Author: Sachin Kumar Porandla
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages :
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Book Description
A Hybrid Electric Vehicle (HEV) is a complex electro-mechanical-chemical system that involves two or more energy sources. The inherent advantages of HEVs are their increased fuel economy, reduced harmful emissions and better vehicle performance. The extent of improvement in fuel economy and vehicle performance greatly depends on selecting optimal component sizes. The complex interaction between the various components makes it difficult to size specific components manually or analytically. So, simulation-based multi-variable design optimization is a possible solution for such kind of system level design problems. The multi-modal, noisy and discontinuous nature of the Hybrid Vehicle design requires the use of derivative-free global algorithms because the derivative-based local algorithms work poorly with such design problems. In this thesis, a Hybrid Vehicle is optimized using various Global Algorithms -- DIviding RECTangles (DIRECT), Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The objective of this study is to increase the overall fuel economy on a composite of city and highway driving cycle and to improve the vehicle performance. The performance of each algorithm is compared on a six variable hybrid electric vehicle design problem. Powertrain System Analysis Tool (PSAT), a state-of-the-art powertrain simulator, developed in MATLAB/Simulink environment by Argonne National Laboratory is used as the vehicle simulator. Further, a Hybrid algorithm that is a combination of global and local algorithm is developed to improve the convergence of the global algorithms. The hybrid algorithm is tested on two simple mathematical functions to check its efficiency.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
A Hybrid Electric Vehicle (HEV) is a complex electro-mechanical-chemical system that involves two or more energy sources. The inherent advantages of HEVs are their increased fuel economy, reduced harmful emissions and better vehicle performance. The extent of improvement in fuel economy and vehicle performance greatly depends on selecting optimal component sizes. The complex interaction between the various components makes it difficult to size specific components manually or analytically. So, simulation-based multi-variable design optimization is a possible solution for such kind of system level design problems. The multi-modal, noisy and discontinuous nature of the Hybrid Vehicle design requires the use of derivative-free global algorithms because the derivative-based local algorithms work poorly with such design problems. In this thesis, a Hybrid Vehicle is optimized using various Global Algorithms? DIviding RECTangles (DIRECT), Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The objective of this study is to increase the overall fuel economy on a composite of city and highway driving cycle and to improve the vehicle performance. The performance of each algorithm is compared on a six variable hybrid electric vehicle design problem. Powertrain System Analysis Tool (PSAT), a state-of-the-art powertrain simulator, developed in MATLAB/Simulink environment by Argonne National Laboratory is used as the vehicle simulator. Further, a Hybrid algorithm that is a combination of global and local algorithm is developed to improve the convergence of the global algorithms. The hybrid algorithm is tested on two simple mathematical functions to check its efficiency.
Author: Chirag Desai
Publisher:
ISBN:
Category :
Languages : en
Pages : 100
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Book Description
Advanced propulsion technologies such as hybrid electric vehicles (HEVs) have demonstrated improved fuel economy with lower emissions compared to conventional vehicles. Superior HEV performance in terms of higher fuel economy and lower emissions, with satisfaction of driving performance, necessitates a careful balance of drivetrain component design as well as control strategy parameter monitoring and tuning. In this thesis, an evolutionary global optimization-based derivative-free, multi-objective genetic algorithm (MOGA) is proposed, to optimize the component sizing of a NOVA® parallel hybrid electric transit bus drivetrain. In addition, the proposed technique has been extended to the design of an optimal supervisory control strategy for effective on-board energy management. The proposed technique helps find practical trade off-solutions for the objectives. Simulation test results depict the tremendous potential of the proposed optimization technique in terms of improved fuel economy and lower emissions (nitrous-oxide, NOx, carbon monoxide, CO, and hydrocarbons, HC). The tests were conducted under varying drive cycles and control strategies.
