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

Development of a Variable Roller Pump and Evaluation of its Power Saving Potential as a Charge Pump in Hydrostatic Drivetrains

Predložená doktorandská dizertačná práca (ďalej len práca) sa zaoberá rozsiahlou analýzou valčekového hydrogenerátora s premenlivým geometrickým objemom a predikciou výkonových úspor dosiahnutých aplikáciou navrhnutého valčekového hydrogenerátora s premenlivým geometrickým objemom v hydrostatickom pohone vybraných mobilných pracovných strojov. Teoretický rozbor princípov fungovania valčekového hydrogenerátora a teória jednorozmerného simulačného modelu sú popísané v prvej časti práce. Na základe odvodenej teórie je vytvorený simulačný model, ktorý je vhodný na predikciu priebehu tlaku v komorách valčekového hydrogenerátora, síl pôsobiacich na valček a na predikciu vnútorných únikov vzniknutých skratovaním rozvodovej dosky, ktoré majú priamy vplyv na objemovú účinnosť valčekového hydrogenerátora. Simulačný model bol úspešne použitý pre optimalizáciu rozvodových dosiek valčekového hydrogenerátora a vhodnosť simulačného modelu potvrdili následné merania Práca obsahuje aj analýzu síl pôsobiacich na vodiaci prstenec, ktorej výsledky boli taktiež potvrdené meraním. Analýza týchto síl môže vylepšiť v konečnom dôsledku parametre budúcich tlakových regulácii. Práca ďalej obsahuje základné porovnanie použitých tlakových regulácii. Všetky uskutočnené merania potvrdili, že valčekový hydrogenerátor s premenlivým geometrickým objemom s testovanými tlakovými reguláciami je schopný úspešne pracovať v hydrostatickej prevodovke. Druhá časť práce analyzuje potenciál výkonových úspor valčekového hydrogenerátora s premenlivým geometrickým objemom pre dve mobilné aplikácie - teleskopický nakladač s hmotnosťou 9 ton a kombajn s hmotnosťou 20 ton. Analýza vyžaduje jednorozmerný simulačný model hydrostatického pohonu s teplotnou predikciou hydrostatickej prevodovky. Dva rozdielne koncepty variabilného doplňovacieho systému hydrostatickej prevodovky sú porovnané so štandardným doplňovacím systémom pre pracovný a transportný režim oboch vybraných typov vozidiel. Simulácia pohonu vozidla s valčekovým hydrogenerátorom s premenlivým geometrickým objemom vo funkcii doplňovacieho hydrogenerátora a obtokovou clonou potvrdili vyššie úspory iba v prípadoch, kedy rýchlosť doplňovacieho hydrogenerátora bola výrazne vyššia a prietok cez obtokovú clonu do skrine hlavného hydrogenerátora zabezpečil dostatočné chladenie. Najvyššie výkonové úspory boli dosiahnuté s premenlivým preplachovacím systémom, ktorého prietok sa menil podľa požiadaviek hydrostatickej prevodovky. Záver druhej časti práce sa zaoberá metodikou dimenzovania veľkosti doplňovacieho hydrogenerátora.Presented doctoral thesis deals with an extensive hydraulic variable roller pump analysis and the power saving prediction of hydrostatic drivetrains in the mobile machines achieved with a variable roller charge pump implementation. At the first part of the work, the roller pump functionality was described and the theory of a 1-D simulation model was developed. Based on this developed simulation model is suitable for pressure profile prediction, roller force prediction and cross port leakage prediction which has a direct impact on the total volumetric efficiency. The simulation model was successfully used as a tool for optimization of the port plates, which was confirmed by measurements. The first part of the work includes the pump control force analysis validated by measurements and also the basic pressure compensator controls comparison. Developed control force prediction could help to improve the control performance. The measurements confirmed that the variable roller charge pump is able to successfully work in transmissions with measured types of the control. The second part of the work analyzed the power saving potential of a variable charge pump for two selected typical mobile applications: telehandler (9 ton) and combine harvester (20 ton). This part required a 1-D drivetrain simulation model together with thermal behaviour of the hydrostatic transmission. Two different modifications of the charging systems were compared with the conventional charging system in simulations performed for the working and transporting mode. The drivetrain simulation of the variable roller charge pump with a bypass orifice confirms higher power savings only in cases when the pump speed was significantly higher than normal speeds and a relatively constant flushing flow through the bypass orifice to the pump case still ensures suitable cooling. The highest power savings were achieved with variable flushing flows, where the demand for charging flow was adjusted according to the hydrostatic transmission cooling requirements. At the end of the second part, this thesis deals with a variable charge pump sizing.

Eras of electric vehicles: electric mobility on the Verge. Focus Attention Scale

Daily or casual passenger vehicles in cities have negative burden on our finite world. Transport sector has been one of the main contributors to air pollution and energy depletion. Providing alternative means of transport is a promising strategy perceived by motor manufacturers and researchers. The paper presents the battery electric vehicles-BEVs bibliography that starts with the early eras of invention up till 2015 outlook. It gives a broad overview of BEV market and its technology in a chronological classification while sheds light on the stakeholders’ focus attentions in each stage, the so called, Focus-Attention-Scale-FAS. The attention given in each era is projected and parsed in a scale graph, which varies between micro, meso, and macro-scale. BEV-system is on the verge of experiencing massive growth; however, the system entails a variety of substantial challenges. Observations show the main issues of BEVsystem that require more attention followed by the authors’ recommendations towards an emerging market

An Intelligent Advisor for City Traffic Policies

Nowadays, city streets are populated not only by private vehicles but also by public transport, fleets of workers, and deliveries. Since each vehicle class has a maximum cargo capacity, we study in this article how authorities could improve the road traffic by endorsing long term policies to change the different vehicle proportions: sedans, minivans, full size vans, trucks, and motorbikes, without losing the ability of moving cargo throughout the city. We have performed our study in a realistic scenario (map, road traffic characteristics, and number of vehicles) of the city of Malaga and captured the many details into the SUMO microsimulator. After analyzing the relationship between travel times, emissions, and fuel consumption, we have defined a multiobjective optimization problem to be solved, so as to minimize these city metrics. Our results provide a scientific evidence that we can improve the delivery of goods in the city by reducing the number of heavy duty vehicles and fostering the use of vans instead.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by the Spanish MINECO and FEDER projects TIN2014-57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. University of Malaga, Andalucia TECH. Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish MECD. Christian Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis

Empowering citizens' cognition and decision making in smart sustainable cities

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Advances in Internet technologies have made it possible to gather, store, and process large quantities of data, often in real time. When considering smart and sustainable cities, this big data generates useful information and insights to citizens, service providers, and policy makers. Transforming this data into knowledge allows for empowering citizens' cognition as well as supporting decision-making routines. However, several operational and computing issues need to be taken into account: 1) efficient data description and visualization, 2) forecasting citizens behavior, and 3) supporting decision making with intelligent algorithms. This paper identifies several challenges associated with the use of data analytics in smart sustainable cities and proposes the use of hybrid simulation-optimization and machine learning algorithms as an effective approach to empower citizens' cognition and decision making in such ecosystemsPeer ReviewedPostprint (author's final draft