Refueling Behavior Research Articles

Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

This paper presents an analysis of two methodologies that can be used to predict refueling behavior. Both models aim to provide insights into hydrogen-fuel vehicle users’ refueling patterns and behaviors. The first model leverages probabilistic transitions between refueling states to simulate and predict the refueling behavior of hydrogen vehicle users. In contrast, the GP-1 model employs Gaussian processes to capture the underlying patterns and uncertainties in hydrogen-fuel vehicle refueling behavior, taking into consideration additional factors such as weather conditions and the time of the year. The model demonstrates statistical significance and accuracy in predicting trips while identifying the insignificance of precipitation and high ambient temperatures. The methodologies, findings, strengths, and limitations of the two models were tested and compared to identify their relevant contributions. By contrasting their methodologies and evaluating their predictive performance, using performance metrics such as accuracy, precision, and recall values, this study provides valuable insights into the strengths and limitations of each approach. Limitations include assuming a stationary refueling process and excluding external factors and limitations related to data availability, as well as the absence of a specific focus on hydrogen-fuel vehicles. By understanding the differences and similarities between these two models, this paper aims to provide a unique perspective on gaps and further requirements for accurate prediction and modeling of refueling behavior to guide policymakers, infrastructure planners, and stakeholders in making informed decisions regarding the design and optimization of hydrogen refueling infrastructure.

Optimized Configuration and Operating Plan for Hydrogen Refueling Station with On-Site Electrolytic Production

Hydrogen refueling stations (HRSs) are critical for the popularity of hydrogen vehicles (fuel cell electric vehicles—FCEVs). However, due to high installation investment and operating costs, the proliferation of HRSs is difficult. This paper studies HRSs with on-site electrolytic production and hydrogen storage devices and proposes an optimization method to minimize the total costs including both installation investment and operating costs (OPT-ISL method). Moreover, to acquire the optimization constraints of hydrogen demand, this paper creatively develops a refueling behavior simulation method for different kinds of FCEVs and proposes a hydrogen-demand estimation model to forecast the demand with hourly intervals for HRS. The Jensen–Shannon divergence is applied to verify the accuracy of the hydrogen-demand estimation. The result: 0.029 is much smaller than that of the estimation method in reference. Based on the estimation results and peak-valley prices of electricity from the grid, a daily hydrogen generation plan is obtained, as well as the optimal capacities of electrolyzers and storage devices. As for the whole costs, compared with previous configuration methods that only consider investment costs or operating costs, the proposed OPT-ISL method has the least, 8.1 and 10.5% less, respectively. Moreover, the proposed OPT-ISL method shortens the break-even time for HRS from 11.1 years to 7.8 years, a decrease of 29.7%, so that the HRS could recover its costs in less time.

Analysis of refueling behavior of hydrogen fuel vehicles through a stochastic model using Markov Chain Process

Fueling of hydrogen vehicles is an important factor that needs to be analyzed and optimized for hydrogen fuel vehicles to attain significant market penetration and relevance. This paper presents and applies a stochastic model that integrates the Markov Chain Process to trend the refueling patterns of hydrogen fuel vehicles and driver behavior within the South African transport sector. The aim is to understand the stochastic nature of the daily refueling behavior of hydrogen fuel vehicles. The key contribution of this paper includes the development of data on refueling behavior of hydrogen fuel vehicles such as fueling times, fueling capacity, and time spent at refueling stations using the algorithm which are important factors that needs to be analyzed and optimized for hydrogen fuel vehicles to attain significant market penetration and relevant issues such as infrastructure development. This accounts for the lack of data within the South African context on refueling patterns of hydrogen fuel vehicle users and aids in understanding the expected hydrogen fuel consumption (demand) a typical station would experience. Another significant contribution is the daily and weekly refueling patterns (profiles) of hydrogen fuel vehicles generated by the model. This provides useful insights and trends on refueling patterns, specifically in South Africa, where there is little hydrogen fuel vehicles present. The accuracy of the refueling patterns was ascertained by verifying the model against real-life hydrogen fuel vehicle data and through sensitivity analysis. Also, the model can be applied to quantify the effects of different parameters on refueling patterns.

