Fuzzy Nonlinear Programming Based Life Cycle Cost Optimization for Hybrid Electric Vehicles

2012 ◽  
Author(s):  
Chang-Pin Lin ◽  
I-Hsiang Chan
Keyword(s):  
Life Cycle ◽  
Nonlinear Programming ◽  
Electric Vehicles ◽  
Life Cycle Cost ◽  
Hybrid Electric Vehicles ◽  
Cost Optimization ◽  
Hybrid Electric ◽  
Fuzzy Nonlinear Programming

Life-cycle private costs of hybrid electric vehicles in the current Chinese market

Energy Policy ◽  
2013 ◽  
Vol 55 ◽  
pp. 501-510 ◽  
Author(s):  
Chengtao Lin ◽  
Tian Wu ◽  
Xunmin Ou ◽  
Qian Zhang ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Life Cycle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Chinese Market ◽  
Hybrid Electric

Developments in Light Vehicle Life Cycle Analysis With Application to Electric Vehicles

2011 ◽  
Author(s):  
Peter S. Curtiss ◽  
Jan F. Kreider
Keyword(s):  
Life Cycle ◽  
Data Base ◽  
Electric Vehicles ◽  
Energy Use ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Web Based ◽  
Mercury Emissions ◽  
Hybrid Electric ◽  
Total Life

An LCA tool first reported on at the ASME ES conference in 2007 has been expanded and improved as follows: • More than 400 production vehicles from all over the world are now in the data base. • Conventional and renewable liquid and gas fuels are included. • Electric vehicles (EVs) and plug in hybrid electric vehicles (PHEVs) are included along with hybrid electric vehicles (HEVs) and conventional internal combustion engine vehicles. • The tool is now web-based. The LCA tool includes both fuel and vehicle life cycle coefficients in its data base. To illustrate the LCA ranking of vehicles using electricity (EVs, PHEVs, and HEVs) vs. conventional vehicles this paper will report on greenhouse gas emissions, total life cycle energy use along with NOx, SOx and mercury emissions. It will be shown, for example, that EVs are not the cleanest solution contrary to claims of various commentators in the popular press and of EV enthusiasts who do not take the entire life cycle into account.

scholarly journals Identification of the Optimal Passenger Car Vehicle Fleet Transition for Mitigating the Cumulative Life-Cycle Greenhouse Gas Emissions until 2050

Vehicles ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 75-99
Author(s):  
Benjamin Blat Belmonte ◽  
Arved Esser ◽  
Steffi Weyand ◽  
Georg Franke ◽  
Liselotte Schebek ◽  
...  
Keyword(s):  
Life Cycle ◽  
Greenhouse Gas ◽  
Long Range ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Ghg Emissions ◽  
Energy Sector ◽  
Passenger Car ◽  
Vehicle Fleet ◽  
Hybrid Electric

We present an optimization model for the passenger car vehicle fleet transition—the time-dependent fleet composition—in Germany until 2050. The goal was to minimize the cumulative greenhouse gas (GHG) emissions of the vehicle fleet taking into account life-cycle assessment (LCA) data. LCAs provide information on the global warming potential (GWP) of different powertrain concepts. Meta-analyses of batteries, of different fuel types, and of the German energy sector are conducted to support the model. Furthermore, a sensitivity-analysis is performed on four key influence parameters: the battery production emissions trend, the German energy sector trend, the hydrogen production path trend, and the mobility sector trend. Overall, we draw the conclusion that—in any scenario—future vehicles should have a plug-in option, allowing their usage as fully or partly electrical vehicles. For short distance trips, battery electric vehicles (BEVs) with a small battery size are the most reasonable choice throughout the transition. Plug-in hybrid electric vehicles (PHEVs) powered by compressed natural gas (CNG) emerge as promising long-range capable solution. Starting in 2040, long-range capable BEVs and fuel cell plug-in hybrid electric vehicles (FCPHEVs) have similar life-cycle emissions as PHEV-CNG.

scholarly journals Should India move towards vehicle electrification? Assessing life-cycle greenhouse gas and criteria air pollutant emissions of alternative and conventional fuel vehicles in India.

