scholarly journals Plug-In Hybrid Vehicle Simulation: How Battery Weight and Charging Patterns Impact Cost, Fuel Consumption, and CO2 Emissions

2008 ◽  
Author(s):  
Richard Hauffe ◽  
Constantine Samaras ◽  
Jeremy J. Michalek
Keyword(s):  
Fuel Consumption ◽  
Hybrid Electric Vehicle ◽  
Ghg Emissions ◽  
Simulation Models ◽  
Vehicle Performance ◽  
Vehicle Simulation ◽  
Transportation Sector ◽  
Battery Capacity ◽  
Impact Cost ◽  
Small Capacity

Plug-in hybrid electric vehicle (PHEV) technology is receiving attention as an approach to reducing U.S. dependency on foreign oil and emissions of greenhouse gases (GHG) from the transportation sector. Because plug-in vehicles require large batteries for energy storage, battery weight can have a significant impact on vehicle performance: Additional storage capacity increases the range that a PHEV can travel on electricity from the grid; however, the associated increased weight causes reduced efficiency in transforming electricity and gasoline into miles driven. We examine vehicle simulation models for PHEVs and identify trends in fuel consumption, operating costs, and GHG emissions as battery capacity is increased. We find that PHEVs with large battery capacity consume less gasoline than small capacity PHEVs when charged every 200 miles or less. When charged frequently, small capacity PHEVs are less expensive to operate and release fewer GHGs, but medium and large capacity PHEVs are more efficient for drivers that charge every 25–100 miles. While statistics on average commute length suggest that frequent charges are possible, answering the question of which PHEV designs will best help to achieve national goals will require a realistic understanding of likely consumer driving and charging behavior as well as future trends in electricity generation.

Vehicle-Terrain Interaction Simulation With Parallelized Multiscale Moving Soil Patch Model

2019 ◽  
Author(s):  
Hiroki Yamashita ◽  
Guanchu Chen ◽  
Yeefeng Ruan ◽  
Paramsothy Jayakumar ◽  
Hiroyuki Sugiyama
Keyword(s):  
Interaction Model ◽  
Simulation Models ◽  
Vehicle Performance ◽  
Vehicle Simulation ◽  
Full Vehicle ◽  
Simulation Techniques ◽  
Patch Technique ◽  
Dynamics Models ◽  
Hierarchical Multiscale ◽  
Soil Patch

Abstract Although many physics-based off-road mobility simulation models are proposed and utilized for vehicle performance evaluation as well as for understanding of tire-soil interaction problems, full vehicle simulation on deformable terrain requires addressing the computational complexity associated with the large dimensional physics-based terrain dynamics models for practical use. This paper, therefore, presents a hierarchical multiscale tire-soil interaction model that is fully integrated into parallelized off-road mobility simulation framework. In particular, a co-simulation procedure is developed for full vehicle simulation with multiscale terrain dynamics models by exploiting the moving soil patch technique. To this end, a detailed off-road vehicle simulation model is divided into five subsystems: a multibody vehicle subsystem and four tire-soil subsystems composed of nonlinear FE tires and multiscale moving soil patches. The tire-soil subsystems are interfaced with the vehicle subsystem by MPI through force-displacement coupling. It is demonstrated that the proposed framework allows for alleviating computational intensity of a full vehicle simulation that involves complex hierarchical multiscale terrain dynamics models by effectively distributing computational loads with co-simulation techniques.

Optimal Plug-In Hybrid Vehicle Design and Allocation for Minimum Life Cycle Cost, Petroleum Consumption and Greenhouse Gas Emissions

2010 ◽  
Author(s):  
Ching-Shin Norman Shiau ◽  
Scott B. Peterson ◽  
Jeremy J. Michalek
Keyword(s):  
Life Cycle ◽  
Greenhouse Gas ◽  
Life Cycle Cost ◽  
Hybrid Electric Vehicle ◽  
Operating Cost ◽  
Ghg Emissions ◽  
The United States ◽  
Vehicle Design ◽  
Battery Capacity ◽  
Hybrid Electric

Plug-in hybrid electric vehicle (PHEV) technology has the potential to help address economic, environmental, and national security concerns in the United States by reducing operating cost, greenhouse gas (GHG) emissions and petroleum consumption from the transportation sector. However, the net effects of PHEVs depend critically on vehicle design, battery technology, and charging frequency. To examine these implications, we develop an integrated optimization model utilizing vehicle physics simulation, battery degradation data, and U.S. driving data to determine optimal vehicle design and allocation of vehicles to drivers for minimum life cycle cost, GHG emissions, and petroleum consumption. We find that, while PHEVs with large battery capacity minimize petroleum consumption, a mix of PHEVs sized for 25–40 miles of electric travel produces the greatest reduction in lifecycle GHG emissions. At today’s average US energy prices, battery pack cost must fall below $460/kWh (below $300/kWh for a 10% discount rate) for PHEVs to be cost competitive with ordinary hybrid electric vehicles (HEVs). Carbon allowance prices have marginal impact on optimal design or allocation of PHEVs even at $100/tonne. We find that the maximum battery swing should be utilized to achieve minimum life cycle cost, GHGs, and petroleum consumption. Increased swing enables greater all-electric range (AER) to be achieved with smaller battery packs, improving cost competitiveness of PHEVs. Hence, existing policies that subsidize battery cost for PHEVs would likely be better tied to AER, rather than total battery capacity.