A Novel Aircraft Refueling Behavior Detection Model based on Deep Learning

How to efficiently and accurately monitor the sparse aircraft Refueling behaviors from a large amount of video streams is of great significant for improving the level of management and refueling efficiency of aviation fuel stations. Due to the COVID-19 virus epidemic, the number of flights has dropped severely, the collection of image samples for refueling behaviors from large airport becomes difficult, which hinders the real-time detection of Refueling behaviors and reduces the efficiency of aviation fuel station. Therefore, automatically detecting the refueling behaviors of each station in time and accurately from a large number of aviation refuel stations still keeps challenging. To address this challenge, we propose a novel aircraft refueling behavior detection model based on deep learning, to quickly and accurately determine the refueling behaviors through analysing the video stream collected from the massive cameras deployed in the airport. Our proposed model adopts Inception v3 architecture of ImageNet to realize the model capability of transfer learning, the data augmentation to address the issue of overfitting, and the mAP (mean Average Precision) to test the performance. Our proposed model is also applied in the detection of refueling behaviors in China National Aviation Fuel Group, LTD (CNAF). The practical application results show better performance than other existing methods. Our work will promote the updating of related industry standard.

Running on empty – Users’ charging behavior of electric vehicles versus traditional refueling

The demand-oriented establishment of a charging infrastructure is crucial for the deep market penetration of electric vehicles. In order to identify specific optimal locations for charging stations, a deeper understanding of the users’ charging behavior is required. For reliable simulations and forecasts, it is not only necessary to take the charging behavior of the current early-adopter group into account, but also to compare it with the current refueling behavior of those, who could switch to electric vehicles in the near and medium-term future. The study follows a two-stage research approach. First of all, qualitative interviews (N=24) were conducted to identify refueling behavior in terms of behavioral patterns, refueling motives and conditions. The second step involves a quantitative comparison of the refueling and charging behavior with regard to conditions, frequencies and critical levels. A large-scale questionnaire study (N=1021) with car drivers from Germany was carried out for this purpose. The results showed that the conditions for a refueling or charging decision only differ partially. While financial aspects play a minor role for e-vehicle users, for internal combustion engine users planning and habit are less important. There is no difference between the two groups regarding range-relevant factors. In particular, the filling level perceived as critical is identical for fuel tanks and batteries. In terms of behavior patterns, e-vehicle users tend to charge consumed quantities in a timely manner, while users of vehicles with combustion engines often run on empty and then refill the tank completely. Only a few predictors for both behaviors could be identified. While socio-demographic factors hardly play a role, socio-economy and vehicle utilization are the biggest predictors. The results are incorporated into the modeling of potential users when planning new charging infrastructure.

Empirical Study on CNG Refilling Behaviors of Different Timescale Based on Boxplot Method

The number of CNG secondary filling stations is limited in cities because of their security requirement. However, the number of CNG vehicles is increasing. It is important to improve the efficiency of CNG secondary filling stations, as critical infrastructures in CNG industry, to meet the increasing demand. As a first step to find possible improvement methods, the periodical CNG refueling behaviors are empirically studied in this article. By boxplot method, the intraday, day-of-the-week and monthly CNG refueling behaviors are studied. Based on analysis of more 250 thousand records of a CNG secondary filling station in Tianjin, the hourly, daily, and monthly volume, initial pressure, final pressure and the time spent in each refueling behavior are presented. From the central tendency and outliers analysis, it is found that one possible way to improve efficiency is to consider factors in intraday and monthly timescale. Another way to improve efficiency is to reduce outliers, which are independent of time.

Visual Exploration of Vehicle Refueling Behavior with Geospatial and Temporal Features

Visual Exploration of Vehicle Refueling Behavior with Geospatial and Temporal Features

Decentralized refueling of compressed natural gas (CNG) fleet vehicles in Southern California

While some compressed natural gas (CNG) vehicle fleets have a station at their base for central refueling, others lack refueling capability at their fleet depot and must rely on publicly available stations. To understand this kind of decentralized refueling behavior, we surveyed 133 drivers of CNG fleet vehicles at six public stations across the Los Angeles region. Nearly one-third of CNG fleet drivers were solely reliant upon public refueling for their operations. For each driver's refueling trip, we used GIS to compare the chosen station's proximity to the driver's fleet base and their deviation from the shortest path between their previous and next stops relative to all other stations they could have chosen. This revealed-preference approach shows that fleet drivers chose the station with the smallest deviation over the station closest to base by a 6:1 ratio, though this ratio varied by the driver's availability of central refueling and type of vehicle and route. Given that public stations remain essential to meeting decentralized refueling demand for other fleets as well as consumers, these findings have important implications for fleets that are both considering the adoption of CNG vehicles and the additional investment of hosting central refueling infrastructure at their base.