2021 ◽  
Author(s):  
Tapas Peshin ◽  
Shayak Sengupta ◽  
Inês Azevedo
Keyword(s):  
Life Cycle ◽  
Greenhouse Gases ◽  
Greenhouse Gas ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Indian States ◽  
Vehicle Class ◽  
Hybrid Electric

India is the third largest contributor of greenhouse gases and its transportation emissions account for nearly one-fifth of all greenhouse gas (GHG) emissions. Furthermore, the transportation sector accounts a significant part of other air pollutant emissions that have damaging consequences to human health. Up until now, it was unclear what the greenhouse gas and air pollutant emissions consequences of electrifying vehicles in India would be, as replacing traditional vehicles with electrified ones reduces tailpipe emissions, but it will increase the emissions from the power sector when vehicles are charging. We mitigate that gap in the literature by performing a state specific life-cycle assessment of GHGs and criteria air pollutant emissions for representative passenger vehicles (four-wheelers, three-wheelers, two-wheelers and buses) driven in Indian states/union territories. We consider several vehicle technologies (internal combustion engine (ICE) vehicles, battery electric vehicles (BEVs), hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs)). We find that in most states, four-wheeler BEVs have higher greenhouse gases and criteria air pollutant emissions than other conventional or alternative vehicles and thus electrification of that vehicle class would not lead to emissions reductions. In contrast, in most states, electrified buses and three-wheelers are the best strategy to reduce greenhouse gases, but these are also the worst solution in terms of criteria air pollutant emissions. Electrified two-wheelers have lower criteria air pollutant emissions than gasoline only in five states. The striking conclusion is that unless the Indian grid becomes less polluting, the case for widespread electrification of vehicles for sustainability purposes is simply not there. Moving towards a sustainable, low carbon and low pollution electricity grid is a requirement to make a widespread transportation electrification case for India.

A MINLP Model for Global Optimization of Plug-In Hybrid Vehicle Design and Allocation to Minimize Life Cycle Greenhouse Gas Emissions

2010 ◽  
Author(s):  
Ching-Shin Norman Shiau ◽  
Jeremy J. Michalek
Keyword(s):  
Life Cycle ◽  
Greenhouse Gas ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Ghg Emissions ◽  
Global Solutions ◽  
Mixed Integer ◽  
Vehicle Design ◽  
Light Duty Vehicle ◽  
Hybrid Electric

Plug-in hybrid electric vehicles (PHEVs) have potential to reduce greenhouse gas (GHG) emissions in the U.S. light-duty vehicle fleet. GHG emissions from PHEVs and other vehicles depend on both vehicle design and driver behavior. We pose a twice-differentiable, factorable mixed-integer nonlinear programming model utilizing vehicle physics simulation, battery degradation data, and U.S. driving data to determine optimal vehicle design and allocation for minimizing lifecycle greenhouse gas (GHG) emissions. The resulting nonconvex optimization problem is solved using a convexification-based branch-and-reduce algorithm, which achieves global solutions. In contrast, a randomized multistart approach with local search algorithms finds global solutions in 59% of trials for the two-vehicle case and 18% of trials for the three-vehicle case. Results indicate that minimum GHG emissions is achieved with a mix of PHEVs sized for around 35 miles of electric travel. Larger battery packs allow longer travel on electric power, but additional battery production and weight result in higher GHG emissions, unless significant grid decarbonization is achieved. PHEVs offer a nearly 50% reduction in life cycle GHG emissions relative to equivalent conventional vehicles and about 5% improvement over ordinary hybrid electric vehicles. Optimal allocation of different vehicles to different drivers turns out to be of second order importance for minimizing net life cycle GHGs.

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