Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

A novel genetic programming approach to the design of engine control systems for the voltage stabilisation of hybrid electric vehicle generator outputs

This paper describes a Genetic Programming based automatic design methodology applied to the maintenance of a stable generated electrical output from a series-hybrid vehi- cle generator set. The generator set comprises a 3-phase AC generator whose output is subsequently rectified to DC.The engine/generator combination receives its control input via an electronically actuated throttle, whose control integration is made more complex due to the significant system time delay. This time delay problem is usually addressed by model predictive design methods, which add computational complexity and rely as a necessity on accurate system and delay models. In order to eliminate this reliance, and achieve stable operation with disturbance rejection, a controller is designed via a Genetic Programming framework implemented directly in Matlab, and particularly, Simulink. the principal objective is to obtain a relatively simple controller for the time-delay system which doesn’t rely on computationally expensive structures, yet retains inherent disturabance rejection properties. A methodology is presented to automatically design control systems directly upon the block libraries available in Simulink to automatically evolve robust control structures

A Critical Review of Optimization Methods for Road Vehicles Design

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77078/1/AIAA-2006-6998-235.pd

Modelling and control of hybrid electric vehicles (a comprehensive review)

The gradual decline in global oil reserves and presence of ever so stringent emissions rules around the world, have created an urgent need for the production of automobiles with improved fuel economy. HEVs (hybrid electric vehicles) have proved a viable option to guarantying improved fuel economy and reduced emissions.The fuel consumption benefits which can be realised when utilising HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilized. The challenge in developing an HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability without over-depleting the battery state of charge at the end of the defined driving cycle.To this effect, a number of power management strategies have been proposed in literature. This paper presents a comprehensive review of these literatures, focusing primarily on contributions in the aspect of parallel hybrid electric vehicle modelling and control. As part of this treatise, exploitable research gaps are also identified. This paper prides itself as a comprehensive reference for researchers in the field of hybrid electric vehicle development, control and optimization

Sustainable aviation electrification: a comprehensive review of electric propulsion system architectures, energy management, and control

The civil aviation sector plays an increasingly significant role in transportation sustainability in the environmental, economic, and social dimensions. Driven by the concerns of sustainability in the aviation sector, more electrified aircraft propulsion technologies have emerged and form a very promising approach to future sustainable and decarbonized aviation. This review paper aims to provide a comprehensive and broad-scope survey of the recent progress and development trends in sustainable aviation electrification. Firstly, the architectures of electrified aircraft propulsion are presented with a detailed analysis of the benefits, challenges, and studies/applications to date. Then, the challenges and technical barriers of electrified aircraft propulsion control system design are discussed, followed by a summary of the control methods frequently used in aircraft propulsion systems. Next, the mainstream energy management strategies are investigated and further utilized to minimize the block fuel burn, emissions, and economic cost. Finally, an overview of the development trends of aviation electrification is provided

Optimal energy management strategies for hybrid electric vehicles : A recent survey of machine learning approaches

Hybrid Electric Vehicles (HEVs) have emerged as a viable option for reducing pollution and attaining fuel savings in addition to reducing emissions. The effectiveness of HEVs heavily relies on the energy management strategies (EMSs) employed, as it directly impacts vehicle fuel consumption. Developing suitable EMSs for HEVs poses a challenge, as the goal is to maximize fuel economy yet optimize vehicle performance. EMSs algorithms are critical in determining power distribution between the engine and motor in HEVs. Traditionally, EMSs for HEVs have been developed based on optimal control theory. However, in recent years, a rising number of people have been interested in utilizing machine-learning techniques to enhance EMSs performance. This article presents a current analysis of various EMSs proposed in the literature. It highlights the shift towards integrating machine learning and artificial intelligence (AI) breakthroughs in EMSs development. The study examines numerous case studies, and research works employing machine learning techniques across different categories to develop energy management strategies for HEVs. By leveraging advancements in machine learning and AI, researchers have explored innovative approaches to optimize HEVs’ performance and fuel economy. Key conclusions from our investigation show that machine learning has made a substantial contribution to solving the complex problems associated with HEV energy management. We emphasize how machine learning algorithms may be adjusted to dynamic operating environments, how well they can identify intricate patterns in hybrid electric vehicle systems, and how well they can manage non-linear behaviors

Multiobjective Optimization of the Power Flow Control of Hybrid Electric Power Train Systems within Simulation and Experimental Emulation Applications