scholarly journals Multiobjective optimisation of a series hybrid electric vehicle using DIRECT algorithm

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Abdelmoula Rihab ◽  
◽  
Ben Hadj Naourez ◽  
Chaieb Mohamed ◽  
Neji Rafik ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Vehicle Performance ◽  
Performance Requirements ◽  
Toxic Emissions ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Electric ◽  
Fuel Consumption And Emissions

With the economic development, transportation in the city becomes more crowded. Furthermore, fuel consumption is causing a serious problem of pollution in the urban environment. Hybrid electric vehicles are considered as a good solution compared to conventional internal combustion engine vehicles. In order to solve those problems, the components parameters of a series hybrid electric vehicle are selected and tested with the ADvanced VehIcle SimulatOR (ADVISOR) simulation tool, which is a software-based on Matlab_simulink. Then, an optimisation was done to minimise simultaneous fuel consumption and emissions (HC, CO, and NOx) of the vehicle engine. In addition, the driving performance requirements are also examined during the urban dynamometer driving schedule (UDDS) to fix their optimal control parameters. Finally, the results show that those steps help reduce fuel consumption and emissions while guaranteeing vehicle performance. Hence, the series hybrid electric vehicle greatly improves fuel economy and reduces toxic emissions.

Potential Contribution of Deflection-Induced Fuel Consumption to U.S. Greenhouse Gas Emissions

2020 ◽  
Vol 2674 (8) ◽  
pp. 931-937 ◽  
Author(s):  
Hessam AzariJafari ◽  
Jeremy Gregory ◽  
Randolph Kirchain
Keyword(s):  
Elastic Modulus ◽  
Greenhouse Gas ◽  
Fuel Consumption ◽  
Modulus Of Elasticity ◽  
Ghg Emissions ◽  
Potential Contribution ◽  
Ghg Emission ◽  
Surface Layers ◽  
Transportation Sector ◽  
Business As Usual

Various methods have been proposed to reduce greenhouse gas (GHG) emissions associated with transportation. We investigate the potential of increasing the elastic modulus of pavement surface layers across the entire U.S. pavement network as a means of lowering vehicle excess fuel consumption (EFC) resulting from deflection-induced pavement–vehicle interaction. We show that in a business-as-usual case deflection-induced EFC represents up to 2660 million metric tons (Mt) over a 50-year analysis period. Elastic modulus increases can be accomplished using several currently implementable methods. The analysis shows that increasing the modulus of elasticity using 10% resurfacing in the network per year leads to an 18% reduction of GHG emissions from the pavement network, or 440 Mt CO2eq, over a 50-year analysis period. This would potentially offset 0.5% of the future GHG emission of the whole transportation sector.

Analyzing Drive Cycles for Hybrid Electric Vehicle Simulation and Optimization

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Benjamin M. Geller ◽  
Thomas H. Bradley
Keyword(s):  
Hybrid Electric Vehicle ◽  
Energy Use ◽  
Computational Cost ◽  
Vehicle Design ◽  
Optimized Design ◽  
Drive Cycle ◽  
Vehicle Performance ◽  
Design And Optimization ◽  
Simulation And Optimization ◽  
Vehicle Simulation

System design tools including simulation and component optimization are an increasingly important component of the vehicle design process, placing more emphasis on early stages of design to reduce redesign and enable more robust design. This study focuses on the energy use and power management simulations used in vehicle design and optimization. Vehicle performance is most often evaluated in simulation, physical testing, and certification using drive cycle cases (also known as dynamometer schedules or drive schedules). In vehicle optimization studies, the information included in each drive cycle has been shown to influence the attributes of the optimized vehicle, and including more drive cycles in simulation optimizations has been shown to improve the robustness of the optimized design. This paper aims to quantitatively understand the effect of drive cycles on optimization in vehicle design and to specify drive cycles that can lead to robust vehicle design with minimal simulation. Two investigations are performed in service of this objective; investigation 1 tests how different combinations of drive cycles affect optimized vehicle performance and design variables (DV); investigation 2 evaluates the use of stochastic drive cycles for improving the robustness of vehicle designs without adding computational cost to the design and optimization process.

