A Pareto Trade-Off Analysis of Cost Versus Greenhouse Gas Emissions for a Model of a Mid-Sized Vehicle With Various Powertrains

2018 ◽  
Author(s):  
Karim Hamza ◽  
Kenneth P. Laberteaux ◽  
John Willard ◽  
Kang-Ching Chu
Keyword(s):  
Greenhouse Gas ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Electric Energy ◽  
Baseline Scenario ◽  
Large Set ◽  
Energy Usage ◽  
Trade Off ◽  
Design For All ◽  
Hybrid Electric

This paper presents a simulation-based analysis of a model of a mid-sized vehicle while exploring powertrains of interest. In addition to a baseline conventional vehicle (CV), the explored powertrain architectures include: hybrid electric vehicle (HEV), plugin hybrid electric vehicle (PHEV) and batterW2Wy-only electric vehicle (BEV). The modeling also considers several different all electric driving range (AER) of the PHEVs and BEVs. Fuel economy/energy-efficiency assessment is conducted by with open source software (FASTSim), and by analyzing a large set of real-world driving trips from California Household Travel Survey (CHTS-2013), which contains a record of more than 65 thousand trips with one second interval recording of the vehicle seed. Gas and/or electric energy usage from the analyzed trips are then used to generate greenhouse gas (GHG) statistical distributions (in units of gm-CO2/mile) for a modelled vehicle powertrain. Gas and/or electric energy usage are also utilized in the calculation of the running cost, and ultimately the net average cost (in units of $/mile) for the modelled powertrains. Pareto trade-off analysis (Cost vs GHG) is then conducted for four sub-population segments of CHTS vehicle samples in a baseline scenario as well as four future-looking scenarios where carbon intensity in electric power generation gets lower, gas gets more expensive and batteries get less expensive. While noting limitations of the conducted analysis, key findings suggest that: i) mix of PHEVs and BEVs with various AER that is properly matched to driver needs would be better than one single powertrain design for all drivers, and ii) electrified powertrains do not become cost-competitive in their own right (without incentives or subsidies) until some of the future battery technology goals are attained.

Design and Development Process for a Range Extended Split Parallel Hybrid Electric Vehicle

2010 ◽  
Author(s):  
Lynn R. Gantt ◽  
Patrick M. Walsh ◽  
Douglas J. Nelson
Keyword(s):  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Development Process ◽  
Energy Use ◽  
Gas Emissions ◽  
Vehicle Development ◽  
Hybrid Electric ◽  
Vehicle Development Process

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2009–2011 EcoCAR: The NeXt Challenge Advanced Vehicle Technology Competition series organized by Argonne National Lab (ANL), and sponsored by General Motors Corporation (GM) and the U.S. Department of Energy (DOE). The goal of EcoCAR is for student engineers to take a GM-donated crossover SUV and re-engineer it to reduce greenhouse gas emissions and petroleum energy use, while maintaining performance, safety and consumer appeal. Following GM’s Vehicle Development Process (VDP), HEVT established team goals that meet or exceed the competition requirements for EcoCAR in the design of a plug-in range-extended hybrid electric vehicle. HEVT is split up into three subteams to complete the competition and meet the requirements of the vehicle development process. The Mechanical subteam is tasked with modifying and refining the Year 1 component specifications and designs for packaging in the vehicle. The Electrical subteam is tasked with implementing a safe high voltage system on the vehicle including the design and development of a Lithium Iron Phosphate (LiFePO4) energy storage subsystem (ESS) donated by A123 Systems. The Controls subteam is tasked with modeling the Vehicle Technical Specifications (VTS) so that the subteams can make intelligent design decisions. The Controls subteam also used a controller Hardware-In-the-Loop (HIL) simulation setup running a real-time vehicle model against the controller hardware to test the HEVT-designed Hybrid Vehicle Supervisory Controller (HVSC). The result of this design process is an Extended-Range Electric Vehicle (E-REV) that uses grid electric energy and E85 fuel for propulsion. The vehicle design is predicted to achieve an SAE J1711 utility factor-corrected fuel consumption of 2.9 l(ge)/100 km (82 mpgge) with an estimated all-electric range of 69 km (43 miles). Using corn-based E85 fuel in North America for the 2015 timeframe and an average North American electricity mix, the well-to-wheels petroleum energy use and greenhouse gas emissions are reduced by 90% and 30% respectively when compared to the stock vehicle: a 4-cylinder, gasoline-fueled Vue XE.

