Battery Electric Vehicles Drivers Research Articles

Multi-Criteria assessment of the user experience at E-Vehicle charging stations in Germany

We propose an assessment framework for the user experience at public electric vehicle charging stations in Germany, which enables a structured analysis from a driver’s perspective. The assessment criteria were derived from 20 interviews with battery electric vehicle drivers and corroborated by existing literature. Seven criteria are considered as particularly relevant: ease of use, green power, comfort, functionality, price, price transparency, and availability. Using Saaty’s Analytic Hierarchy Process approach, the weights assigned to these criteria were determined in an online survey with 108 drivers of battery electric vehicles. The results indicate the particular importance of availability and functionality. The assessment framework includes value scores that indicate the match between specific charging stations and the assessment criteria. The conceptual framework can help to support operators of public charging points and electric mobility providers in the design of their services and products, as well as guide policy-makers.

A Battery Electric Vehicle Transportation Network Design Model with Bounded Rational Travelers

With governments worldwide emphasizing environmental protection and the global focus on carbon reduction, the battery electric vehicle (BEV) industry has developed rapidly. An urban transportation network with BEVs as the main form of transportation will soon become mainstream. Motivated by the abovementioned background, a BEV transportation network design problem is investigated, and a network design model is established. The model aims to minimize the system travel time of BEV transportation networks and optimize the government’s lane expansion scheme (the location and number of lanes) under a limited budget. To consider the travel characteristics of BEV drivers, the charging time, range anxiety, and bounded rationality factors are simultaneously incorporated into the model. A heuristic algorithm is designed based on the active set algorithm to obtain the local optimal solution to the actual-scale problem. Moreover, a cutting-plane method is used to convert the original problem into a different form, and a column generation technique is embedded in the abovementioned algorithm to avoid the enumeration of paths. Sensitivity analyses of different levels of rationality of BEV drivers and government investment scales are performed. The experimental results demonstrate that the model and algorithm can effectively solve the problem and provide decision support for the government in formulating transportation infrastructure construction policies.

The costs and challenges of installing corridor DC Fast Chargers in California

A national network of DC Fast Charging infrastructure (DCFC) corridors can facilitate connectivity and long-distance travel using battery electric vehicles (BEVs). Optimal locations of such facilities are where BEV drivers have easy access to charging where drivers will not have to make any deviation from their pre-planned trips. Such optimally located corridor DCFCs are usually in remote, underserved communities and immediately next to a highway where they lack the advantages of shared utility infrastructure in an urban setting. Therefore, we find that other studies and industry knowledge of infrastructure investments do not apply to corridor DCFC locations. This study evaluates the full project costs of installing and commissioning 54 DC Fast Chargers in 36 sites located in major transportation corridors in California and finds significant variation in costs between them. While existing studies show costs ranging from $20,000 - $150,000, we find costs range anywhere between $122,000 and $440,000. This data is critical for new investment in the U.S. to construct a national charging network of DC Fast charging corridors. We find that a significant proportion of the full project costs are taken up by on site “make-ready infrastructure” costs that vary greatly due to site-specific factors and design choices. DCFC installations should be considered civil construction projects with significant electrical infrastructure planning and installation that requires the cooperation of many local stakeholders. We find that costs can be greatly reduced by working with local electrical utilities early in the design and site selection stages when possible. Our study finds that some cost shift towards utility side costs can greatly reduce overall construction costs for sites along highways. We also find that grid-connected DCFC design are substantially cheaper than off-grid solar powered DCFC with onsite storage.

Optimal Mileage of Electric Vehicles Considering Range Anxiety and Charging Times

This paper aims to find out the optimal mileage of battery electric vehicles (BEVs) by considering the trade-off between range anxiety and charging times, since frequent charging is a customary way to ease range anxiety for BEV drivers in practice, but declines the life cycle of battery and increases the charging cost. We propose a power function to measure the range anxiety and then solve two types of optimal mileages. The results show that the increment of BEVs’ cruising range increases the optimal absolute mileage but decreases the optimal relative mileage, while the improvement of the driver’s tolerance to range anxiety increases both. It is concluded that improving the driver’s tolerance, such as by expanding charging infrastructures and raising drivers’ practical experience with BEVs, is more effective than the increment of BEVs’ cruising range. The findings help to understand the optimal mileage of EVs and provide recommendations on the design of BEVs’ cruising range.

