Vehicle Usage Data Research Articles

The Indirect Carbon Cost of E-Mobility for Select Countries Based on Grid Energy Mix Using Real-World Data

Electric vehicles are considered by many as an emission-free or low-emission solution to meet the challenge of sustainable transportation. However, the operational input, electrical energy, has an associated cost, greenhouse gasses, which results in indirect emissions. Given this knowledge, we pose the following question: “Are zero-emission transportation targets achievable given our current energy mix?” The objective of this article is to assess the impact of a grid’s energy mix on the indirect emissions of an electric vehicle. The study considers real-world data, vehicle usage data from an electric vehicle, and carbon intensity data for India, the USA, France, the Netherlands, Brazil, Germany, and Poland. Linear programming-based optimization is used to compute the best charging scenario for each of the given grids and, consequently, the indirect emissions are compared to those of a high-efficiency 1.5 L diesel internal combustion engine for the vehicle: a 2019 Renault Clio dCi 85. The results indicate that for grids with low renewable energy penetration, such as those of Poland and India (Maharashtra), an electric vehicle, even when optimally charged, can be classified as neither a low- nor zero-emission alternative to normal thermal vehicles. Also, for grids with elevated levels of variation in their carbon intensity, there is significant potential to reduce the carbon footprint related to charging an electric vehicle. This article provides a real-world perspective of how an electric vehicle performs in the face of different energy mixes and serves as a precursor to the development of robust indicators for determining the carbon reductions related to the e-mobility transition.

Design of Next-Generation Automotive Systems: Challenges and Research Opportunities

Abstract The automotive industry is undergoing a massive transformation, driven by the mega-trends of “CASE”: connected, autonomous, shared, and electric. These trends are affecting the nature of automobiles, both internally and externally. Internally, the transition from internal combustion engines (ICE) to electric drive-trains has resulted in a shift from hardware-centric vehicles to software-defined vehicles (SDVs), where software is increasingly becoming the dominant asset in the automotive value chain. These trends are leading to new design challenges such as how to manage different configurations of design, how to decouple the design of software and services from hardware, and how to design hardware to allow for upgrades. Externally, automobiles are no longer isolated products. Instead, they are part of the larger digital ecosystem with cloud connectivity. Vehicle usage data are increasingly connected with smart factories, which create new opportunities for agile product development and mass customization of features. The role of the human driver is also changing with increasing levels of autonomy features. In this paper, the authors discuss the ongoing transformation in the automotive industry and its implications for engineering design. The paper presents a road map for engineering design research for next-generation automotive applications.

Capturing diversity in electric vehicle charging behaviour for network capacity estimation

This paper proposes a stochastic data-driven model for uncontrolled charging that accurately captures diversity in individual consumer behaviour. This is important because understanding the diversity between consumers is necessary to accurately estimate the number of electric vehicles’ charging a distribution network could support without reinforcements. The model combines readily available travel survey data with high resolution data from an electric vehicle trial, using clustering and conditional probabilities. We demonstrate through a case study of UK residential charging that existing approaches may overestimate the increase in peak distribution network demand by 50%, which has implications for assessing the cost of network investments required. We also show that the peak charging demand varies regionally from 0.2–1.4 kW per household, demonstrating the importance of using locally representative vehicle usage data.

Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand

A car-sharing system has been playing an important role as an alternative transport mode in order to avoid traffic congestion and pollution due to a quick growth of usage of private cars. In this paper, we propose a novel vehicle relocation system with a major improvement in threefolds: (i) data preprocessing, (ii) demand forecasting, and (iii) relocation optimization. The data preprocessing is presented in order to automatically remove fake demands caused by search failures and application errors. Then, the real demand is forecasted using a deep learning approach, Bidirectional Gated Recurrent Unit. Finally, the Minimum Cost Maximum Flow algorithm is deployed to maximize forecasted demands, while minimizing the amount of relocations. Furthermore, the system is deployed in the real use case, entitled “CU Toyota Ha:mo,” which is a car-sharing system in Chulalongkorn University. It is based on a web application along with rule-based notification via Line. The experiment was conducted based on the real vehicle usage data in 2019. By comparing in real environment in November of 2019, the results show that our model even outperforms the manual relocation by experienced staff. It achieved a 3% opportunity loss reduction and 3% less relocation trips, reducing human effort by 17 man-hours/week.

Patterns of spinal cord injury in automobiles versus motorcycles and bicycles.