Sensing the Pulse of Urban Refueling Behavior

Urban transportation is an important factor in energy consumption and pollution, and is of increasing concern due to its complexity and economic significance. Its importance will only increase as urbanization continues around the world. In this article, we explore drivers’ refueling behavior in urban areas. Compared to questionnaire-based methods of the past, we propose a complete data-driven system that pushes towards real-time sensing of individual refueling behavior and citywide petrol consumption. Our system provides the following: detection of individual refueling events (REs) from which refueling preference can be analyzed; estimates of gas station wait times from which recommendations can be made; an indication of overall fuel demand from which macroscale economic decisions can be made, and a spatial, temporal, and economic view of urban refueling characteristics. For individual behavior, we use reported trajectories from a fleet of GPS-equipped taxicabs to detect gas station visits. For time spent estimates, to solve the sparsity issue along time and stations, we propose context-aware tensor factorization (CATF), a factorization model that considers a variety of contextual factors (e.g., price, brand, and weather condition) that affect consumers’ refueling decision. For fuel demand estimates, we apply a queue model to calculate the overall visits based on the time spent inside the station. We evaluated our system on large-scale and real-world datasets, which contain 4-month trajectories of 32,476 taxicabs, 689 gas stations, and the self-reported refueling details of 8,326 online users. The results show that our system can determine REs with an accuracy of more than 90%, estimate time spent with less than 2 minutes of error, and measure overall visits in the same order of magnitude with the records in the field study.

Considering the dynamic refueling behavior in locating electric vehicle charging stations

Abstract. Electric vehicles (EVs) will certainly play an important role in addressing the energy and environmental challenges at current situation. However, location problem of EV charging stations was realized as one of the key issues of EVs launching strategy. While for the case of locating EV charging stations, more influence factors and constraints need to be considered since the EVs have some special attributes. The minimum requested charging time for EVs is usually more than 30minutes, therefore the possible delay time due to waiting or looking for an available station is one of the most important influence factors. In addition, the intention to purchase and use of EVs that also affects the location of EV charging stations is distributed unevenly among regions and should be considered when modelling. Unfortunately, these kinds of time-spatial constraints were always ignored in previous models. Based on the related research of refuelling behaviours and refuelling demands, this paper developed a new concept with dual objectives of minimum waiting time and maximum service accessibility for locating EV charging stations，named as Time-Spatial Location Model (TSLM). The proposed model and the traditional flow-capturing location model are applied on an example network respectively and the results are compared. Results demonstrate that time constraint has great effects on the location of EV charging stations. The proposed model has some obvious advantages and will help energy providers to make a viable plan for the network of EV charging stations.

Post-Exercise Recovery Refueling Knowledge, Behavior and Attitudes of Athletes

Post-Exercise Recovery Refueling Knowledge, Behavior and Attitudes of Athletes

The fuel-travel-back approach to hydrogen station siting

The problem of hydrogen station location is often studied through understanding refueling behavior or reviewing the experience of gasoline stations. Driven by the notion “where you drive more is where you more likely need refueling”, this paper develops a new approach where station siting is treated as a “fuel-travel-back” problem and the only required data is the distribution of vehicle miles traveled. Such a fuel-travel-back problem is in nature a typical transportation problem and can be solved using a mix-integer-programming model. When the total fuel-travel-back time is minimized, so is the average refueling travel time of a random motorist, for which theoretical deduction is provided. The model is applied to derive an optimal station roll-out scheme for Southern California. The results show that, if station size constraints are relaxed, only 18% of the existing gasoline station number is needed to achieve the current fuel accessibility of gasoline in the region. Fewer stations lead to a larger station size, suggesting a need to re-examine the current thinking on designs of hydrogen station and distribution systems and to conduct more regional studies to identify more or less promising regions for hydrogen. The results also indicate that early stations should be located strategically in a regional sense, and sometimes including low-demand locations, which is contradictory to the conventional wisdom.

Refueling behavior of automobile drivers

This paper presents a descriptive and explanatory analysis of refueling behavior of gasoline-car drivers. The relationship between drivers' attributes and their refueling behavior is examined using the results of driver surveys conducted at several refueling outlets in northern California. The objectives of the analysis are to identify spatial and temporal characteristics of refueling behavior, and to evaluate the variations in refueling behavior across driver population segments. The results show that refueling behavior is only weakly correlated with demographic and socioeconomic descriptors of the driver. On the other hand, variables representing car ownership and utilization are strongly associated with refueling behavior.