In this thesis, the power flow control of hybrid electric power train systems is discussed using the focus of multiobjective optimization goals and related algorithms, based on different control optimization methods, are developed and applied within simulation and experimental environments. Based on the basic relations of hybrid power train systems, an improved technique for the experimental realization and evaluation of these systems is developed and the related Hardware-in-the-Loop (HiL) hybrid electric power train emulation system is demonstrated. Hereby, it is shown that this emulation system technique is suitable to be applied for a more generalized view of the power train structures (consideration of the components as power sources, sinks, transmission elements, storage elements etc.) and its power flow control. The principal applicability of the system is demonstrated using the example of a hybrid electric vehicle as well as other system technologies such as hybrid hydraulic power trains and wind energy conversion systems. The core of the thesis is the discussion, development, application, and evaluation of power flow control optimization algorithms. Hereby, the considered power flow control techniques of the power train are realized with respect to a multiobjective framework using the example of drivability, fuel economy, and component life time as system requirements to be optimized during the operation. From this requirements, a multiobjective control optimization problem results consisting of a suitable combination of the known control goals power management, energy management, and lifetime management is realized. After a discussion about the principal influences of the power flow control on the different performance properties, the application of different control optimization techniques is discussed. Hereby, the example of a fuel cell/supercapacitor-based hybrid electric power train system including braking energy recovery is used. As control optimization methods, parameter optimization techniques are applied at first. Hereby, an embedded-online optimization based on a Golden Section search and an offline optimization based on Global Optimation methods are discussed and applied. Furthermore, direct optimization techniques based on Dynamic Programming (DP) and Model Predictive Control (MPC) are realized. Subsequently, an Instantaneous Optimality (IO)-based technique, which consists of a lookup table-based Time-Invariant Feedback Controller technique, is developed. It becomes clear that all methods leads to suitable results and significant improvement of the control performance. A concluding overview of the methods and its strengths and weaknesses dependent on the application is provided.In dieser Arbeit wird die Leistungsflussregelung bei hybridelektrischen Antriebssystemen mit dem Schwerpunkt der Mehrkriterienoptimierung diskutiert. Hierbei werden geeignete Algorithmen, basierend auf verschiedenen Stellgrößenoptimierungsmethoden, entwickelt und in Simulationen sowie in experimentellem Umfeld angewendet. Aufbauend auf die Grundzusammenhänge hybrider Antriebssysteme wird eine weiterentwickelte experimentelle Umgebung zur Untersuchung und Bewertung vorgestellt und der entsprechende Hardware-in-the-Loop (HiL)-Versuchsstand zur Emulation entsprechender Systeme demonstriert. Diese Emulationstechnik erlaubt eine generalisierte Betrachtung von Antriebssystemstrukturen (Betrachtung der Komponenten als Quellen, Senken, Übertragungselemente, Speicher etc.) und der entsprechenden Leistungsflussregelung. Den Hauptteil dieser Arbeit bildet die Diskussion sowie die Entwicklung, Anwendung und Bewertung von Algorithmen zur Optimierung der Leistungsflussregelung hybridelektrischer Antriebssysteme. In diesem Zusammenhang erfolgt eine mehrkriterielle Betrachtung und Bewertung des Antriebssystems in Hinblick auf die Dynamik, die Kraftstoffökonomie und die Komponentenlebensdauer. Das hieraus resultierende mehrkriterielle Optimierungsproblem der Stellgrößenfolge kann hierbei als Überlagerung von Leistungs-, Energie- und Lebensdauermanagement aufgefasst werden. Basierend auf den Haupteinflüssen der Leistungsflussregelungen auf verschiedene Systemeigenschaften erfolgt die Entwicklung, Anwendung, Bewertung und Diskussion verschiedener Stellgrößenoptimierungsmethoden und -algorithmen. Diese werden am Beispiel eines Brennstoffzellen/Supercap-basierten hybridelektrischen Antriebssystems mit Bremsenergierekuperation demonstriert. Zur Optimierung der Leistungsflussregelung werden als erstes Parameteroptimierungstechniken vorgestellt, wobei eine Embedded-online-Optimierung basierend auf der Methode des Goldenen Schnitts sowie eine Offline-Optimierung unter Verwendung von globalen Optimierungsalgorithmen diskutiert und angewendet werden. Nachfolgend werden direkte Stellgrößenoptimierungstechniken vorgestellt, wobei die Verfahren der Dynamischen Programmierung und des Modelprädiktiven Reglers realisiert werden. Abschließend wird die Entwicklung und Anwendung eines Algorithmus basierend auf der momentanen Optimalität (Instantaneous Optimality) diskutiert, welcher aus einem kombinierten Geschwindigkeits-Prädiktionsalgorithmus und vordefinierten Kennfeldern für die Regelung besteht. Die verwendeten Methoden werden vergleichend gegenübergestellt und gemäß ihrer Stärken und Schwächen bewertet

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools