1,720,969 research outputs 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
Effect 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
Intelligent Energy Management of Plug-In Electric Vehicles with Environment and Traffic Awareness
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
Real-time energy management and sensitivity study for hybrid electric vehicles
No full textThis paper presents a real-time energy management algorithm for hybrid electrical vehicles (HEV). The proposed approach features a practical structure and manageable computation complexity for real-time implementation. It adopts a Model Predictive Control framework and utilizes the information attainable from Intelligent Transportation Systems (ITS) to establish a prediction based real-time controller structure. Simulations have been conducted with a Matlab/Simulink based vehicle model to assess the optimality of the algorithm, in comparison with existing control approaches. For real-time HEV control algorithms, ITS based driving prediction is an essential component. It is important to investigate the impact of the accuracy of ITS information on HEV energy consumption. In this work, we study the the effect of noises and errors in the velocity profile prediction under different control approaches. The sensitivity of the HEV energy use is investigated based on real driving data. The results provide better understanding of the need in driving profile prediction in real-time HEV control
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
Energy economic analysis of PV based charging station at workplace parking garage
No full textPlug-In Electric Vehicles (PEVs, whether hybrid or battery-only) are receiving a great deal of interest in the United States due to their energy efficiency, convenient and low-cost recharging capabilities and reduced use of petroleum. The impact of charging millions of PEVs from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions, and economics. Some vehicles are parked during the day at workplace parking garages and can be charged from the solar energy using photovoltaic (PV) based charging facilities. The use of solar energy for charging helps to reduce the load and emissions from the power grid but also affects the cost of charging. Therefore, an energy economic analysis of PEV charging using PV panels at workplace parking garage is presented in this paper. Two locations (Columbus, OH and Los Angeles, CA) having different levels of yearly solar radiation and finance structure are considered and hourly energy analysis and cash flow analysis is performed with different types of vehicles, statistical data for driving distances, park time, etc, and all finances including tax rebates and incentives. A rate structure that charges an extra amount for accessing the charging facility is considered to analyze the economics of garage owner and the vehicle owner. The impact of this PV based workplace charging facility on emissions from power grid is also analyzed. This emission impact is combined with economic analysis for vehicle owner by considering carbon tax. The results of different parametric studies show the feasibility of a PV based charging facility and benefits to vehicle and garage owner
A Statistical Approach to assess the Impact of Road Events on PHEV Performance using Real World Data
No full textPlug in hybrid electric vehicles (PHEVs) have gained interest over last decade due to their increased fuel economy and ability to displace some petroleum fuel with electricity from power grid. Given the complexity of this vehicle powertrain, the energy management plays a key role in providing higher fuel economy. The energy management algorithm on PHEVs performs the same task as a hybrid vehicle energy management but it has more freedom in utilizing the battery energy due to the larger battery capacity and ability to be recharged from the power grid. The state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining overall fuel consumption. The knowledge of the power requirement or velocity profile during a driving trip is necessary to achieve best fuel economy results; clearly, performance of the energy management algorithm is closely related to the amount of trip information available in the form of road profile, velocity profile, trip distance, and weather conditions. The intelligent transportation system (ITS) allows the vehicle to communicate with other vehicles and the infrastructure to collect data about environment and the expected events in the future, such as traffic density, expected turns, road grade, rain, snow and temperature. The ability to effectively interpret the weather and traffic 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 the ITS integration with energy management of PHEVs: an approach to determine correlations/transfer functions between different road events and velocity profile characteristics. This study utilizes a year round data collected from a plug-in Prius and principal component analysis, numerical clustering method to determine the correlations between events and performance. The overarching goal of this study is to identify the factors that have largest impact on the vehicle fuel economy and are most important for subsequent development of energy management strategies in optimizing fuel economy
Comparative study of different control strategies for Plug-In Hybrid Electric Vehicles
No full textPlug-In Hybrid Vehicles (PHEVs) represent the middle point between Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs), thus combining benefits of the two architectures. PHEVs can achieve very high fuel economy while preserving full functionality of hybrids - long driving range, easy refueling, lower emissions etc. These advantages come at an expense of added complexity in terms of available fuel. The PHEV battery is recharged both though regenerative braking and directly by the grid thus adding extra dimension to the control problem. Along with the minimization of the fuel consumption, the amount of electricity taken from the power grid should be also considered, therefore the electricity generation mix and price become additional parameters that should be included in the cost function.
Two control algorithms - ECMS (Equivalent Consumption Minimization Strategy) and DP (dynamic programming) - are considered in this paper to optimize the power split between electrical and mechanical energy sources. The performance obtained using dynamic programming as global optimal energy management strategy for a PHEV is used as benchmark for evaluating on-board implementable control strategy - ECMS. The ECMS is used to design two control modes - EV and Blended. The model of a PHEV version of a Chevrolet Equinox fueled by bio-diesel B20 has been developed in the Matlab/Simulink environment. A Chevrolet Equinox was hybridized at The Center of Automotive Research (CAR), at The Ohio State University as part of Challenge-X competition; the vehicle was used to validate the components of the Simulink model
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