Including a Battery State of Health model in the HEV component sizing and optimal control problem

Johannesson, Lars; Murgovski, Nikolce; Ebbesen, Soren; Egardt, Bo; Gelso, Esteban R.; Hellgren, Jonas

doi:10.3182/20130904-4-jp-2042.00018

Cited by 29 publications

(18 citation statements)

References 8 publications

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“…Battery life-cycle could deform the theoretical optimum to, for example, a bigger battery to minimize the number of charge/discharge cycles for a given mileage. The interested reader may find an extended discussion of components sizing and battery life-cycle at [22].…”

Section: Resultsmentioning

confidence: 99%

“…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]. Vehicle performance is commonly introduced in the optimization problem in the form of an additional constraint that bounds the space of candidates as approached in [20].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

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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“…For instance, in [1], [2], a genetic algorithm (GA) has been used to find the sub-optimal gear ratios, battery capacity, and MG power with respect to fuel consumption only. In [3], a dynamic programming (DP) approach is applied to optimize non-convex and mixed-integer models, whereas a convex optimization scheme for component sizing is proposed in [4], [5]. In addition to the mainly employed fuel consumption criterion, the battery's state of health has been considered in [4] for the entire lifespan of a hybrid bus.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], a dynamic programming (DP) approach is applied to optimize non-convex and mixed-integer models, whereas a convex optimization scheme for component sizing is proposed in [4], [5]. In addition to the mainly employed fuel consumption criterion, the battery's state of health has been considered in [4] for the entire lifespan of a hybrid bus. However, less work has been done by incorporating The authors Boehme and Frank are with the IAV Automotive Engineering, Department of Gasoline Engine Systems, 38518 Gifhorn, Germany (contact: dr.thomas.boehme@iav.de).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimal powertrain design of parallel hybrid vehicles with respect to fuel consumption and driving performance

Boehme

Frank

Schori

et al. 2014

2014 European Control Conference (ECC)

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In the past decade, Hybrid Electric Vehicles have been demonstrated to significantly reduce the fuel consumption and emissions. However, this capability strongly depends on the sizing of the components and on the quality of the energy management. These challenges require new optimization procedures for a systematical exploration of the design space to find the optimal component sizings and control trajectories. A novel two-layer optimization strategy based on a multi-objective problem formulation is proposed. The first layer consists of a multi-objective genetic algorithm for determining the best system design parameters with respect to fuel consumption and driving performance. The second layer solves a deterministic hybrid optimal control problem (HOCP) to find for each individual of the population pool the optimal continuous and discrete control trajectories for the energy management. The proposed optimization strategy is benchmarked to a one-layer genetic algorithm approach on a parallel hybrid design study.

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“…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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Including a Battery State of Health model in the HEV component sizing and optimal control problem

Cited by 29 publications

References 8 publications

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

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

Multi-objective optimal powertrain design of parallel hybrid vehicles with respect to fuel consumption and driving performance

Optimal Control for Automotive Powertrain Applications

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