 실증분석: 제주지역 전기차 민간보급 대상자를 중심으로

본 연구는 전기차(Battery Electric Vehicle) 이용 만족도와 요인을 분석하였다. 2013년부터 2015년까지 전기차를 보급받고 실생활에서 전기차를 이용하고 있는 제주지역 전기차 구매자를 대상으로 수행된 설문조사 자료를 활용하여 전기차 이용 만족도를 살펴보고 요인 분석을 위해 순서형 로짓 모형을 구축하였다. 전기차 이용자의 18%가 매우 많이 만족, 32%가 많이 만족하는 것으로 나타났으며, 27%가 만족하는 것으로 나타났다. 모형추정 결과, 겨울철 1회 충전 주행거리가 상대적으로 긴 전기차 소유자, 거주지에 이용 가능한 충전인프라가 구축되어 있는 전기차 소유자, 전기차 정책에 대한 이해 수준이 높은 전기차 소유자일수록 전기차 이용 만족도가 높은 것으로 나타났다. 그러나 전기차 충전 빈도가 증가하면 할수록, 전기차 충전기 고장 경험이 많으면 많을수록, 전기차 이용 불편을 다양하게 경험하면 할수록 전기차 이용 만족도가 낮은 것으로 분석되었다. 거주지 중심 충전기 구축 중요성과 전기차 정책 홍보 및 전기차 이용편의 정책 추진의 필요성이 시사점으로 도출되었다.