Battery Electric Vehicles Owners Research Articles

A latent-based segmentation framework for the investigation of charging behaviour of electric vehicle users

Electrification of transport is deemed by many countries worldwide as one of the key strategies to mitigate CO2 emissions, yet the availability of reliable public charging infrastructure systems represents a potential serious bottleneck to such endeavours. Existing studies exploring battery electric vehicle (BEV) charging behaviour are typically based on either non-representative samples or stated choices experiments. This paper analyses observational data from a representative sample of German BEV owners who provided information on mileage and charging activities over a timeframe of eight weeks. BEV charging patterns, related vehicles kilometres travelled (VKT) and battery charging behaviour are assessed via a multifaceted empirical framework that pairs a hazard survival-based model with a log linear regression approach. A latent class method is also employed to segment BEV owners into different charging segments. The model suggests two types of charging behaviour exist, consisting of regular and irregular chargers. Charging frequencies and patterns are found to be radically different between the two groups under study, with regular chargers estimated to charge their vehicles 1.5 times more than irregular chargers. Lastly, the framework proposed is used to explore how charging behaviour will mutate due to both technology advancements (BEV driving range improvements) and user-centric factors (VKT variations). Neither technological or user factors are predicted to substantially affect the inter-charging duration of irregular chargers, whereas both increasing BEV driving ranges and reducing VKT results in a longer elapsed time between two consecutive charges for regular chargers.

Towards BitCO2, an individual consumption-based carbon emission reduction mechanism

Human activities, such as burning fossil fuels for electricity generation, heating, and transport, are the primary drivers of a large amount of greenhouse gases emission. The individual consumers, able to influence the supply-chains behind the commodities their chose to fulfil their needs is the driver behind production and, consequently, its impacts. Thus, the active and willing participation of citizens in combatting climate change may be pivotal to address this issue. The present work is aimed at presenting and modelling a novel market-based carbon emission reduction mechanism, called BitCO2, designed to incentivize individual consumption choices toward lower carbon footprints. This mechanism is tested for the Italian private transportation sector thanks to an ad hoc developed System Dynamics model. The Battery Electric Vehicle (BEV) adoption, if compared with the Internal Combustion Engine Vehicle (ICEV) one, cause less CO2 emissions per km travelled. After a certain number of travelled km, a BitCO2 token is assigned to BEV owners for each ton of avoided CO2. This token can be exchanged in a dedicated market and used to get a discount on insurance services. Assuming a Social Cost of Carbon of 9.22 [2.13–22.3] €/tonCO2eq, model results show that the BitCO2 mechanism would allow for a cumulated CO2 emission reduction of 973 [68.9–5’230] ktonCO2eq over 20 years of operation with a peak of 39.3 [5.34–189] thousand additional BEV registration per year.

A method for determining optimal electric range by considering electric vehicle lightweighting on perceived ownership cost

The limited driving range due to high costs and low energy densities of batteries constraints the battery electric vehicle (BEV) market growth. Lightweighting in theory can reduce energy consumption rate and extend the driving range. The knowledge gap is to quantitatively link the cost-effectiveness of light-weight technologies with range extension and consumer acceptance of BEVs. In this study, a physics-based energy consumption model of BEVs is constructed and associated with a statistics-based model on the basis of travel surveys. A perceived cost of ownership (PCO) is then developed by adding intangible costs to traditional total cost of ownership models. We estimate, at the disaggregate vehicle model and driver level and the aggregate market level, 1) the extended range due to lightweighting for a given battery size; and 2) the optimal electric range based on lightweighting decisions. The cost-effectiveness of lightweighting for BEV range extension is found to vary with income-dependent daily range limitation value, driving patterns and lightweighting technology costs. In general, adopting lightweighting in BEVs is more cost-effective for consumers with higher daily limitation value, as well as for those with higher driving intensity or suitable daily driving patterns. When the lightweighting involves a higher vehicle production cost, less lightweighting could reduce the overall PCO for BEV owners. 4 of the selected top ten BEV models are found to benefit from additional 2.09%–4.45% lightweighting. The method built in this study can guide automakers in planning R&D investments in battery and lightweighting technologies.

