1,721,259 research outputs found
Energy and economic evaluation of PHEVs and their interaction with renewable energy sources and the power grid
No full textStrong dependency on crude oil in most areas of modern transportation coupled with increased demand for electric power generation lead to a significant consumption of fossil fuel resources over many decades. Homes and cars represent the biggest personal impact on the increasing energy demand, global warming and air quality; furthermore, electric power utilities spend a tremendous amount of capacity to continuously balance supply and demand across the grid or provide backup electricity during outages and peak demand periods. As a consequence, research is quickly moving towards interconnected renewable energy based systems for transportation and residential/commercial buildings. Thus, this paper deals with an energy and economic evaluation of Plug-in Hybrid Electric Vehicles (PHEVs) and their interaction with the power grid and the energy market. A multi-configurable personal eco-system with a plug-in hybrid vehicle is modeled. The model uses a set of data for the State of Ohio, including cost of energy, potential photovoltaic capacity, wind patterns and government regulations and incentives. The PHEV can draw electricity either from the power grid or from a personal eco-system consisting of a hybrid wind/photovoltaic generating system. Simulations are carried out starting from hourly local load demand, wind speed data, approximate solar radiation, energy market and state regulations. Various configurations and various available contracts for buying/selling energy from/to the grid are analyzed and compared. Results show the potential for reduction of energy cost, pollutant and dependency on the grid, along with substantial economic benefits
PEVs Market Penetration and Impact on Fuel Taxes
No full textIn this era of fossil fuels dependency and concern about Greenhouse Gas (GHG) emissions, the scientific community is putting great effort to curb and rationalize energy consumption.
The transportation sector represents the biggest final use of oil in the U.S.. Of the almost 3 Gallons a day of oil consumed by each American, 50% goes to transporting people and goods from one place to another. Moreover, oil accounts for 43% of U.S. overall GHG emissions.
Plug-in Electric Vehicles (PEVs) are nowadays seen as a concrete solution to reduce the oil consumed in the transportation sector in the 2030-2050 timeframe, since they are able to power a substantial portion of daily travels with electricity.These advance vehicles could allow displacing a large fraction of gasoline and diesel use. Technical, economical and environmental aspects related to these innovative vehicles are extensively studied by researcher throughout the world.
In particular, this paper proposes a comparative analysis of the total PEV market penetration and sample numerical simulations are proposed for a case study. Strong emphasis has been given to study the impact of these vehicles on motor fuel taxes, a topic that has not been extensively investigated in the open literature. These taxes account not only for oil externalities, but are used to maintain the U.S. surface transportation structure. Consuming less gasoline per mile travelled will lead to reduced revenue collected from gasoline taxation, meaning that other mechanisms or funds that could augment the current means for funding and financing highway and transit infrastructure have to be found
Energy management for Plug-in Hybrid Electric Vehicles using Equivalent Consumption Minimisation Strategy
No full textOne strategy to minimise petroleum fuel consumption of a Plug-in Hybrid Electric Vehicle (PHEV) is to attain the lowest admissible battery State of Charge (SOC) at the end of driving cycle while following an optimal SOC profile. The challenge of an optimisation algorithm is to find this optimal profile by using least future information about the power demand. An application of Equivalent Consumption Minimisation Strategy (ECMS) for PHEV is presented in this paper and benchmarked against the dynamic programming (DP) for information requirement and optimality. The optimality is assessed in simulation by considering petroleum fuel economy and deviation of the optimal SOC profile from a reference profile for different driving scenarios and battery sizes. Results show that for longer distances and larger battery sizes, ECMS and DP provide similar fuel economy and SOC profiles. A sensitivity analysis with respect to driving distance is presented at the end of the paper. Copyright © 2010 Inderscience Enterprises Ltd
Economic and environmental impacts of a PV powered workplace parking garage charging station
No full textEconomic and Environmental Analysis of Plug-in Hybrid Electric Vehicles Based on Common Driving Habits
No full textThe objective draw by this project is to develop tools for Plug-in Hybrid Electric Vehicle (PHEV) design, energy analysis and energy management, with the aim of analyzing the effect of design, driving cycles, charging frequency and energy management on performance, fuel economy, range and battery life.
A Chevrolet Equinox fueled by bio diesel B20 has been hybridized at the Center for Automotive Research (CAR), at The Ohio State University. The vehicle model has been developed in Matlab/Simulink environment, and validated based on laboratory and test. The PHEV battery pack has been modeled starting from Li-Ion batteries experimental data and then implemented into the simulator.