Charging behavior modeling of battery electric vehicle drivers on long-distance trips

Using the data from an interactive stated choice experiment, we analyzed battery electric vehicle (BEV) users’ fast-charging choices on long-distance trips using both static and dynamic discrete choices models (SDCMs and DDCMs). The results show that battery state of charge (SOC) and the ability to reach the next station without deviating from the original plan are the primary factors influencing charging decisions. Charging cost, time, the detour time to reach a station, and the amenities are statistically significant predictors, but less important than SOC and the ability to complete the trip as planned. The comparison of the SDCMs and DDCMs shows that SDCMs have better goodness-of-fit than more complicated DDCMs. By comparing the relative size of the coefficient, we estimated the monetary value of increasing charging power, moving the charging stations closer to highway exits, and having amenities such as restrooms, restaurants, and Wi-Fi at the charging stations.

Examining customer purchase decision towards battery electric vehicles in Vietnam market: A combination of self-interested and pro-environmental approach

This research aims at examining determinants of battery electric vehicle (BEV) purchase in Vietnam market from both self-interested and pro-environmental perspectives. Data collected from 200 BEV drivers show that purchase intention is positively affected by self-interested factors such as performance expectancy, effort expectancy and facilitating conditions. Additionally, intention could be shaped by personal norm, which is influenced by awareness of consequences and ascription of responsibility. Finally, intention is a reliable predictor of purchase behavior, and the relationship is moderated by personal innovativeness. This research provides various theoretical and practical implications to enhance public acceptance towards BEVs.

The Impact of Road Types on the Energy Consumption of Electric Vehicles

The widespread adoption of battery electric vehicles (BEVs) is hindered by their limited ranges and long charging times. Optimizing eco-driving strategies and BEV-specific routing through a thorough understanding of the BEV discharge behavior is vital to overcome these barriers in the short term. Therefore, this study investigates the impact of road types on the BEV discharge behavior while accounting for explanatory variables (i.e., ambient temperature, the initial state of charge, and driver). Thirty participants drove a 2017 Volkswagen eGolf along two predefined routes in Rhode Island. The results illustrate that BEVs are the most efficient on-road types with medium speed and low variation (i.e., “major collectors,” “minor arterials,” and “other principal arterials”). Meanwhile, findings confirmed a significantly higher average energy consumption rate on roads with higher average speeds (“interstates” and “other freeways/expressways”). Moreover, “local roads,” associated with a low average travel speed and a high variation in speed, showed a negative effect on BEV efficiency. The study further supported previous findings that BEVs are less efficient in colder temperatures. Thus, adapting eco-driving strategies, including the alteration of route choice to avoid “local roads” and “interstates,” can offer BEV drivers the potential for energy savings and range extensions. We propose a consideration of these findings to mitigate the effects of BEV range limitations and ease BEV adoption and ownership.

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.

Optimal charging strategy for intercity travels of battery electric vehicles

Limited driving range, long charging time and high charging costs affect the use of battery electric vehicles (BEVs) for intercity travels and often compel drivers to charge their vehicles more than once. This study proposes a multistage optimisation model to provide BEV drivers with a charging strategy for intercity travel. This model aims to jointly minimise travel time and charging cost and to determine the optimal amount of charged energy in each charging station located along the available routes. A dynamic programming method for solving this model is also designed. The feasibility and effectiveness of the proposed model and solution method are verified through the numerical example and simulations. The results indicate that the trade-offs between travel time and charging cost significantly influence the proposed solution, and the residual energy at the destination affects the availability of routes. The policy implications for the BEV-based intercity travels are discussed.

Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles With Federated Learning

Today's drivers of battery electric vehicles must deal with limited driving range in a sparse charging infrastructure. An accurate prediction of energy demand and driving range is therefore important and enables reliable routing and charge planning applications. Predictions of energy demand entail uncertainty, which can be considered directly with the use of probabilistic prediction algorithms. Machine learning algorithms are frequently applied in this context, but data used to train these algorithms are often distributed over a fleet of connected vehicles. Federated learning can be applied in this setting, but predictive uncertainty is typically not considered. We apply an extension of the federated averaging algorithm to learn probabilistic neural networks and linear regression models in a communication-efficient and privacy-preserving manner. We demonstrate the performance advantage of probabilistic prediction models over deterministic prediction models using proper scoring rules. Furthermore, we show that federated learning can improve the standard, driver-individual learning. Using probabilistic predictions, variable safety margins based on destination attainability can be applied, leading to increased effective driving range and reduced travel time.

