scholarly journals Hybrid Electric Vehicle Characteristics Change Analysis Using Mileage Interval Data

2020 ◽  
Vol 10 (16) ◽  
pp. 5533
Author(s):  
Jiyoung Woo ◽  
Inbeom Yang ◽  
Chongun Pyon
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Interval Data ◽  
Statistical Properties ◽  
Change Analysis ◽  
Hybrid Electric ◽  
Significant Change ◽  
The Relationship

In this work, the relationship between the accumulated mileage of a hybrid electric vehicle (HEV) and the data provided from vehicle parts has been analyzed. Data were collected while traveling over 70,000 km in various paths. The collected data were aggregated for 10 min and characterized in terms of centrality and variability. It has been examined whether the statistical properties of vehicle parts are different for each cumulative mileage interval. When the cumulative mileage interval is categorized into 30,000–50,000, 50,000–60,000, and 60,000–70,000, the statistical properties contributed in classifying the mileage interval with accuracy of 92.68%, 82.58%, and 80.65%, respectively. This indicates that if the data of the vehicle parts are collected by operating the HEV for 10 min, the cumulative mileage interval of the vehicle can be estimated. This makes it possible to detect abnormality or characteristics change in the vehicle parts relative to the accumulated mileage. It also can be used to detect abnormal aging of vehicle parts and to inform maintenance necessity. Furthermore, a part or module that has a significant change in characteristics according to the mileage interval has been identified.

scholarly journals A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

2019 ◽  
Vol 11 (20) ◽  
pp. 5761 ◽  
Author(s):  
Bolong Yun ◽  
Daniel (Jian) Sun ◽  
Yingjie Zhang ◽  
Siwen Deng ◽  
Jing Xiong
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Choice Model ◽  
Environmental Benefits ◽  
The Public ◽  
Home Based ◽  
Hybrid Electric ◽  
Charging Stations ◽  
The Relationship

Electric vehicles (EVs) are promising alternatives to replace traditional gasoline vehicles. The relationship between available charging stations and electric vehicles has to be precisely coordinated to facilitate the increasing promotion and usage of EVs. This paper aims to investigate the choice of the charging location with global positioning system (GPS) trajectories of 700 Plug-in Hybrid Electric Vehicle (PHEV) users as well as the charging facility data in Shanghai. First, the recharge accessibility of each PHEV user was investigated, and 9% rely solely on public charging networks. Then, we explored the relationship between fuel consumption and the average distance between charging to analyze the environmental benefits of PHEVs. It was found that 16% PHEVs are similar to EVs, and 9% whose drivers rely solely on public charging stations are similar to internal combustion engine (ICE) vehicles. PHEV users were divided into four types based on the actual recharge access: home and workplace-based user (private + workplace + public), the home-based user (private + public), the workplace-based user (workplace + public), and the public-based user (public). Models were developed to identify and compare the factors that influence PHEV user’s charging location choices (home, workplace, and public stations). The modeling and results interpretation were carried out for all PHEV users, home and workplace-based users, home-based users, and workplace-based users, respectively. The estimation results demonstrated that PHEV users tended to charge at home or workplace rather than public charging stations. Charging price, charging price tariff, the initial state of charge (SOC), dwell time, charging power, the density and size of public charging stations, the total number of public charging, vehicle kilometer travel (VKT) of the current trip and current day are the main predictors when choosing the charging location. Findings of this study may provide new insights into the operational strategies of the public charging station as well as the deployment of public charging facilities in urban cities.

Characterizing Plug-In Hybrid Electric Vehicle Consumers Most Influenced by California’s Electric Vehicle Rebate

2017 ◽  
Vol 2628 (1) ◽  
pp. 23-31 ◽  
Author(s):  
Clair Johnson ◽  
Brett Williams
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Positive Association ◽  
Interest Level ◽  
Consumer Perspective ◽  
Hybrid Electric ◽  
The Relationship ◽  
Lower Household Income

California’s Clean Vehicle Rebate Project (CVRP) provides cash rebates to make plug-in and fuel-cell electric vehicle purchases and leases more financially attractive. Self-reported evidence provided by CVRP participants provides a unique opportunity to examine the influence of the incentive from the consumer perspective. With evidence from a voluntary survey offered to all individual CVRP participants, this inquiry used logistic regression to examine the relationship between consumer factors and the influence of CVRP on consumers’ acquisition decisions. In other words, would they have purchased their vehicle without the rebate? This initial analysis examined a set of consumers who adopted plug-in hybrid electric vehicles between fall 2012 and spring 2015 ( n = 7,345). Factors considered for inclusion encompassed transaction, household, and demographic characteristics, motivations for adopting plug-in hybrid electric vehicles, and measures of experience with plug-in electric vehicles (PEVs). Findings indicated, as expected, that several characteristics and experiences are associated with a greater likelihood that a consumer would consider the rebate essential. These characteristics and experiences include having lower household income, being younger, adopting less-expensive vehicles, being more motivated to adopt a PEV by a desire to save money, being less motivated to adopt a PEV by a desire to reduce environmental impact, and reporting a lower initial interest level in adopting a PEV. Less straightforward, but informative, results included a positive association between rebate influence and identification with a nonwhite ethnicity or as male. Additionally, the lack of significance of some predictors was unexpected; in particular, no housing characteristics were associated with the influence of the rebate.

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