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.

Parametric Vehicular Simulator in the Evaluation of HELVIS Mini-HEV EDS Control

2011 ◽  
Author(s):  
Rafael C. B. Sampaio ◽  
Gabriel S. de Lima ◽  
Vinicius V. M. Fernandes ◽  
Andre´ C. Hernandes ◽  
Marcelo Becker
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Real Time ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Differential System ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Hybrid Electric ◽  
The Universe

HELVIS (Hybrid Electric Vehicle In Low Scale) is a mini-HEV platform used on the research of HEVs (Hybrid Electric Vehicles), through which students of all degrees have the opportunity to be introduced to the universe that surrounds HEVs in many aspects. In this work the HELVIS-Sim is presented. HELVIS-Sim is a full dynamic & kinematic vehicular simulator for the HELVIS platform, consisting of a Simulink™ environment through which the states of a large number of variables related to the vehicle can be observed and analyzed. Specially in this paper, the focus is in the control of HELVIS EDS (Electronic Differential System), presenting classic, A.I.-based (Artificial Intelligence) and optimal robust controllers in the problem of the adjustment of the rear angular speeds. HELVIS-Sim results are then compared to experimental data obtained from the real HELVIS EDS, with the aid of a dSpace™ real time interface board.

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.

Evaluating the Performances of Advanced Powertrains

Volume 2 ◽  
2004 ◽  
Author(s):  
Massimo Feola ◽  
Fabrizio Martini ◽  
Stefano Ubertini
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Hybrid Vehicles ◽  
Propulsion System ◽  
Road Vehicles ◽  
Hybrid Propulsion ◽  
Hybrid Electric

Over the last few decades a tremendous effort has been made to reduce road vehicles engines contribution to air pollution and fuel consumption. Due to the more stringent limits imposed by governments, various manufactures started working in the incorporation of alternative powertrain configurations, such as pure electric vehicles (EV), hybrid electric vehicles (HEV) and fuel cell vehicles (FCV), in the automotive consumer market. In order to appreciate the advantages and disadvantages of these new vehicles over conventional vehicles a comparison must be performed in terms of efficiency and pollutant emissions. However, hybrid vehicles comprise many components with at least two different energy conversion devices (i.e. internal combustion engine and electric machine) drawing energy from at least two different energy storage devices (i.e. fuel tank and battery). In recent times, many hybrid propulsion system configurations have emerged and many others can be imagined comprising multiple reversible and irreversible energy paths. Therefore, considering that in a hybrid vehicle at least two different forms of energy (i.e. fuel chemical energy and electricity) are consumed, fuel consumption alone is no more sufficient to give a measure of the effectiveness of a hybrid propulsion system. This paper presents a first attempt to give a general mathematical form of the traction energy, the global efficiency and the specific fuel consumption of a hybrid electric vehicle that recovers as particular cases the thermal vehicle and the series hybrid electric vehicle. To evaluate the efficiency of the generic propulsion system the complete process from fuel energy and electricity to power available at the wheels is considered. The introduced concept of equivalent fuel consumption can be the basis for the comparison between road vehicles whatever the powertrain is pure thermal or hybrid. In order to get a better understanding of the mathematical analysis and its potential effectiveness some numerical simulations of hybrid vehicles virtual prototypes are performed through a suitable simulation model. The aim of the present analysis is to provide an instrument that allow a quick evaluation of the performances of hybrid electric vehicles.

scholarly journals The Future of Hybrid Electric Vehicles and Sustainable Vehicles in the UK

2021 ◽  
pp. 213-221
Author(s):  
Greg Last ◽  
David E. Agbro ◽  
Abhishek Asthana
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Medium Term ◽  
Hybrid Electric ◽  
The Government ◽  
The Uk ◽  
Phase Out

AbstractThis paper details the development of the hybrid electric vehicle (HEV) and its integration into the UK market. The aim of this research was to explore the benefits and limitations of the HEV system which there are many. Government policies and incentives; both current and future as well as HEV technologies are also summarised. The HEV is an excellent short to medium term solution for making travel more sustainable. However, in the long term, push for electric vehicles (EVs) will significantly increase from the Government in its aim to meet stringent emissions policies and there will likely be legislation to phase out HEVs that cannot be plugged in.

Driving shaft lifetime enhancement by hybrid electrification

2018 ◽  
Vol 233 (3) ◽  
pp. 572-584
Author(s):  
Volkan Sezer
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Power Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Conventional Vehicle ◽  
Management Algorithm ◽  
Hybrid Electric

As a classical definition, the main aim of hybrid electric vehicle technology is to decrease the fuel consumption and emissions with the assistance of its power management algorithm. However, hybrid electric vehicles could also be optimized for fatigue minimization of the driving shaft to enhance its lifetime. To the best of our knowledge, there are no studies on hybrid electric vehicles regarding this concept. In this study, we model a conventional vehicle, convert it into hybrid electric vehicle in simulation environment, and optimize the power management algorithm by considering its driving shaft lifetime enhancement. The optimization is done by redesigning one of the previous equivalent cost minimization strategy studies, which includes a new state of charge sustaining approach. In this work, we reformulate the solution considering the assumptive torque–cycle life curve of the driving shaft instead of fuel consumption or emissions. Longitudinal vehicle model is prepared for simulations and the performance of the new strategy is compared with the conventional vehicle under the real driving cycle data. The results demonstrate a significant enhancement potential of 26% in driving shaft’s lifetime. Finally, we show the additional electric motor’s optimum torque tracking performance under a real driving cycle using the experimental testbed.

scholarly journals An application of Hybrid Bayesian Network (HBN) in Hybrid Electric Vehicle (HEV) manufacturing

2021 ◽  
Author(s):  
Muneer Mujahed Lyati
Keyword(s):  
Bayesian Network ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Environmental Quality ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Continuous Variables ◽  
Hybrid Electric

A network is a hybrid Bayesian network if it has both discrete and continuous variables. In this research, we discuss how the hybrid Bayesian network can utilized to further understand the network from subsidies, manufacturing to the environmental quality in the context of Hybrid electric vehicles.

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