Understanding the influence of state of health on the range anxiety of battery electric vehicle drivers

Understanding the influence of state of health on the range anxiety of battery electric vehicle drivers

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

AbstractDue to sparse charging infrastructure and short driving ranges, drivers of battery electric vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty battery. To help eliminate range anxiety and make BEVs more attractive for customers, accurate range estimation methods need to be developed. In recent years, many publications have suggested machine learning algorithms as a fitting method to achieve accurate range estimations. However, these algorithms use a large amount of data and have high computational requirements. A traditional placement of the software within a vehicle's electronic control unit could lead to high latencies and thus detrimental to user experience. But since modern vehicles are connected to a backend, where software modules can be implemented, high latencies can be prevented with intelligent distribution of the algorithm parts. On the other hand, communication between vehicle and backend can be slow or expensive. In this article, an intelligent deployment of a range estimation software based on ML is analyzed. We model hardware and software to enable performance evaluation in early stages of the development process. Based on simulations, different system architectures and module placements are then analyzed in terms of latency, network usage, energy usage, and cost. We show that a distributed system with cloud‐based module placement reduces the end‐to‐end latency significantly, when compared with a traditional vehicle‐based placement. Furthermore, we show that network usage is significantly reduced. This intelligent system enables the application of complex, but accurate range estimation with low latencies, resulting in an improved user experience, which enhances the practicality and acceptance of BEVs.

Predicted Network Equilibrium Model of Electric Vehicles With Stationary and Dynamic Charging Infrastructure on the Road Network

With the rapid development of charging-while-driving technology, the deployments of charging lanes and direct current fast chargers (dcFCs) inevitably affect the charging and route choices behavior of battery electric vehicle (BEV) drivers. This article describes the first attempt to develop a predicted user equilibrium model on the road network considering both dcFC and charging lanes charging choices. This article develops a two-stage algorithm: First, we design a stated preference survey to seek the most significant attributes to attract BEV drivers to use a charging lane. Based on that, the second stage constructs a network equilibrium model: Given the locations of public charging stations and charging lanes, we then assigned the BEV traffic flow to a network based on their charging preferences. As an illustrative example, we conducted a numerical analysis on a road network to demonstrate the effectiveness of the model and solution algorithm. The results suggest that income and travel cost are the most significant attributes of the choice behavior and that the locations of charging stations and charging lanes have a significant influence on the route choices of drivers.

En-Route Battery Management and a Mixed Network Equilibrium Problem Based on Electric Vehicle Drivers’ En-Route Recharging Behaviors

With the rapidly increasing number of electric vehicle users, in many urbans transport networks, there are mixed traffic flows (i.e., electric vehicles and gasoline vehicles). However, limited by driving ranges and long battery recharging, the battery electric vehicle (BEV) drivers’ route choice behaviors are inevitably affected. This paper assumes that in a transportation network, when BEV drivers are traveling between their original location and destinations, they tend to select the path with the minimal driving times and recharging time, and ensure that the remaining charge is not less than their battery safety margin. In contrast, gasoline vehicle drivers tend to select the path with the minimal driving time. Thus, by considering BEV drivers’ battery management strategies, e.g., battery safety margins and en-route recharging behaviors, this paper developed a mixed user equilibrium model to describe the resulting network equilibrium flow distributions. Finally, a numerical example is presented to demonstrate the mixed user equilibrium model. The results show that BEV drivers’ en-route recharging choice behaviors are significantly influenced by their battery safety margins, and under the equilibrium, the travel routes selected by some BEV drivers may not be optimal, but the total travel time may be more optimal.

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.

Positive influence of practical electric vehicle driving experience and range related knowledge on drivers' experienced range stress

The challenge of reducing worldwide greenhouse gas emissions has become increasingly important in recent years. Battery electric vehicles (BEVs) are a promising alternative having the potential for a CO2 neutral and sustainable utilization over the whole vehicle lifetime. However, BEVs limited range is still considered as one important usage barrier. Reducing drivers' experience of range stress is discussed as one possibility to overcome this barrier. The objective of the present research was to investigate the influence of range related knowledge, practical driving experience and the encounter of a critical range situation (i.e., experience of a small range buffer) on drivers' range stress. A specific focus lied on the effects of critical range situation encounters on experienced BEV drivers. A field experiment with 63 participants (20 experienced drivers and 43 inexperienced drivers) who drove a BEV in a critical range situation was conducted. Results indicate that range related knowledge and practical driving experience reduced range stress. The experience of the critical range situation is related to lower range stress, even for the experienced BEV drivers. The results can be useful to derive strategies and design recommendations aimed at reducing the experience of BEV drivers' range stress.

Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles

Due to the limited power cell performance of battery electric vehicles (BEVs), BEV drivers endure a short cruising range and a long charging time. Additionally, uneven charging facilities and unreasonable charging arrangements result in partial queuing and partial idling of charging stations. To solve these problems, it is critical to understand BEV charging behavior and its influential factors. Considering the urgency of BEV charging, BEV drivers tend to choose fast charging when BEV is in driving state. This study investigates fast charging behavior by utilizing private BEV connected data collected from Beijing. First, 130 private BEVs with travel rules were screened out. Using seven months of BEV data, a total of 15,752 trajectories were identified, among which 2161 have fast charging behavior. According to the relationship between fast charging behavior and some influential factors, including battery modeling, driving behavior, weather and environment, and even user habit, were empirically investigated. Moreover, the battery state of charge at the start time, time-origin, travel time duration, driving distance, driving speed, wind power, temperature, and last-fast-status are determined as significant influencing factors. Lastly, a prediction model based on the significant factors is proposed to estimate whether there is fast charging in a day trajectory. The proposed model achieves the best accuracy over compared models, i.e., univariate linear regression (ULR) with several factors and multivariate linear regression (MLR) model. The study is expected to help better understand fast charging behavior and further contribute to the future improvement of fast charging efficiency.

The multi-mode mobile charging service based on electric vehicle spatiotemporal distribution

A novel charging approach, named mobile charging service, is proposed for battery electric vehicles. The mobile charging service operator receives booking reservations from battery electric vehicle drivers including electricity, locations and required time windows, and based on the information allocated mobile charging vehicles do the charging. This service offers an alternative of fixed charging facilities and also flexibility to battery electric vehicle drivers as they do not need to wait in their vehicles during the charging service. Mobile charging service operators may be equipped with high service-efficiency mobile charging vehicles and low service-efficiency mobile charging vehicles. High service-efficiency mobile charging vehicles can complete the same charging service in a short period of time and charge high service fees, but the cost is high, while low service-efficiency mobile charging vehicles are the opposite. It is urgent to optimize both the number and routes of these two types of mobile charging vehicles at the same time to take advantage of their respective advantages from the operator’s perspective. To address this challenge, we introduce the mobile charging service problem with time windows and multiple mode service, and formulate it as a 0–1 mixed integer linear model. The designed model is tested in 24 small examples and two large instances in Dalian, China, where sensitivity analyses are performed to further evaluate the effectiveness of this model. The result shows, the combination of mobile charging vehicles with different service efficiency can help the operator obtain more profits than all mobile charging vehicles with the same service efficiency regardless of high or low service efficiency.

A Route Choice Model for Capturing Driver Preferences When Driving Electric and Conventional Vehicles

Battery Electric Vehicles (BEVs) play an important role in the needed transition away from fossil fuels and Internal Combustion Engine Vehicles (ICEVs). Although transport planning models and routing problem solutions exist for BEVs, the assumption that BEV drivers search for the shortest path while constraining energy consumption does not have any empirical basis. This study presents a study of actual route choice behavior of drivers from 107 Danish households participating in a large-scale experiment with BEVs and at the same time driving their ICEVs. GPS traces from 8968 BEV and 6678 ICEV routes were map matched to a detailed road network to construct observed routes, and a route choice model was specified and estimated to capture behavioral differences related to the vehicle type. The results reveal that drivers had a higher sensitivity to travel time and trip length when driving BEVs, and to route directness after receiving the BEV, regardless of vehicle type. The results suggest the need to revise the assumptions of transport planning models and routing problems for BEVs in order not to fail to predict what drivers will do by ignoring differences and similarities related to vehicle type.

Analysis of the effect of charging needs on battery electric vehicle drivers’ route choice behaviour: A case study in the Netherlands

Electric travelling appears to dominate the transport sector in the near future due to the needed transition from internal combustion vehicles (ICV) towards Electric Vehicles (EV) to tackle urban pollution. Given this trend, investigation of the EV drivers’ travel behaviour is of great importance to stakeholders including planners and policymakers, for example in order to locate charging stations. This research explores the Battery Electric Vehicle (BEV) drivers route choice and charging preferences through a Stated Preference (SP) survey. Collecting data from 505 EV drivers in the Netherlands, we report the results of estimating a Mixed Logit (ML) model for those choices. Respondents were requested to choose a route among six alternatives: freeways, arterial ways, and local streets with and without fast charging. Our findings suggest that the classic route attributes (travel time and travel cost), vehicle-related variables (state-of-charge at the origin and destination) and charging characteristics (availability of a slow charging point at the destination, fast charging duration, waiting time in the queue of a fast-charging station) can influence the BEV drivers route choice and charging behaviour significantly. When the state-of-charge (SOC) at the origin is high and a slow charger at the destination is available, routes without fast charging are likely to be preferred. Moreover, local streets (associated with slow speeds and less energy consumption) could be preferred if the SOC at the destination is expected to be low while arterial ways might be selected when a driver must recharge his/her car during the trip via fast charging.