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

Experimental and Numerical Analysis of Ethanol Fueled HCCI Engine

Presently, the research on the homogeneous charge compression ignition (HCCI) engines has gained importance in the field of automotive power applications due to its superior efficiency and low emissions compared to the conventional internal combustion (IC) engines. In principle, the HCCI uses premixed lean homogeneous charge that auto-ignites volumetrically throughout the cylinder. The homogeneous mixture preparation is the main key to achieve high fuel economy and low exhaust emissions from the HCCI engines. In the recent past, different techniques to prepare homogeneous mixture have been explored. The major problem associated with the HCCI is to control the auto-ignition over wide range of engine operating conditions. The control strategies for the HCCI engines were also explored. This dissertation investigates the utilization of ethanol, a potential major contributor to the fuel economy of the future. Port fuel injection (PFI) strategy was used to prepare the homogeneous mixture external to the engine cylinder in a constant speed, single cylinder, four stroke air cooled engine which was operated on HCCI mode. Seven modules of work have been proposed and carried out in this research work to establish the results of using ethanol as a potential fuel in the HCCI engine. Ethanol has a low Cetane number and thus it cannot be auto-ignited easily. Therefore, intake air preheating was used to achieve auto-ignition temperatures. In the first module of work, the ethanol fueled HCCI engine was thermodynamically analysed to determine the operating domain. The minimum intake air temperature requirement to achieve auto-ignition and stable HCCI combustion was found to be 130 °C. Whereas, the knock limit of the engine limited the maximum intake air temperature of 170 °C. Therefore, the intake air temperature range was fixed between 130-170 °C for the ethanol fueled HCCI operation. In the second module of work, experiments were conducted with the variation of intake air temperature from 130-170 °C at a regular interval of 10 °C. It was found that, the increase in the intake air temperature advanced the combustion phase and decreased the exhaust gas temperature. At 170 °C, the maximum combustion efficiency and thermal efficiency were found to be 98.2% and 43% respectively. The NO emission and smoke emissionswere found to be below 11 ppm and 0.1% respectively throughout this study. From these results of high efficiency and low emissions from the HCCI engine, the following were determined using TOPSIS method. They are (i) choosing the best operating condition, and (ii) which input parameter has the greater influence on the HCCI output. In the third module of work, TOPSIS - a multi-criteria decision making technique was used to evaluate the optimum operating conditions. The optimal HCCI operating condition was found at 70% load and 170 °C charge temperature. The analysis of variance (ANOVA) test results revealed that, the charge temperature would be the most significant parameter followed by the engine load. The percentage contribution of charge temperature and load were63.04% and 27.89% respectively. In the fourth module of work, the GRNN algorithm was used to predict the output parameters of the HCCI engine. The network was trained, validated, and tested with the experimental data sets. Initially, the network was trained with the 60% of the experimental data sets. Further, the validation and testing of the network was done with each 20% data sets. The validation results predicted that, the output parameters those lie within 2% error. The results also showed that, the GRNN models would be advantageous for network simplicity and require less sparse data. The developed new tool efficiently predicted the relation between the input and output parameters. In the fifth module of work, the EGR was used to control the HCCI combustion. An optimum of 5% EGR was found to be optimum, further increase in the EGR caused increase in the hydrocarbon (HC) emissions. The maximum brake thermal efficiency of 45% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 10 ppm and 0.61% respectively. In the sixth module of work, a hybrid GRNN-PSO model was developed to optimize the ethanol-fueled HCCI engine based on the output performance and emission parameters. The GRNN network interpretive of the probability estimate such that it can predict the performance and emission parameters of HCCI engine within the range of input parameters. Since GRNN cannot optimize the solution, and hence swarm based adaptive mechanism was hybridized. A new fitness function was developed by considering the six engine output parameters. For the developed fitness function, constrained optimization criteria were implemented in four cases. The optimum HCCI engine operating conditions for the general criteria were found to be 170 °C charge temperature, 72% engine load, and 4% EGR. This model consumed about 60-75 ms for the HCCI engine optimization. In the last module of work, an external fuel vaporizer was used to prepare the ethanol fuel vapour and admitted into the HCCI engine. The maximum brake thermal efficiency of 46% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 5 ppm and 0.45% respectively. Overall, it is concluded that, the HCCI combustion of sole ethanol fuel is possible with the charge heating only. The high load limit of HCCI can be extended with ethanol fuel. High thermal efficiency and low emissions were possible with ethanol fueled HCCI to meet the current demand

Energy for Sustainable Future

Energy and the environment are irrevocably interrelated, and they are critical factors that influence the development of societies. The pollution of the environment without considering various consequences has become one of the most important global issues today. This environmental pollution is mainly the result of increases in economic activities, population, transportation, electricity generation, agriculture, forestry, and land use. The exigency of energy for these activities, the rapidly rising price of petroleum oil, the harmful effect of greenhouse gases, and the quest for energy security have steered our attention towards sustainable sources of energy. It is fundamental to find innovative solutions that are sustainable from the perspective of energy management and environmental protection. This book includes three review articles which review the state-of-the-art of different sustainable energy resources. These articles include ammonia as a renewable energy carrier, integration of solar photovoltaic, and bio-oil from waste tires for automotive engine application. In addition, eight research studies reveal new knowledge about energy for a sustainable future. The topics covered span many diverse areas associated with sustainable energy, including various biofuels, photovoltaic, and other aspects of sustainability. These complementary contributions provide a substantial body of knowledge in the field of Renewable and Sustainable Energy