Empirical Analysis of the User Needs and the Business Models in the Norwegian Charging Infrastructure Ecosystem

The Norwegian charging infrastructure ecosystem was investigated from a user perspective by (1) developing knowledge of end-user experiences with public charging, (2) mapping BEV owners and future owner’s user-friendliness needs and the extent to which these needs are met, (3) pointing at potential user-friendliness improvements, (4) mapping the charging infrastructure ecosystem and business models, and (5) developing scenarios for the future system development and the impact on charging infrastructure user-friendliness. The article draws on the literature, a BEV (battery electric vehicle) and ICEV (internal combustion engine vehicle) owner survey, 15 BEV owner interviews, 21 charging infrastructure actor interviews, and open information sources on the charger actors. The unregulated charging system evolved into a complex web of actors that developed their own charging networks following their individually sensible business models, which in sum led to serious user-friendliness issues. To gain access to all chargers, users need to interact with up to 20–30 apps and 13 payment systems, which comes on top of different plug types, power levels, and charger interfaces. Some actors support roaming, while others oppose it. OEMs want users to interface with chargers through the navigation system. In the future, the system will become even more complex and less user friendly as more actors join unless, e.g., consolidation, regulation, or independent network orchestrators reduce the complexity.

Evaluating the current perceived cost of ownership for buses and trucks in China

Decarbonizing commercial vehicles, including buses, trucks, and special purpose vehicles, is essential for China to achieve its carbon neutrality target by 2060. However, understanding the adoption barriers of alternative fuel technologies for commercial vehicles has largely been limited to the total cost of ownership approach that mainly considers tangible costs. This study proposes a perceived cost of ownership (PCO) model, which monetizes both tangible and implicit costs, including charging annoyance for buses and trucks by size and classification under different use scenarios. In the case study for China's current bus and truck market, the PCO of alternative fuel technologies, such as battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs) are 27%–129% and 31%–86% higher than the most economically competitive powertrains – the compressed natural/liquefied petroleum gas technology and hybrid electric technology. BEVs could be cost-effective in the mini/midsize bus segment or in the daily short-distance driving scenario, and FCEVs could firstly be deployed in cities with a low hydrogen price. Though the implicit costs caused by range anxiety and charging annoyance account for at least 27% of BEV's PCO, making BEV uncompetitive, the battery swapping technique could reduce the implicit cost and overall cash outflow for BEV owners.

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.

Public charging station location determination for electric ride-hailing vehicles based on an improved genetic algorithm

In recent years, electric ride-hailing has increased considerably in the taxi industry with the development of battery electric vehicles (BEVs) and implementation of greenhouse gas (GHG) emission regulations. Private BEVs are charged mostly in private garages, while electric ride-hailing services require public charging stations (CSs). This work uses an improved genetic algorithm (GA) to locate public CSs by considering the investment of CS operators and the travel costs of BEV owners. A case study is presented with large-scale order data collected from the ride-hailing fleet of the city of Haikou and charging data from the electric ride-hailing fleet of the city of Shanghai. The elastic demand for electric ride-hailing is also considered by incorporating feedback between congestion at the CS and the geographical area. The proposed methodology uses the multipopulation genetic algorithm (MPGA) to provide more feasible allocations for public CSs and could reduce the total cost by 7.6%.

The role of psychological factors on vehicle kilometer travelled (VKT) for battery electric vehicle (BEV) users

Electric vehicles (EVs) are related to various symbols, identities, and beliefs, and are considered much more than a means of transport. Existing literature has investigated the contribution of financial incentives and various psychological factors to the EV purchase decision. However, few studies investigate the effect of psychological factors on post-purchase EV use. We emphasize that the ultimate success in the widespread acceptance of EVs depends acutely on their post-purchase use. This study empirically addressed the effect of perceived attributes related to EVs, perceived accidental risk, self-environmental identity, and general environmental beliefs on the annual vehicle kilometres travelled (VKT) by battery electric vehicle (BEV) owners. Drivers who own only BEVs and those who own both internal combustion engine vehicles and BEVs were compared to identify the role of psychological factors in BEV use in a Norwegian sample. The dataset was analysed using an ordinary least squared regression model. The socio-demographic characteristics and mobility patterns of the two groups are investigated. The findings indicate that economic aspects are positively associated with annual VKT for sole BEV owners, whereas perceived operating barriers have a negative effect on annual VKT for the other group. The results suggest the inclusion of psychological factors in predicting a more precise model of the induced travel demand of EV owners, which, in turn, is necessary to estimate energy demand accurately and to take steps in establishing the required infrastructure.