In order to simulate “real world” scenarios, custom driving cycles/typical days were identified starting from average driving statistics and well-known cycles. The driving cycles are based on commonly accepted standardized cycles (FUDS, FHDS, etc) and then combined to reflect common driving habits, average commute time, thus resulting in an annual distance traveled of about 15.000 miles. Several scenarios have been drawn considering different vehicle operation modes, i.e. EV (electric vehicle) and blended, different battery sizes, 6.97 and 8.87 kWh of stored energy and different charging availability, i.e. controlled (once a day, overnight) and uncontrolled (charging is possible whenever the vehicle is parked). For a complete assessment of the benefits achievable by PHEVs, results are compared to two other vehicle architectures (equivalent in terms of available power): the hybrid version of the proposed model and the conventional ICE vehicle (stock vehicle converted into B20). Results show significant benefits of PHEVs in terms of petroleum reduction, overall fuel cost and CO2 emissions; it is also clear that none of the proposed configurations (i.e. different battery sizes) and vehicle operation modes (i.e. EV or blended) represents an absolute optimum, but the analysis strongly depends on the chosen objective function to minimize, vehicle components sizing and adapted strategies, fuel and electricity cost, charging availability and power grid generation mix
Optimality Assessment of Equivalent Consumption Minimization Strategy for PHEV Applications
No full textThis paper deals with optimization algorithms for energy management of Plug-in Hybrid Electric Vehicles (PHEVs). In order to maximize fuel economy of a PHEV, the battery should attain its lowest admissible state of charge at the end of the driving cycle by following an optimal State of Charge (SOC) profile. Finding this optimal profile is a challenging optimization problem and requires prior knowledge of the entire driving cycle. There are many different optimization methods that can be applied to the energy management of PHEVs and they are usually classified into two main categories according to the optimality of their solutions. In general, in order to obtain the global optimum, the complete knowledge of future driving conditions is needed. This requirement renders unfeasible the on-line implementation of such strategies. On the other hand, simpler algorithms which are on-board implementable, do not provide the optimal solution. In this paper, a global optimal strategy — Dynamic Programming, is considered as a benchmark for evaluating the performance of an onboard implementable strategy — Equivalent Consumption Minimization Strategy with linearly decreasing reference SOC'. The study is conducted on an energy-based model of a parallel hybrid powertrain developed in Matlab/Simulink environment. The model and each powertrain components are validated based on road tests and laboratory data for a Chevrolet Equinox (hybridized at The Ohio State University Center for Automotive Research). The optimality assessment considers two main metrics, namely fuel economy and deviations from the optimal SOC profile. Simulations are carried out by considering different driving scenarios and battery sizes. Results show that for longer distances and bigger batteries, Equivalent Consumption Minimization Strategy and Dynamic Programming provide similar fuel economy and SOC profiles
Virtual PHEV fleet study based on Monte Carlo simulation
No full textThe characteristics of electric power generation, transmission and distribution in the USA are such that experts have clearly identified local distribution as the most likely component of the chain to be adversely affected by unregulated Plug-in Hybrid Electric Vehicle (PHEV) charging. This paper presents a study performed to find the impact of PHEV charging on the local distribution transformer. Monte Carlo based mass simulation of highly clustered PHEVs was conducted based on statistical representations of some key factors like vehicle characteristics and vehicle usage patterns. The presented methodology may assist in determining the most suitable local/regional charging strategies for PHEVs
Intelligent Energy Management of Plug-In Electric Vehicles with Environment and Traffic Awareness
No full textEffect of Traffic, Road and Weather Information on PHEV Energy Management
No full textEnergy management plays a key role in achieving higher fuel economy for plug-in hybrid electric vehicle (PHEV) technology; the state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining the fuel consumed. The energy management algorithm should be designed to meet all driving scenarios while achieving the best possible fuel economy. The knowledge of the power requirement during a driving trip is necessary to achieve the best fuel economy results; performance of the energy management algorithm is closely related to the amount of information available in the form of road grade, velocity profiles, trip distance, weather characteristics and other exogenous factors. Intelligent transportation systems (ITS) allow vehicles to communicate with one another and the infrastructure to collect data about surrounding, and forecast the expected events, e.g., traffic condition, turns, road grade, and weather forecast. The ability to effectively interpret this traffic and weather data to estimate the power demand is important for the energy management and plays crucial role in the battery utilization.
This paper presents an important step towards ITS integration with energy management of PHEVs: the goal of this research is to determine the correlation (or heuristic relationship) between different road events, weather conditions and PHEV energy management performance. The first step of this study utilizes real world data collected from a plug-in Toyota Prius (after-market conversion kit Hymotion L5) to determine the correlations between events and velocity profile characteristics. The second step finds the impact of power profile characteristics on the performance of equivalent consumption minimization Strategy (ECMS) for PHEV energy management using a high fidelity, validated PHEV simulator. The goal of this study is to identify the impact factors and define qualitative impact on the energy management algorithm and vehicle fuel economy
Effects of different PHEV control strategies on vehicle performance
No full textForeign oil dependence, increased cost of fuel, pollution, global warming are buzz words of today's era. Automobiles have a large impact on increasing energy demand, pollution and related issues. As a consequence, many efforts are being concentrated on innovative systems for transportation that could replace petroleum with cleaner fuel, i.e. electricity from the power grid. The use of plug-in hybrid electric vehicles (PHEVs) can become a very important change in this direction, since such vehicles could benefit from the increasing availability of renewable energy. PHEVs requires new control and energy management algorithms, that are crucial for vehicle performance. This paper deals with evaluation of two modes, Electric Vehicle (EV) mode and Blended mode, for plug-in hybrid electric vehicles and their comparison with conventional and hybrid electric vehicle performance. In this paper two PHEV architectures are considered: through road parallel plug-in hybrid and series plug-in hybrid. Similar models have been developed to evaluate vehicle performance for conventional and hybrid architectures. Both PHEV architectures are analyzed with two different modes- EV and Blended; a modified version of ECMS (Equivalent Consumption Minimization Strategy) is used for both algorithms. Various standard as well as custom designed driving cycles are used in this analysis. The paper provides quantitative analysis of the control algorithms to analyze their effects on fuel economy, use of electric energy, cost of operation, etc.; these results are compared with the simulations for hybrid and conventional vehicles. Some important relationships between fuel economy, design architectures and control strategies are shown and can be useful in the design of the optimal control algorithms for PHEVs. As shown in the results, the control problem for PHEVs is not limited to fuel economy but it also involves external factors, such as price of electricity, energy market and regulations, charging - availability, battery life issues, etc
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