Development of a software platform for a plug-in hybrid electric vehicle simulator

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Athanasios Karlis ◽  
Eric Bibeau ◽  
Paul Zanetel ◽  
Zelon Lye
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Alternative Energy ◽  
Hybrid Electric Vehicle ◽  
Energy Mix ◽  
Electricity Use ◽  
Transportation Sector ◽  
Battery Capacity ◽  
Vehicle Simulator ◽  
Hybrid Electric

AbstractElectricity use for transportation has had limited applications because of battery storage range issues, although many recent successful demonstrations of electric vehicles have been achieved. Renewable biofuels such as biodiesel and bioethanol also contribute only a small percentage of the overall energy mix for mobility. Recent advances in hybrid technologies have significantly increased vehicle efficiencies. More importantly, hybridization now allows a significant reduction in battery capacity requirements compared to pure electric vehicles, allowing electricity to be used in the overall energy mix in the transportation sector. This paper presents an effort made to develop a Plug-in Hybrid Electric Vehicle (PHEV) platform that can act as a comprehensive alternative energy vehicle simulator. Its goal is to help in solving the pressing needs of the transportation sector, both in terms of contributing data to aid policy decisions for reducing fossil fuel use, and to support research in this important area. The Simulator will allow analysing different vehicle configurations, and control strategies with regards to renewable and non-renewable fuel and electricity sources. The simulation platform models the fundamental aspects of PHEV components, that is, process control, heat transfer, chemical reactions, thermodynamics and fluid properties. The outcomes of the Simulator are: (i) determining the optimal combination of fuels and grid electricity use, (ii) performing greenhouse gas calculations based on emerging protocols being developed, and (iii) optimizing the efficient and proper use of renewable energy sources in a carbon constrained world.

Optimal Plug-in Hybrid Electric Vehicle Design and Allocation for Diverse Charging Patterns

2009 ◽  
Author(s):  
Nikhil Kaushal ◽  
Ching-Shin Norman Shiau ◽  
Jeremy J. Michalek
Keyword(s):  
Greenhouse Gas ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Optimal Allocation ◽  
Operating Cost ◽  
Ghg Emissions ◽  
The United States ◽  
Large Capacity ◽  
Hybrid Electric

Plug-in hybrid electric vehicle (PHEVs) technology has the potential to address economic, environmental, and national security concerns in the United States by reducing operating cost, greenhouse gas (GHG) emissions and petroleum consumption. However, the net implications of PHEVs depend critically on the distances they are driven between charges: Urban drivers with short commutes who can charge frequently may benefit economically from PHEVs while also reducing fuel consumption and GHG emissions, but drivers who cannot charge frequently are unlikely to make up the cost of large PHEV battery packs with future fuel cost savings. We construct an optimization model to determine the optimal PHEV design and optimal allocation of PHEVs, hybrid-electric vehicles (HEVs) and conventional vehicles (CVs) to drivers in order to minimize net cost, fuel consumption, and GHG emissions. We use data from the 2001 National Household Transportation Survey to estimate the distribution of distance driven per day across vehicles. We find that (1) minimum fuel consumption is achieved by assigning large capacity PHEVs to all drivers; (2) minimum cost is achieved by assigning small capacity PHEVs to all drivers; and (3) minimum greenhouse gas emissions is achieved by assigning medium-capacity PHEVs to drivers who can charge frequently and large-capacity PHEVs to drivers who charge less frequently.

scholarly journals Characteristics of Battery SOC According to Drive Output and Battery Capacity of Parallel Hybrid Electric Vehicle

2020 ◽  
Vol 10 (8) ◽  
pp. 2833 ◽  
Author(s):  
Insu Cho ◽  
Jinwook Lee
Keyword(s):  
Numerical Analysis ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Fuel Efficiency ◽  
Lithium Ion ◽  
Electrical Energy ◽  
Simulation Code ◽  
Vehicle Simulation ◽  
Battery Capacity ◽  
Hybrid Electric

To mitigate global warming caused by vehicles, emission regulations have been implemented for all automobiles. Hybrid electric vehicles (HEVs) are being designed to meet consumer demand for eco-friendly vehicles that offer increased power and improved fuel efficiency. HEVs are powered by an internal combustion engine (ICE) in combination with one or more electric motors that use electrical energy stored in a secondary battery, which is typically a lithium-based battery. With the use of such a hybrid drivetrain system, the fuel efficiency can be improved over that of conventional ICE vehicles. In this study, we conducted a vehicle-driving experiment to evaluate a transmission-mounted electric device (TMED) type parallel HEV using a chassis dynamometer and on-board diagnostics (OBD) signal-measuring equipment. In addition, we performed a numerical analysis using the CRUISE vehicle simulation code with experimental data. In our analysis, the engine output, which affects the torque of the drive motor, and the capacity (energy density) of the lithium-ion polymer battery were set as variables that affect the fuel-economy performance. As a result of this numerical analysis, a hybrid power-drivetrain model based on CRUISE was developed, and the current balance was evaluated according to the change in the battery capacity. We found that the battery state of charge (SOC) dropped because of a decrease in battery capacity. Thus, we predicted that the lithium-ion battery capacity would be reduced.

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