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.

Investigating the Influence of Consumer Behavior and Governmental Policy on the Diffusion of Electric Vehicles in Beijing, China

The adoption behavior of electric vehicles (EVs) has received considerable attention. However, the whole process of purchase behavior has not been well understood. In response, this paper investigates the uptake of EVs in Beijing, China, with a focus on the whole process of purchasing an EV, as well as the relevant policies. Specifically, the classical Howard–Sheth theory is used to the analyze the whole process of EV purchasing, which is divided into four stages here, namely, “Ignored and Neglected”, “Proactive Attention”, “Comparison and Selection of Vehicles” and “Usage Evaluation”. Furthermore, several “what-if” scenarios are set up to quantify the influence of some typical EV-related polices, including the license plate lottery policy and no traffic constriction on battery electric vehicles (BEVs). The results suggest: (1) 63.4% of BEV owners have rigid travel demands with cars; (2) BEVs with a driving range of 500 km, 30-minutes charging time and price of RMB 15,000 are the first choices for the majority of people; (3) 64% of BEV owners are influenced by positive comments from the users around them in the adoption of EVs; (4) the no traffic constriction on BEVs tends to be more influential than the license plate lottery policy in terms of the numbers of applications for the vehicle purchase permits and BEV purchasers, as well as energy saving and vehicular emission reduction. These findings should be helpful for different EV-related stakeholders, such as the government, to shape their policies and promote the development of EVs.

Simultaneous estimation of battery electric vehicle adoption with endogenous willingness to pay

Abstract To alleviate climate change and health issues caused by the transportation sector, a number of countries and sub-national regions have enacted policies to promote adoption of plug-in vehicles, including policies encouraging the battery electric vehicle (BEV) adoption. Despite the potential benefits of BEVs to both society and consumers, barriers such as high purchase price and limited driving range impede the widespread adoption of BEVs, as evidenced by the small market share of BEVs at present. There is a need to better understand the process in which people make vehicle decisions, and more importantly the value they are willing to pay for purchasing a vehicle. This study employs a bivariate model, wherein a binary probit model of BEV adoption is simultaneously estimated with a log-linear regression of willingness to pay (WTP). The model accounts for the endogeneity of WTP to the equation of BEV adoption and also captures the cross-equation error correlation between the two model components. The estimation results using the 2016 California Vehicle Survey determines the associated influence of factors such as socio-economic characteristics, residential energy sources and prices, and travel behavior, as well as two sets of attitudinal/preferential factors including importance of vehicle attributes and BEV concern factors. The sensitivity analysis of the results reveals that BEV owners are more inclined towards adding another BEV to their households than those with other fuel types and it can be inferred that consumers’ familiarity with the benefits and costs of BEVs may lead to its higher adoption rates.

Who are the early adopters of fuel cell vehicles?

All new technologies, including automotive technologies, are first purchased by early adopters. These consumers are currently posed with the choice of purchasing a fuel cell vehicle (FCV) or a variety of other alternatively fueled vehicles, including battery electric vehicles (BEVs). For FCVs to be commercially successful they need to carve out their own niche in the automotive market, something which may prove challenging in the face of strong BEV market growth. The results in this paper come from a questionnaire survey of 470 FCV owners and 1550 BEV owners. The paper explores the socio-economic profile, travel patterns, and attitudes of FCV buyers and compares them to the buyers of BEVs. The result suggests that the adopters of BEVs and FCV are similar in gender, level of education, household income, and have similar travel patterns. They have differences in age, ownership of previous alternative fuel vehicles, attitudes towards sustainability, and more FCV owners live in rented homes and apartment buildings. The results of the study suggest that FCVs may appeal to consumers who live in homes where they cannot recharge a BEV or install their own charger. FCVs still have several challenges to overcome, including the lack of hydrogen refueling stations and a lack of FCV models to choose from.

