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.

Multirate Integration in Hybrid Electric Vehicle Virtual Proving Grounds

1998 ◽  
Author(s):  
Dario Solis ◽  
Chris Schwarz
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Time Scales ◽  
Electric Vehicles ◽  
Time Scale ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Differential Algebraic Equations ◽  
Vehicle Systems ◽  
Hybrid Electric

Abstract In recent years technology development for the design of electric and hybrid-electric vehicle systems has reached a peak, due to ever increasing restrictions on fuel economy and reduced vehicle emissions. An international race among car manufacturers to bring production hybrid-electric vehicles to market has generated a great deal of interest in the scientific community. The design of these systems requires development of new simulation and optimization tools. In this paper, a description of a real-time numerical environment for Virtual Proving Grounds studies for hybrid-electric vehicles is presented. Within this environment, vehicle models are developed using a recursive multibody dynamics formulation that results in a set of Differential-Algebraic Equations (DAE), and vehicle subsystem models are created using Ordinary Differential Equations (ODE). Based on engineering knowledge of vehicle systems, two time scales are identified. The first time scale, referred to as slow time scale, contains generalized coordinates describing the mechanical vehicle system that includs the chassis, steering rack, and suspension assemblies. The second time scale, referred to as fast time scale, contains the hybrid-electric powertrain components and vehicle tires. Multirate techniques to integrate the combined set of DAE and ODE in two time scales are used to obtain computational gains that will allow solution of the system’s governing equations for state derivatives, and efficient numerical integration in real time.

Control Strategy of Hybrid Electric Vehicles Based on Driving Style Identification

2014 ◽  
Vol 945-949 ◽  
pp. 1587-1596
Author(s):  
Xian Zhi Tang ◽  
Shu Jun Yang ◽  
Huai Cheng Xia
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Entropy Theory ◽  
Driving Style ◽  
Identification Error ◽  
Identification Method ◽  
Hybrid Electric

The driving style comprehensive identification method based on the entropy theory is presented. The error and error proportion of each identification result are calculated. The entropy and the variation degree of the identification error of each identification method are calculated based on the definition of information entropy. According to the entropy and the variation degree of the identification error, the weight of each kind of identification method can be determined in the comprehensive identification method, and the driving style comprehensive identification algorithm is derived. The control strategy of hybrid electric vehicles based on the driving style identification is proposed. The economic control strategy and dynamic control strategy are established. Depending on the results of driving style identification, aiming at different kinds of drivers, the mode of control strategies can be adjusted, so the demands of different kinds of drivers can be satisfied. The hybrid electric vehicle simulation model and control strategy model are built, and the simulations have been done. Due to the simulation results, the drivers’ intention comprehensive identification method based on the entropy theory is proved to represent the driver’s driving style systematically and comprehensively, and the hybrid electric vehicle control strategy based on the driving style identification can make the vehicles satisfy different drivers’ demands.

scholarly journals Real-Time Co-optimization of Vehicle Route and Speed Using Generic Algorithm for Improved Fuel Economy

2019 ◽  
Vol 141 (03) ◽  
pp. S08-S15
Author(s):  
Guoming G. Zhu ◽  
Chengsheng Miao
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Autonomous Vehicles ◽  
Hybrid Electric Vehicle ◽  
High Efficiency ◽  
Combustion Engine ◽  
Cruise Control ◽  
Vehicle Performance ◽  
Hybrid Electric

Making future vehicles intelligent with improved fuel economy and satisfactory emissions are the main drivers for current vehicle research and development. The connected and autonomous vehicles still need years or decades to be widely used in practice. However, some advanced technologies have been developed and deployed for the conventional vehicles to improve the vehicle performance and safety, such as adaptive cruise control (ACC), automatic parking, automatic lane keeping, active safety, super cruise, and so on. On the other hand, the vehicle propulsion system technologies, such as clean and high efficiency combustion, hybrid electric vehicle (HEV), and electric vehicle, are continuously advancing to improve fuel economy with satisfactory emissions for traditional internal combustion engine powered and hybrid electric vehicles or to increase cruise range for electric vehicles.

Design and Analysis of a Series Hybrid Electric Vehicle for Indian Conditions

2012 ◽  
Author(s):  
C. S. Nanda Kumar ◽  
Shankar C. Subramanian
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Electric Motor ◽  
Hybrid Electric Vehicle ◽  
Traction Force ◽  
Drive System ◽  
Maximum Speed ◽  
Performance Requirements ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Electric

Electric and hybrid vehicles are emerging rapidly in the automotive market as alternatives to the traditional Internal Combustion Engine (ICE) driven vehicles to meet stringent emission standards, environmental and energy concerns. Recently, Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) have been introduced in many countries including India. One configuration of a HEV is the Series Hybrid Electric Vehicle (SHEV). The design and analysis of the drive system of a SHEV under Indian conditions is the focus of this paper. In conventional vehicles, the ICE is the power source that drives the vehicle. The energy from the ICE is distributed to the wheels through the transmission, which is then used to generate the traction force at the tyre-road interface. In a HEV, both the engine and the electric motor provide the energy to drive the vehicle. In a SHEV, the energy generated by the electric motor is transmitted through the transmission to meet the torque demand at the wheels. Based on the driver’s acceleration demand and the state of charge of the battery, the controller manages the ICE, the generator and the battery to supply the required energy to the motor. The motor finally develops the required drive torque to generate the traction force at the wheels to meet the vehicle drive performance requirements like gradeability, acceleration and maximum speed. The objective of this paper is to discuss the design of the drive system of a SHEV. This involves the calculation of the power specifications of the electric motor based on the vehicle drive performance requirements. The equations for performing these calculations are presented. The procedure is then demonstrated by considering a typical Indian commercial vehicle along with its typical vehicle parameter values. A simulation study has also been performed by considering the Indian drive cycle to demonstrate the energy savings obtained by the use of a SHEV.

On Usage Context of Hybrid Electric Vehicles in Choice Studies

2011 ◽  
Author(s):  
Lin He ◽  
Wei Chen ◽  
Guenter Conzelmann
Keyword(s):  
Hybrid Electric Vehicle ◽  
Choice Model ◽  
Principal Component ◽  
Choice Modeling ◽  
Multiple Sources ◽  
Hybrid Electric ◽  
Using Data ◽  
Household Travel ◽  
Usage Context ◽  
The Relationship

Considering usage context attributes in choice modeling has been shown to be important when product performance highly depends on the usage context. To build a reliable choice model, it is critical to first understand the relationship between usage context attributes and customer profile attributes, then to identify the market segmentation characterized by both sets of attributes, and finally to construct a choice model by integrating data from multiple sources. This is a complex procedure especially when a large number of customer attributes are potentially influential to the product choice. Using the hybrid electric vehicle (HEV) as an example, this paper presents a systematic procedure and the associated data analysis techniques for implementing each of the above steps. Usage context and customer profile attributes extracted from both National Household Travel Survey (NHTS) and Vehicle Quality Survey (VQS) data are first analyzed to understand the relationship between usage context attributes and customer profile attributes. Next the principal component analysis is utilized to identify the key characteristics of hybrid vehicle drivers, and to determine the market segmentations of HEV and the critical attributes to include in choice models. Before the two sets of data are combined for choice modeling, statistical analysis is used to test the compatibility of the two datasets. A pooled choice model created by incorporating usage context attributes illustrates the benefits of context-based choice modeling using data from multiple sources. Even though NHTS and VQS have been used in the literature to study transportation patterns and vehicle quality ratings, respectively, this work is the first to explore how they may be used together to benefit the study of customer preference for HEVs.

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