Cost and fuel-optimal selection of HEV topologies using Particle Swarm Optimization and Dynamic Programming

Nüesch, Tobias; Ott, Tobias; Ebbesen, Soren; Guzzella, Lino

doi:10.1109/acc.2012.6314868

Cited by 31 publications

(26 citation statements)

References 12 publications

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“…It is possible to estimate the required fuel for the RCV by using (9). Points, time and fuel consumption for each mode using the real drive are shown in Table II.…”

Section: B Classificationmentioning

confidence: 99%

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Cortez¹,

Moreno-Eguilaz²,

Soriano³

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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Abstract-This paper presents a new methodology to estimate the fuel economy in a hybrid electric refuse collector vehicle (RCV). This methodology is based on determinating the fuel consumption in a conventional RCV using real routes, through a quasi-static model that incorporates a mathematical modeling of each component of the powertrain, including the longitudinal dynamic of the conventional RCV. Moreover, a classification of different operational modes of a conventional RCV using real routes is proposed to determinate the required energy that must be provided by the hybrid electric counterpart. The optimal sizing of the energy storage system for the hybrid electric powertrain is presented in order to estimate the fuel consumption by using a real route. The error in the estimation of fuel consumption in a conventional RCV is less than 1.5% in the worst case scenario. The energy storage system sizing allows a 8.62% reduction in the hybrid electric refuse collector vehicle's fuel consumption, by considering 10% of hybridization.

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“…It is possible to estimate the required fuel for the RCV by using (9). Points, time and fuel consumption for each mode using the real drive are shown in Table II.…”

Section: B Classificationmentioning

confidence: 99%

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Cortez¹,

Moreno-Eguilaz²,

Soriano³

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

show abstract

“…Accurate cost models are available at literature [202,203]. A common conclusion is that full hybrids-with a strong electrification-offer an improved fuel efficiency but they are much more expensive; on the contrary, vehicles with a low level of hybridization usually show an acceptable fuel efficiency benefit at a low cost [189]. According to Nuesch et al, "the best topology should then be selected according to the individual preference" [189], suggesting that there is still a lack of an objective criterion that minimizes the payback and total expenses.…”

Section: Sizing Of Hybrid Powertrainsmentioning

confidence: 99%

“…Other approaches that are proposed in the literature are sequential optimization (the design problem is first addressed and, then, the control problem is solved for a given design, repeating this scheme iteratively) which cannot guarantee optimality of the solution, or the methodology proposed in [200]. Some authors also included the vehicle price to the above cost index in order to account for the tradeoff between fuel efficiency and HEV cost at different levels of electrification [189,201]. Accurate cost models are available at literature [202,203].…”

Section: Sizing Of Hybrid Powertrainsmentioning

confidence: 99%

“…Moreover, a given power-split strategy may perform efficiently for a specific powertrain size and produce undesirable results for another, making the size decision specially sensitive to the energy management control [197]. In order to make the powertrain characteristics the only difference, both the sizing problem and the power-split control must be addressed together [189,194,197], resulting in a mixed OC and parametric optimization problem:…”

Section: Sizing Of Hybrid Powertrainsmentioning

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%

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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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Multidisciplinary design optimization for hybrid electric vehicles: component sizing and multi-fidelity frontal crashworthiness

Anselma

Niutta

Mainini

et al. 2020

Struct Multidisc Optim

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Cost and fuel-optimal selection of HEV topologies using Particle Swarm Optimization and Dynamic Programming

Cited by 31 publications

References 12 publications

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Optimal Control for Automotive Powertrain Applications

Multidisciplinary design optimization for hybrid electric vehicles: component sizing and multi-fidelity frontal crashworthiness

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