Investigating Real-World Energy Consumption of Electric Vehicles: A Case Study of Shanghai

The deployment of Battery Electric Vehicle (BEVs) has been viewed as a way to significantly reduce oil dependence, and bring about environmental and economic benefits. This study makes use of a rich database collected from 50 BEVs (i.e. Roewe E50) from Shanghai, China. We first examine BEV owners’ travel patterns with the vehicle usage data. Four factors, i.e., trip distance, speed, initial SOC, and ambient temperature, are analyzed to detect their impacts on energy consumption. Since energy consumption along a route are in part related to driving behavior, traffic conditions and infrastructure design, three routes embedded in sufficient vehicle in-use data are selected as the test objects. Each of them has a record that one particular driver traveling more than 20 times along the route. A multivariate linear regression (MLR) model is built up to estimate the trip energy consumption. The proposed method for analyzing the vehicle usage data to estimate energy consumption along routes can be used for research with larger fleets of BEVs in the future.

Enabling fast charging – Infrastructure and economic considerations

The ability to charge battery electric vehicles (BEVs) on a time scale that is on par with the time to fuel an internal combustion engine vehicle (ICEV) would remove a significant barrier to the adoption of BEVs. However, for viability, fast charging at this time scale needs to also occur at a price that is acceptable to consumers. Therefore, the cost drivers for both BEV owners and charging station providers are analyzed. In addition, key infrastructure considerations are examined, including grid stability and delivery of power, the design of fast charging stations and the design and use of electric vehicle service equipment. Each of these aspects have technical barriers that need to be addressed, and are directly linked to economic impacts to use and implementation. This discussion focuses on both the economic and infrastructure issues which exist and need to be addressed for the effective implementation of fast charging at 400 kW and above. In so doing, it has been found that there is a distinct need to effectively manage the intermittent, high power demand of fast charging, strategically plan infrastructure corridors, and to further understand the cost of operation of charging infrastructure and BEVs.

Incentives for promoting Battery Electric Vehicle (BEV) adoption in Norway

Norway has become a global forerunner in the field of electromobility and the BEV market share is far higher than in any other country. One likely reason for this is strong incentives for promoting purchase and ownership of BEVs. The purpose of this study is to describe the role of incentives for promoting BEVs, and to determine what incentives are critical for deciding to buy a BEV and what groups of buyers respond to different types of incentives. The questions are answered with data from a survey among nearly 3400 BEV owners in Norway.Exemptions from purchase tax and VAT are critical incentives for more than 80% of the respondents. This is very much in line with previous research, which suggests that up-front price reduction is the most powerful incentive in promoting EV adoption. To a substantial number of BEV owners, however, exemption from road tolling or bus lane access is the only decisive factor.Analyses show that there are clear delineations between incentive groups, both in terms of age, gender, and education. Income is a less prominent predictor, which probably results from the competitive price of BEVs in the Norwegian market. Perhaps most interesting is the assumed relation between incentives and character of transport systems the respondents engage in.

Modeling the Charging Choices of Battery Electric Vehicle Drivers by Using Stated Preference Data

The time, location, rate, and duration of plug-in electric vehicle (PEV) charging affects the performance of PEVs, including the amount of petroleum displaced, the emissions associated with electricity generation, the utilization of charging infrastructure, and the load placed on the electric grid. Empirical research has shown that charging preferences vary across PEV drivers and create large variations in the real-world utility factor for a fixed vehicle design. Nevertheless, most analyses of PEV usage rely on simple rule-based assumptions about charging behavior. In this paper, results from a web-based stated preference study of U.S. battery electric vehicle owners are displayed. Subjects were presented with situations representing potential charging opportunities and asked whether they would charge in such a situation. Binary, mixed, and latent class logit models of charging choices are investigated. The latent class logit fits the data best and allows identification of three basic types of decision maker: those who decide to charge on the basis of price and necessity; those who generally charge whenever they have the opportunity; and those who consider a wider range of factors, including charging power, dwell time, and the effect on their home charging bill. A majority of respondents are estimated to be willing to pay a premium of $2.35/h for Level 2 charging and $7.85/h for fast charging beyond what they would pay for Level 1 charging.