Retrospective case series. To examine the patterns and relative rates of occurrence of spinal cord injury (SCI) in automobiles compared to motorcycles and bicycles. Los Angeles County, California. A retrospective chart review of SCI consults at Rancho Los Amigos National Rehabilitation Center in Los Angeles County, California between 2003 and 2013 were selected and screened for a mechanism of injury involving a vehicular accident. Chart review was performed to determine neurological levels and extent of impairment, which were graded according to the International Standards for Neurological Classification of Spinal Cord Injury. We identified 398 cases of SCI from 2003 to 2013 that fit the inclusion criteria. Overall, the relative percentages of ASIA impairment scale (AIS) A/B/C/D did not differ statistically across automobiles, motorcycles, or bicycles. When stratified by spinal region, motorcycles had a higher percentage of thoracic SCIs compared to automobiles. Automobiles resulted in more cervical SCIs with few injuries in the lumbar region. Bicycle patterns followed automobiles, not motorcycles. Thoracic SCIs were more likely graded motor complete than cervical or lumbar injuries, regardless of the mechanism. Automobile, motorcycle, and bicycle related SCIs occur primarily in the cervicothoracic region. SCIs due to motorcycle accidents have a higher predilection for the thoracic region, and there is a statistically higher percentage of motor complete injuries. A higher percentage of cervical SCIs occur as a result of automobile and bicycle accidents. Extrapolations from motor vehicle usage data suggest that the relative rate of occurrence of SCI for motorcycles is higher than for automobiles.

Cost-Effectiveness Analysis of Plug-In Hybrid Electric Vehicles using Vehicle Usage Data Collected in Shanghai, China

This paper investigates the economic viability of plug-in hybrid electric vehicles (PHEV) in Shanghai, China based on a real-world in-use PHEV dataset. To quantify PHEV drivers’ gross profit compared with internal combustion engine vehicle (ICEV) owners, a total cost of ownership (TCO) model is adopted taking account of vehicle retail price, tax credits, subsidies, insurance, maintenance, energy prices, and resale value. The impact of the determinants for gross profit are examined in relation to vehicle distance traveled (VDT) electrically, gasoline price, electricity price, and car-buying cost. It is found that: (1) only 10% of the deployment of PHEVs (i.e., BYD Qin) is economically viable if the benefit from a free license plate is exempt; (2) the 100-kilometer gross profit of PHEVs increases linearly with the electric driving distance, while the saving of energy cost per kilometer decreases with the total VDT; (3) PHEVs’ profit could be significantly improved by reducing the car-buying cost—a decrease of 10% in car-buying cost makes 80% of the PHEV deployment feasible; and (4) if switching the daytime charges to off-peak hours, 50% of the PHEV deployment will become feasible.

A data-based investigation of vehicle maintenance cost components using ANN

Vehicle maintenance is a critical and vital operational component that determines vehicle performance and service longevity. The severity of vehicle usage has been defined as one of the key factors that determine vehicle maintenance requirements. Nigeria is a tropical country with intense heat, poor road network and quality. These factors severely affect car performance and raises maintenance demands toward ensuring vehicle reliability and optimum performance. In this study, vehicle maintenance cost and fuel consumption data in terms of cost and volume, together with the mileage coverage as the vehicle usage data, for two corporate organizations were analyzed. The data was collected and systematically analyzed. The common faults were categorized and their frequency of occurrence was determined. An artificial Neural Network (ANN) model was developed for predicting future maintenance cost, given a set of anticipated vehicle usage inputs. The model has an overall correlation R-value of 0.76645.

An Investigation of Battery Electric Vehicle Driving and Charging Behaviors Using Vehicle Usage Data Collected in Shanghai, China

This paper investigates the driving and charging behaviors of battery electric vehicle (BEV) drivers observed in Shanghai, China. The summary statistics are compared with the observations from the U.S. EV Project. A machine-learning approach, namely self-organizing feature map (SOM), is adopted as a classifier to analyze BEV drivers’ habitual behaviors. The inter-driver heterogeneities are examined in terms of: the distributions of distance traveled per day, the start time of charging, the number of charges per day, distance traveled between consecutive charges, battery state of charge (SOC) before and after charging, and time-of-day electricity demand. It is found that ( a) BEV drivers demonstrate conservative charging behaviors, leading to short distances between consecutive charging events; ( b) a significant number of BEV drivers in Shanghai charge during daytime; ( c) the distributions depicting the driving and charging patterns vary greatly due to the diversity in travel activities among different drivers.

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.

A statistical approach to estimating acceptance of electric vehicles and electrification of personal transportation

The environmental and economic impact of electric vehicles (EVs) will depend on the fraction of users that can accept an EV of a given capability, and then in turn on how those EVs are actually used. Historically, estimates of the fraction of total travel that could be electrified as a function of EV range are based on vehicle usage data for large populations of vehicles, most often the National Household Travel Survey (NHTS). Two assumptions implicit in such estimates are subject to question: (1) that any user could accept an EV as a primary vehicle and would use it for all trips within its range, and (2) that the usage patterns of any individual EV user are the same as that exhibited by entire population. The first assumption is clearly unrealistic; willingness to accept an EV is dependent on the transportation needs and alternatives readily available to each individual user. As a surrogate for a priori knowledge of individual preferences, we use a crude metric of acceptance defined as a threshold frequency of need for alternative transportation above which all users would not accept the inconvenience. To test the validity of the second assumption and better estimate market and electrification potential, we analyze roughly 1year of usage data for each of 133 instrumented vehicles in Minneapolis–St. Paul. We find a characteristic individual usage pattern that does not resemble the average over a large number of vehicles. Using the surrogate metric of EV acceptance and a simple payback model, we show that although the market acceptance and electrification potential of EVs are severely limited by battery cost, it is possible to determine an optimal EV range. Using the same usage data and payback model, we show that plug-in hybrid electric vehicles (PHEVs) can be much more effective than all-electric vehicles in electrifying personal transportation.

PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data

Present-day urban vehicle usage data recorded on a per second basis over a one-year period using GPS devices installed in 76 representative vehicles in the city of Winnipeg, Canada, allow predicting the electric load profiles onto the grid as a function of time for future plug-in electric vehicles. For each parking occurrence, load profile predictions properly take into account important factors, including actual state-of-charge of the battery, parking duration, parking type, and vehicle powertrain. Thus, the deterministic simulations capture the time history of vehicle driving and parking patterns using an equivalent 10 000 urban driving and parking days for the city of Winnipeg. These deterministic results are then compared to stochastic methods that differ in their treatment of how they model vehicle driving and charging habits. The new stochastic method introduced in this study more accurately captures the relationship of vehicle departure, arrival, and travel time compared to two previously used stochastic methods. It outperforms previous stochastic methods, having the lowest error at 3.4% when compared to the deterministic method for an electric sedan with a 24-kWhr battery pack. For regions where vehicle usage data is not available to predict plug-in electric vehicle load, the proposed stochastic method is recommended. In addition, using a combination of home, work, and commercial changing locales, and Level 1 versus Level 2 charging rates, deterministic simulations for urban run-out-of-charge events vary by less than 4% for seven charging scenarios selected. Using the vehicle usage data, charging scenarios simulated have no significant effect on urban run-out-of-charge events when the battery size for the electric sedan is increased. These results contribute towards utilities achieve a more optimal cost balance between: 1) charging infrastructure; 2) power transmission upgrades; 3) vehicle battery size; and 4) the addition of new renewable generation to address new electric vehicle loads for addressing energy drivers.

Development and Application of a Vehicle Procurement Model for Rural Fleet Asset Management

Advanced asset management systems have emerged as important tools in the management, maintenance, and procurement of vehicles for transit fleet operators. Effective design and use of an asset management system can increase productivity, enhance public perception, and provide a consistent basis for decision making and planning. This paper documents the design and application of a vehicle procurement model in an asset management system created for the Alabama Department of Transportation to manage vehicles purchased and operated through the Section 5311 federal grant program. The vehicle procurement model predicts future vehicle serviceability, or condition rating, by using a combination of factors, including service area socioeconomic data and vehicle usage data. Application of the system helps transportation professionals estimate the overall fleet quality, identify vehicles that will need to be replaced each year, aid in the management of vehicles, and provide a basis for predicting future funding and budgetary needs.

Development and Application of a Vehicle Procurement Model for Rural Fleet Asset Management

Advanced asset management systems have emerged as important tools in the management, maintenance, and procurement of vehicles for transit fleet operators. Effective design and use of an asset management system can increase productivity, enhance public perception, and provide a consistent basis for decision making and planning. This paper documents the design and application of a vehicle procurement model in an asset management system created for the Alabama Department of Transportation to manage vehicles purchased and operated through the Section 5311 federal grant program. The vehicle procurement model predicts future vehicle serviceability, or condition rating, by using a combination of factors, including service area socioeconomic data and vehicle usage data. Application of the system helps transportation professionals estimate the overall fleet quality, identify vehicles that will need to be replaced each year, aid in the management of vehicles, and provide a basis for predicting future funding and budgetary needs.

Exploring potential electric vehicle utilization: A computer simulation

This study describes a computer simulation of electric vehicle (EV) usage based on conventional vehicle usage data and battery performance characteristics. A Monte Carlo technique was used to simulate the EV daily travel for the duration of the battery life. The vehicle usage data employed in this study refer to the least driven conventional vehicle in households owning two or more vehicles and include information on number of vehicle tours per day, tour distance, tour duration and the time between tours. (A vehicle tour is defined as a sequence of trips starting and ending at home.) The data reflect variations in vehicle use among different days of the week and different households. The specific set of battery performance parameters used as input to the simulation are (1) battery life is limited to 600 deep-discharge cycles; (2) maximum range of EV is 80 miles when the battery is new, and it decreases to 48 miles at the end of battery lifetime and (3) one hour of battery charging provides 10 miles of travel. Relative to the ability of the EV to meet the travel requirements of the “least used vehicle” in multiple vehicle households, simulations indicate that the EV could satisfy almost all (92 to 96%) of the tours but only 66 to 74% of the annual miles traveled.