Multi-objective Optimization of Plug-in Hybrid Electric Vehicle (PHEV) Powertrain Families Considering Variable Drive Cycles and User Types Over the Vehicle Lifecycle PDF Download
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Author: S. Ehtesham Al Hanif
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Plug-in Hybrid Electric vehicle (PHEV) technology has the potential to reduce operational costs, greenhouse gas (GHG) emissions, and gasoline consumption in the transportation market. However, the net benefits of using a PHEV depend critically on several aspects, such as individual travel patterns, vehicle powertrain design and battery technology. To examine these effects, a multi-objective optimization model was developed integrating vehicle physics simulations through a Matlab/Simulink model, battery durability, and Canadian driving survey data. Moreover, all the drivetrains are controlled implicitly by the ADVISOR powertrain simulation and analysis tool. The simulated model identifies Pareto optimal vehicle powertrain configurations using a multi-objective Pareto front pursuing genetic algorithm by varying combinations of powertrain components and allocation of vehicles to consumers for the least operational cost, and powertrain cost under various driving assumptions.
Author: S. Ehtesham Al Hanif
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Plug-in Hybrid Electric vehicle (PHEV) technology has the potential to reduce operational costs, greenhouse gas (GHG) emissions, and gasoline consumption in the transportation market. However, the net benefits of using a PHEV depend critically on several aspects, such as individual travel patterns, vehicle powertrain design and battery technology. To examine these effects, a multi-objective optimization model was developed integrating vehicle physics simulations through a Matlab/Simulink model, battery durability, and Canadian driving survey data. Moreover, all the drivetrains are controlled implicitly by the ADVISOR powertrain simulation and analysis tool. The simulated model identifies Pareto optimal vehicle powertrain configurations using a multi-objective Pareto front pursuing genetic algorithm by varying combinations of powertrain components and allocation of vehicles to consumers for the least operational cost, and powertrain cost under various driving assumptions.
Author: Shashi Kamal Shahi
Publisher:
ISBN:
Category : Hybrid electric vehicles
Languages : en
Pages : 294
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Book Description
Plug-in hybrid electric vehicles (PHEV), which share the characteristics of both a conventional HEV and an all-electric vehicle, rely on large storage batteries. Therefore, the characteristics and hybridization of the PHEV battery with the engine and electric motor play an important role in the design and potential adoption of PHEVs. In this research work, a multi-objective optimization approach is applied to compare the operational performance of Toyota Prius PHEV20 (PHEV for 20 miles of all electric range) based on fuel economy, operating cost, and green house gas emissions for 4480 combinations (20 batteries, 14 motors, and 16 engines). Powertrain System Analysis Toolkit software package automated with the Pareto Set Pursuing multi-objective optimization method is used for this purpose on two different drive cycles. It was found that 1) battery, motor, and engine work collectively in defining an optimal hybridization scheme; and 2) the optimal hybridization scheme varies with drive cycles.
Author: Omkar Parkar
Publisher:
ISBN:
Category :
Languages : en
Pages : 204
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Book Description
An increase in the awareness of environmental conservation is leading the automotive industry into the adaptation of alternatively fueled vehicles. Electric, Fuel-Cell as well as Hybrid-Electric vehicles focus on this research area with the aim to efficiently utilize vehicle powertrain as the first step. Energy and Power Management System control strategies play a vital role in improving the efficiency of any hybrid propulsion system. However, these control strategies are sensitive to the dynamics of the powertrain components used in the given system. A kinematic mathematical model for Plug-in Hybrid Electric Vehicle (PHEV) has been developed in this study and is further optimized by determining optimal power management strategy for minimal fuel consumption as well as NOx emissions while executing a set drive cycle. A multi-objective optimization using weighted sum formulation is needed in order to observe the trade-off between the optimized objectives. Particle Swarm Optimization (PSO) algorithm has been used in this research, to determine the trade-off curve between fuel and NOx. In performing these optimizations, the control signal consisting of engine speed and reference battery SOC trajectory for a 2-hour cycle is used as the controllable decision parameter input directly from the optimizer. Each element of the control signal was split into 50 distinct points representing the full 2 hours, giving slightly less than 2.5 minutes per point, noting that the values used in the model are interpolated between the points for each time step. With the control signal consisting of 2 distinct signals, speed, and SOC trajectory, as 50 element time-variant signals, a multidimensional problem was formulated for the optimizer. Novel approaches to balance the optimizer exploration and convergence, as well as seeding techniques are suggested to solve the optimal control problem. The optimization of each involved individual runs at 5 different weight levels with the resulting cost populations being compiled together to visually represent with the help of Pareto front development. The obtained results of simulations and optimization are presented involving performances of individual components of the PHEV powertrain as well as the optimized PMS strategy to follow for a given drive cycle. Observations of the trade-off are discussed in the case of Multi-Objective Optimizations.
Author: Maryyeh Chehresaz
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Hybrid electric vehicles (HEVs) were introduced in response to rising environmental challenges facing the automotive sector. HEVs combine the benefits of electric vehicles and conventional internal combustion engine vehicles, integrating an electrical system (a battery and an electric motor) with an engine to provide improved fuel economy and reduced emissions, while maintaining adequate driving range. By comparison with conventional HEVs, plug-in hybrid electric vehicles (PHEVs) have larger battery storage systems and can be fully charged via an external electric power source such as the electrical grid. Of the three primary PHEV architectures, power-split architectures tend to provide greater efficiencies than parallel or series systems; however, they also demonstrate more complicated dynamics. Thus, in this research project, the problem of optimizing the component sizes of a power-split PHEV was addressed in an effort to exploit the flexibility of this powertrain system and further improve the vehicle's fuel economy, using a Toyota plug-in Prius as the baseline vehicle. Autonomie software was used to develop a vehicle model, which was then applied to formulate an optimization problem for which the main objective is to minimize fuel consumption over standard driving cycles. The design variables considered were: the engine's maximum power, the number of battery cells and the electric motor's maximum power. The genetic algorithm approach was employed to solve the optimization problem for various drive cycles and an acceptable reduction in fuel consumption was achieved thorough the sizing process. The model was validated against a MapleSim model. This research project successfully delivered a framework that integrates an Autonomie PHEV model and genetic algorithm optimization and can be used to address any HEV parameter optimization problem, with any objective, constraints, design variables and optimization parameters.
Author: Qing Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 346
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Book Description
Author: Joeri Van Mierlo
Publisher: MDPI
ISBN: 3039214535
Category : Technology & Engineering
Languages : en
Pages : 230
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Book Description
Climate change, urban air quality, and dependency on crude oil are important societal challenges. In the transportation sector especially, clean and energy efficient technologies must be developed. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) have gained a growing interest in the vehicle industry. Nowadays, the commercialization of EVs and PHEVs has been possible in different applications (i.e., light duty, medium duty, and heavy duty vehicles) thanks to the advances in energy storage systems, power electronics converters (including DC/DC converters, DC/AC inverters, and battery charging systems), electric machines, and energy efficient power flow control strategies. This book is based on the Special Issue of the journal Applied Sciences on “Plug-In Hybrid Electric Vehicles (PHEVs)”. This collection of research articles includes topics such as novel propulsion systems, emerging power electronics and their control algorithms, emerging electric machines and control techniques, energy storage systems, including BMS, and efficient energy management strategies for hybrid propulsion, vehicle-to-grid (V2G), vehicle-to-home (V2H), grid-to-vehicle (G2V) technologies, and wireless power transfer (WPT) systems.
Author: Teng Liu
Publisher: Morgan & Claypool Publishers
ISBN: 1681736195
Category : Technology & Engineering
Languages : en
Pages : 99
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Book Description
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application. In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
Author: Yiming He
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Abstract: The U.S. federal government is seeking useful applications of Vehicle-Infrastructure Integration (VII) to encourage a greener and more efficient transportation system; Plug-in Hybrid Electric Vehicles (PHEVs) are considered as one of the most promising automotive technologies for such an application. In this study, the author demonstrates a strategy to improve PHEV energy efficiency via the use of VII. This dissertation, which is composed of three published peer-reviewed journal articles, demonstrates the efficacies of the PHEV-VII system as regards to both the energy use and environmental impact under different scenarios. The first article demonstrates the capabilities of and benefits achievable for a power-split drivetrain PHEV with a VII-based energy optimization strategy. With the consideration of several real-time implementation issues, the results show improvements in fuel consumption with the PHEV-VII system under various driving cycles. In the second article, a forward PHEV model with an energy management system and a cycle optimization algorithm is evaluated for energy efficiency. Prediction cycles are optimized using a cycle optimization strategy, which resulted in 56-86% fuel efficiency improvements for conventional vehicles. When combined with the PHEV power management system, about 115% energy efficiency improvements were achieved. The third article focuses on energy and emission impacts of the PHEV-VII system. At a network level, a benefit-cost analysis is conducted, which indicated that the benefits outweighed costs for PHEV and Hybrid Electric Vehicle (HEV) integrated with a VII system at the fleet penetration rate of 20% and 30%, respectively.
Author: University of California, Davis. Plug-in Hybrid Electric Vehicle Research Center
Publisher:
ISBN:
Category : Electric vehicles
Languages : en
Pages : 116
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Book Description
Author: Zhila Pirmoradi
Publisher:
ISBN:
Category :
Languages : en
Pages : 183
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Book Description
Plug-in Hybrid Electric Vehicles (PHEVs) have been recognized as a solution to mitigate the green-house emission for transportation. A factor to succeed in the marketplace is to provide products that can meet customer expectations and satisfy various functional requirements. As such, the design of PHEVs for diverse market segments requires sufficient differentiation in this product to maximize customer satisfaction with the new technology. However, there are challenges coupled with diversity in production of such a complex product for various customers. This dissertation attempts to address the challenges. This thesis proposed the use of product family design to ensure both the manufacturing efficiency and the customer satisfaction for PHEVs in various market segments. A thorough review of the developments in product family design is first performed, and directions for developing an efficient family design methodology are identified. In order to select the desired or the most preferred variants for the family design purposes, a review of the market studies and fleet data for PHEVs has been performed and summarized as well, based on which a set of five PHEVs- known as variants- are selected for family design assessments. Thirdly, a methodology is proposed for PHEV product family design to enable scale-based design of the selected PHEV variants. The proposed method is verified through a test problem from the literature, and its application to the PHEVs design provides design solutions for the PHEV product family under study. Since the vehicle performance is assessed through expensive simulations, it is shown that the selected optimization algorithm, along with the commonalization strategy and the decision criteria for commonalizing specific design variables make an efficient methodology in terms of the computational costs, and the overall performance of the obtained family solutions. The proposed methodology can also find applications in other product designs that involve expensive simulations and unknown design equations.