Optimizing the performance of vehicle-to-grid (V2G) enabled battery electric vehicles through a smart charge scheduling model

A smart charge scheduling model is presented for potential (1) vehicle-to-grid (V2G) enabled battery electric vehicle (BEV) owners who are willing to participate in the grid ancillary services, and (2) grid operators. Unlike most V2G implementations, which are considered from the perspective of power grid systems, this model includes a communication network architecture for connecting system components that supports both BEV owners and grid operators to efficiently monitor and manage the charging and ancillary service activities. This model maximizes the net profit to each BEV participant while simultaneously satisfying energy demands for his/her trips. The performance of BEVs using the scheduling model is validated by estimating optimal annual financial benefits under different scenarios. An analysis of popular BEV models revealed that one of the existing BEVs considered in the study can generate an annual regulation profit of $454, $394 and $318 when the average daily driving distance is 20 miles, 40 miles and 60 miles, respectively. All popular BEV models can completely compensate the energy cost and generate a positive net profit, through the application of the scheduling model presented in this paper, with an annual driving distance of approximately 15,000 miles. Simulation analysis indicated that the extra load distribution from the optimized BEV charging operations were well balanced compared to the unmanaged BEV operations.

A Travel Distance Power Consumption Model for Battery Electric Vehicles

Electric Vehicles (EVs) are becoming increasingly popular in recent years. Battery Electric Vehicles (BEVs) are a type of EVs use stored energy more efficiently than typical internal combustion engine powered counterparts. A key challenge of BEV is the limited battery energy. In order to relieve electric vehicle range anxiety for BEV owners and achieve an optimal battery sizing for BEV, a travel distance power consumption model has been created to model the travel behavior of BEV in the fleet. The model provides an efficient way of estimating the power requirements of electric vehicles reach the destination.

Large-scale deployment of electric vehicles in Germany by 2030: An analysis of grid-to-vehicle and vehicle-to-grid concepts

This study analyses battery electric vehicles (BEVs) in the future German power system and makes projections of the BEVs hourly load profile by car size (‘mini’, ‘small’, ‘compact’ and ‘large’). By means of a power plant dispatching optimisation model, the study assesses the optimal BEV charging/discharging strategies in grid-to-vehicle (G2V) and vehicle-to-grid (V2G) schemes. The results show that the 2% rise in power demand required to power these BEVs does not hamper system stability provided an optimal G2V scheme is applied. Moreover, such BEV deployment can contribute to further integrating wind and solar power generation. Applying a V2G scheme would increase the capacity factors of base and mid-load power plants, leading to a higher integration of intermittent renewables and resulting in a decrease in system costs. However, the evaluation of the profitability of BEVs shows that applying a V2G scheme is not a viable economic option due to the high cost of investing in batteries. Some BEV owners would make modest profits (€6 a year), but a higher number would sustain losses, for reasons of scale. For BEVs to become part of the power system, further incentives are necessary to make the business model attractive to car owners.

Building the Business Case for Telematics Based Diagnostics at Mahindra Reva

Mahindra Reva began a program of developing telematics capabilities in its electric vehicles around 4 years ago. The primary motivation for considering this technology was to enable remote diagnostics capabilities for our cars that are spread over 24 countries. This paper describes the evolution of telematics enabled capabilities at Mahindra Reva. This paper is divided into four broad sections: (1) we first present a technical overview of the telematics system; (2) we then briefly describe the categories of beneficiaries of telematics and the features and benefits to each category; (3) we then present the results from a survey of our existing battery electric vehicle owners on their perceived utility for such features; (4) based on these we develop a simple cost-benefit analysis to enable investment decisions in this technology and understand the payoffs from the investment.