Optimal Sizing of a Series Hybrid Electric Vehicle Using a Hybrid Genetic Algorithm

Liu, Xudong; Wu, Yanping; Duan, Jianmin

doi:10.1109/ical.2007.4338737

Cited by 25 publications

(16 citation statements)

References 10 publications

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“…For dynamic and unpredictable driving situations, a fuzzy clustering criterion International Journal of Vehicular Technology 9 is used with GA which reduces the computational effort and improves the fuel economy [78]. GA in HEVs is used simultaneously to optimize the component sizes and to minimize the fuel consumption and emissions [79][80][81][82][83][84]. Wang and Yang [85] implement a robust, easy, and real-time implementable FL based energy management strategy and use the GA to tune and optimize the same.…”

Section: Global Optimization a Global Optimization Techniquementioning

confidence: 99%

A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

Panday

Bansal

2014

International Journal of Vehicular Technology

196

104

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Presence of an alternative energy source along with the Internal Combustion Engine (ICE) in Hybrid Electric Vehicles (HEVs) appeals for optimal power split between them for minimum fuel consumption and maximum power utilization. Hence HEVs provide better fuel economy compared to ICE based vehicles/conventional vehicle. Energy management strategies are the algorithms that decide the power split between engine and motor in order to improve the fuel economy and optimize the performance of HEVs. This paper describes various energy management strategies available in the literature. A lot of research work has been conducted for energy optimization and the same is extended for Plug-in Hybrid Electric Vehicles (PHEVs). This paper concentrates on the battery powered hybrid vehicles. Numerous methods are introduced in the literature and based on these, several control strategies are proposed. These control strategies are summarized here in a coherent framework. This paper will serve as a ready reference for the researchers working in the area of energy optimization of hybrid vehicles.

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Section: Global Optimization a Global Optimization Techniquementioning

confidence: 99%

A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

Panday

Bansal

2014

International Journal of Vehicular Technology

196

104

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“…Usually a cost index is defined, including fuel consumption, vehicle cost or pollutant emissions, and an optimization method is employed to reach a global minimum to the problem. Many works rely on genetic algorithms [18], [19] or particle swarm optimization [20], [21] to surf across a space of component size candidates. Other studies make use of convex optimization techniques [22], mainly Pontryagin minimum principle, mixing the control strategy optimization and battery sizing into one single problem [23] or to guarantee the optimal operation of the powertrain at any component size combination [24].…”

Section: Introductionmentioning

confidence: 99%

Cost of ownership-efficient hybrid electric vehicle powertrain sizing for multi-scenario driving cycles

Luján

Guardiola

Plá

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part D:

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Luján, JM.; Guardiola, C.; Pla Moreno, B.; Reig, A. (2016 and in the society the hybrid vehicle is increasingly common. Many manufacturers had become involved in the business while some others have HEV projects in development. Thus, there is already a great variety of HEV in production -from small micro hybrids to range extenders.Although there are some HEVs designed for urban driving or luxury segments, most of the market share is aimed to the same kind of use and driving, resulting in potentially sub-or oversized hybrid systems that could lead to an inefficient use of the vehicle's fuel saving capabilities in many situations.The present work studies powertrain components (i.e. engine, motor and battery) size influence on fuel economy under different assumptions: urban, highway and mixed driving. The utilized framework permits the calculation of the theoretically optimum powertrain size assuming a particular target. Different drivers and traffic conditions are also evaluated. Finally, a longterm cost evaluation is carried out to estimate the optimal size of the HEV powertrain as a function of vehicle's type of use along its life-cycle.