Author: Chris Mi
Publisher: John Wiley & Sons
ISBN: 1119970113
Category : Technology & Engineering
Languages : en
Pages : 449
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Book Description
Modern Hybrid Electric Vehicles provides vital guidance to help a new generation of engineers master the principles of and further advance hybrid vehicle technology. The authors address purely electric, hybrid electric, plug-in hybrid electric, hybrid hydraulic, fuel cell, and off-road hybrid vehicle systems. They focus on the power and propulsion systems for these vehicles, including issues related to power and energy management. They concentrate on material that is not readily available in other hybrid electric vehicle (HEV) books such as design examples for hybrid vehicles, and cover new developments in the field including electronic CVT, plug-in hybrid, and new power converters and controls. Covers hybrid vs. pure electric, HEV system architecture (including plug-in and hydraulic), off-road and other industrial utility vehicles, non-ground-vehicle applications like ships, locomotives, aircrafts, system reliability, EMC, storage technologies, vehicular power and energy management, diagnostics and prognostics, and electromechanical vibration issues. Contains core fundamentals and principles of modern hybrid vehicles at component level and system level. Provides graduate students and field engineers with a text suitable for classroom teaching or self-study.
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: Roger C. Shulze
Publisher:
ISBN:
Category : Automobiles
Languages : en
Pages : 488
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Book Description
Author: M. Ehsani
Publisher:
ISBN:
Category : Electric automobiles
Languages : en
Pages : 212
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Book Description
Electrically Peaking Hybrid (ELPH) is a parallel hybrid electric vehicle propulsion concept that was invented at Texas A & M University, by the advanced vehicle systems research group. Over the past six years, design methodologies, component development, and system optimization work has been going on for this invention. This project was a first attempt in integrating the above developments into an optimized design of an ELPH passenger car. Design specifications were chosen for a full size passenger car, performing as well as any conventional car, over the EPA-FTP-75 combined city/highway drive cycles. The results of this design project were two propulsion systems. Both were appropriate for commercial production, from the point of view of cost, availability of the technologies, and components. One utilized regenerative braking and the other did not. Substantial fuel savings and emissions reductions resulted from simulating these designs on the FTP-75 drive cycle. For example, our ELPH full size car, with regenerative braking, was capable of delivering over 50 miles per gallon in city driving, with corresponding reductions in its emissions. This project established the viability of the ELPH concept and the design methodologies, in computer simulations. More work remains to be done on investigating more advanced power plants, such as fuel cells, and more advanced components, such as switched reluctance motor drives, for the designs. Furthermore, the design optimization can be carried out to more detailed levels, for prototyping and production.
Author: Bram de Jager
Publisher: Springer Science & Business Media
ISBN: 1447150767
Category : Technology & Engineering
Languages : en
Pages : 159
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Book Description
Optimal Control of Hybrid Vehicles provides a description of power train control for hybrid vehicles. The background, environmental motivation and control challenges associated with hybrid vehicles are introduced. The text includes mathematical models for all relevant components in the hybrid power train. The power split problem in hybrid power trains is formally described and several numerical solutions detailed, including dynamic programming and a novel solution for state-constrained optimal control problems based on the maximum principle. Real-time-implementable strategies that can approximate the optimal solution closely are dealt with in depth. Several approaches are discussed and compared, including a state-of-the-art strategy which is adaptive for vehicle conditions like velocity and mass. Three case studies are included in the book: • a control strategy for a micro-hybrid power train; • experimental results obtained with a real-time strategy implemented in a hybrid electric truck; and • an analysis of the optimal component sizes for a hybrid power train. Optimal Control of Hybrid Vehicles will appeal to academic researchers and graduate students interested in hybrid vehicle control or in the applications of optimal control. Practitioners working in the design of control systems for the automotive industry will also find the ideas propounded in this book of interest.
Author: Qing Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 346
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Book Description
Author: Xi Zhang
Publisher: Springer Science & Business Media
ISBN: 0857297368
Category : Technology & Engineering
Languages : en
Pages : 353
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Book Description
Vehicle Power Management addresses the challenge of improving vehicle fuel economy and reducing emissions without sacrificing vehicle performance, reliability and durability. It opens with the definition, objectives, and current research issues of vehicle power management, before moving on to a detailed introduction to the modeling of vehicle devices and components involved in the vehicle power management system, which has been proven to be the most cost-effective and efficient method for initial-phase vehicle research and design. Specific vehicle power management algorithms and strategies, including the analytical approach, optimal control, intelligent system approaches and wavelet technology, are derived and analyzed for realistic applications. Vehicle Power Management also gives a detailed description of several key technologies in the design phases of hybrid electric vehicles containing battery management systems, component optimization, hardware-in-the-loop and software-in-the-loop. Vehicle Power Management provides graduate and upper level undergraduate students, engineers, and researchers in both academia and the automotive industry, with a clear understanding of the concepts, methodologies, and prospects of vehicle power management.