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.

Improvement of ultracapacitors-energy usage in fuel cell based hybrid electric vehicle

2020 ◽  
Vol 45 (26) ◽  
pp. 13746-13756 ◽  
Author(s):  
Jesus E. Valdez-Resendiz ◽  
Julio C. Rosas-Caro ◽  
Jonathan C. Mayo-Maldonado ◽  
Abraham Claudio-Sanchez ◽  
Omar Ruiz-Martinez ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Energy Usage ◽  
Hybrid Electric

scholarly journals Multi-Objective Optimization for Plug-In 4WD Hybrid Electric Vehicle Powertrain

2019 ◽  
Vol 9 (19) ◽  
pp. 4068 ◽  
Author(s):  
Zhengwu Wang ◽  
Yang Cai ◽  
Yuping Zeng ◽  
Jie Yu
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Electric Energy ◽  
Compromise Solution ◽  
Nsga Ii ◽  
Acceleration Time ◽  
Multi Objective ◽  
Electric Energy Consumption ◽  
Original Scheme ◽  
Hybrid Electric

This paper focuses on the parameter optimization for the CVT (a continuously variable transmission) based plug-in 4WD (4-wheel drive) hybrid electric vehicle powertrain. First, the plug-in 4WD hybrid electric vehicle (plug-in 4WD HEV)’s energy management strategy based on the CD (charge depleting) and CS (charge sustain) mode is developed. Then, the multi-objective optimization’s mathematical model, which aims at minimizing the electric energy consumption under the CD stage, the fuel consumption under the CS stage and the acceleration time from 0–120 km/h, is established. Finally, the multi-objective parameter optimization problem is solved using an evolutionary based non-dominated sorting genetic algorithms-II (NSGA-II) approach. Some of the results are compared with the original scheme and the classical weight approach. Compared with the original scheme, the best compromise solution (i.e., electric energy consumption, fuel consumption and acceleration time) obtained using the NSGA-II approach are reduced by 1.21%, 6.18% and 5.49%, respectively. Compared with the weight approach, the Pareto optimal solutions obtained using NSGA-II approach are better distributed over the entire Pareto optimal front, as well as the best compromise solution is also better.

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

2010 ◽  
Vol 132 (9) ◽  
Author(s):  
Ching-Shin Norman Shiau ◽  
Nikhil Kaushal ◽  
Chris T. Hendrickson ◽  
Scott B. Peterson ◽  
Jay F. Whitacre ◽  
...  
Keyword(s):  
Life Cycle ◽  
Greenhouse Gas ◽  
Electric Vehicle ◽  
Life Cycle Cost ◽  
Hybrid Electric Vehicle ◽  
Operating Cost ◽  
Ghg Emissions ◽  
Vehicle Design ◽  
Ghg Reduction ◽  
Hybrid Electric

Plug-in hybrid electric vehicle (PHEV) technology has the potential to reduce operating cost, greenhouse gas (GHG) emissions, and petroleum consumption in 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 optimization model integrating vehicle physics simulation, battery degradation data, and U.S. driving data. The model identifies optimal vehicle designs and allocation of vehicles to drivers for minimum net life cycle cost, GHG emissions, and petroleum consumption under a range of scenarios. We compare conventional and hybrid electric vehicles (HEVs) to PHEVs with equivalent size and performance (similar to a Toyota Prius) under urban driving conditions. We find that while PHEVs with large battery packs minimize petroleum consumption, a mix of PHEVs with packs sized for ∼25–50 miles of electric travel under the average U.S. grid mix (or ∼35–60 miles under decarbonized grid scenarios) produces the greatest reduction in life cycle GHG emissions. Life cycle cost and GHG emissions are minimized using high battery swing and replacing batteries as needed, rather than designing underutilized capacity into the vehicle with corresponding production, weight, and cost implications. At 2008 average U.S. energy prices, Li-ion battery pack costs must fall below $590/kW h at a 5% discount rate or below $410/kW h at a 10% rate for PHEVs to be cost competitive with HEVs. Carbon allowance prices offer little leverage for improving cost competitiveness of PHEVs. PHEV life cycle costs must fall to within a few percent of HEVs in order to offer a cost-effective approach to GHG reduction.

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