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“…Performance benchmarks of these two methods are issued in [122][123][124], with an detailed discussion in [125]. Other approaches such as DM [126], fuzzy logic [127], neural networks [128], genetic algorithms [129,130], or linear optimization [131] have also been already investigated in literature with limited acceptance. Heuristics are the topic for some works as well [132][133][134], which is the solution usually adopted by the industry, but since it is an arbitrary question these works fall out of the interest of this thesis.…”

Section: Hybrid Powertrains: Power-split Controlmentioning

confidence: 99%

“…However, it is possible to benefit from mathematical optimization algorithms to find the most efficient powertrain design for a given set of requirements. Hence, the choice of the powertrain components may be approached as an optimization problem whose unknowns-battery, motor and engine sizes-must minimize fuel consumption [ [130,183,187,188], particle swarm optimization [185,189], convex optimization [190][191][192][193] or MINLP [184] among others. Despite its designing advantages, it must be taken into account that optimal approaches to his problem are cycledependent, since the performance of the powertrain is intrinsically linked to the particular driving cycle.…”

Section: Sizing Of Hybrid Powertrainsmentioning

confidence: 99%

Optimal Control for Automotive Powertrain Applications

Bernad¹

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To the geniuses of the past, who built our future; to those names in the shades, bringing our knowledge. They not only supported this work but also the world we live in.If you can solve it, it is an exercise; otherwise it's a research problem.-Richard Bellman e ResumenEl ControlÓptimo (CO) es esencialmente un problema matemático de bús-queda de extremos, consistente en la definición de un criterio a minimizar (o maximizar), restricciones que deben satisfacerse y condiciones de contorno que afectan al sistema. La teoría de CO ofrece métodos para derivar una trayectoria de control que minimiza (o maximiza) ese criterio.Esta Tesis trata la aplicación del CO en automoción, y especialmente en el motor de combustión interna. Las herramientas necesarias son un método de optimización y una representación matemática de la planta motriz. Para ello, se realiza un análisis cuantitativo de las ventajas e inconvenientes de los tres métodos de optimización existentes en la literatura: programación dinámica, principio mínimo de Pontryagin y métodos directos. Se desarrollan y describen los algoritmos para implementar estos métodos así como un modelo de planta motriz, validado experimentalmente, que incluye la dinámica longitudinal del vehículo, modelos para el motor eléctrico y las baterías, y un modelo de motor de combustión de valores medios.El CO puede utilizarse para tres objetivos distintos:1. Control aplicado, en caso de que las condiciones de contorno estén definidas. Puede aplicarse al control del motor de combustión para un ciclo de conducción dado, traduciéndose en un problema matemático de grandes dimensiones. Se estudian dos casos particulares: la gestión de un sistema de EGR de doble lazo, y el control completo del motor, en particular de las consignas de inyección, SOI, EGR y VGT.2. Obtención de reglas de control cuasi-óptimas, aplicables en casos en los que no todas las perturbaciones se conocen. A este respecto, se analizan el cálculo de calibraciones de motor específicas para un ciclo, y la gestión energética de un vehículo híbrido mediante un control estocástico en bucle cerrado.3. Empleo de trayectorias de CO como comparativa o referencia para tareas de diseño y mejora, ofreciendo un criterio objetivo. La ley de combustión así como el dimensionado de una planta motriz híbrida se optimizan mediante el uso de CO.Las estrategias de CO han sido aplicadas experimentalmente en los trabajos referentes al motor de combustión, poniendo de manifiesto sus ventajas sustanciales, pero también analizando dificultades y líneas de actuación para superarlas. Los métodos desarrollados en esta Tesis Doctoral son generales y aplicables a otros criterios si se dispone de los modelos adecuados. ResumEl ControlÒptim (CO)és essencialment un problema matemàtic de cerca d'extrems, que consisteix en la definició d'un criteri a minimitzar (o maximitzar), restriccions que es deuen satisfer i condicions de contorn que afecten el sistema. La teoria de CO ofereix mètodes per a derivar una trajectòria de control que minimitza (o maximitza) a...

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Optimal Sizing of a Series Hybrid Electric Vehicle Using a Hybrid Genetic Algorithm

Cited by 25 publications

References 10 publications

A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

Cost of ownership-efficient hybrid electric vehicle powertrain sizing for multi-scenario driving cycles

Optimal Control for Automotive Powertrain Applications

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