Evaluation and optimisation of traction system for hybrid railway vehicles

Over the past decade, energy and environmental sustainability in urban rail transport have become increasingly important. Hybrid transportation systems present a multifaceted challenge, encompassing aspects such as hydrogen production, refuelling station infrastructure, propulsion system topology, power source sizing, and control. The evaluation and optimisation of these aspects are critical for the adaptation and commercialisation of hybrid railway vehicles. While there has been significant progress in the development of hybrid railway vehicles, further improvements in propulsion system design are necessary. This thesis explores strategies to achieve this ambitious goal by substituting diesel trains with hybrid trains. However, limited research has assessed the operational performance of replacing diesel trains with hybrid trains on the same tracks. This thesis develops various optimisation techniques for evaluating and refining the hybrid traction system to address this gap. In this research's first phase, the author developed a novel Hybrid Train Simulator designed to analyse driving performance and energy flow among multiple power sources, such as internal combustion engines, electrification, fuel cells, and batteries. The simulator incorporates a novel Automatic Smart Switching Control technique, which scales power among multiple power sources based on the route gradient for hybrid trains. This smart switching approach enhances battery and fuel cell life and reduces maintenance costs by employing it as needed, thereby eliminating the forced charging and discharging of excessively high currents. Simulation results demonstrate a 6% reduction in energy consumption for hybrid trains equipped with smart switching compared to those without it. In the second phase of this research, the author presents a novel technique to solve the optimisation problem of hybrid railway vehicle traction systems by utilising evolutionary and numerical optimisation techniques. The optimisation method employs a nonlinear programming solver, interpreting the problem via a non-convex function combined with an efficient "Mayfly algorithm." The developed hybrid optimisation algorithm minimises traction energy while using limited power to prevent unnecessary load on power sources, ensuring their prolonged life. The algorithm takes into account linear and non-linear variables, such as velocity, acceleration, traction forces, distance, time, power, and energy, to address the hybrid railway vehicle optimisation problem, focusing on the energy-time trade-off. The optimised trajectories exhibit an average reduction of 16.85% in total energy consumption, illustrating the algorithm's effectiveness across diverse routes and conditions, with an average increase in journey times of only 0.40% and a 15.18% reduction in traction power. The algorithm achieves a well-balanced energy-time trade-off, prioritising energy efficiency without significantly impacting journey duration, a critical aspect of sustainable transportation systems. In the third phase of this thesis, the author introduced artificial neural network models to solve the optimisation problem for hybrid railway vehicles. Based on time and power-based architecture, two ANN models are presented, capable of predicting optimal hybrid train trajectories. These models tackle the challenge of analysing large datasets of hybrid railway vehicles. Both models demonstrate the potential for efficiently predicting hybrid train target parameters. The results indicate that both ANN models effectively predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade-forward neural network topology in the time-based architecture outperforms the feed-forward neural network topology in terms of mean squared error and maximum error in the power-based architecture. Specifically, the cascade-forward neural network topology within the time-based structure exhibits a slightly lower MSE and maximum error than its power-based counterpart. Moreover, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time-based architecture exhibits lower differences (0.18% and 0.85%) compared to the power-based architecture (0.46% and 0.92%)

Marine dual fuel engines modelling and optimisation employing : a novel combustion characterisation method

Dual fuel (DF) engines have been an attractive alternative of traditional diesel engines for reducing both the environmental impact and operating cost. The major challenge of DF engine design is to deal with the performance-emissions trade-off via operating settings optimisation. Nevertheless, determining the optimal solution requires large amount of case studies, which could be both time-consuming and costly in cases where methods like engine test or Computational Fluid Dynamics (CFD) simulation are directly used to perform the optimisation. This study aims at developing a novel combustion characterisation method for marine DF engines based on the combined use of three-dimensional (3D) simulation and zero-dimensional/one-dimensional (0D/1D) simulation methods. The 3D model is developed with the CONVERGE software and validated by employing the measured pressure and emissions. Subsequently, the validated 3D model is used to perform a parametric study to explore the engine operating settings that allow simultaneous reduction of the brake specific fuel consumption (BSFC) and NOx emissions at three engine operation conditions (1457 r/min, 1629 r/min and 1800 r/min). Furthermore, the derived heat release rate (HRR) is employed to calibrate the 0D Wiebe combustion model by using Response Surface Methodology (RSM). A linear response model for the Wiebe combustion function parameters is proposed by considering each Wiebe parameter as a function of the pilot injection timing, equivalence ratio and natural gas mass. The 0D/1D model is established in the GT-ISE software and used to optimise the performance-emissions trade-off of the reference engine by employing the Nondominated Sorting Genetic Algorithm II (NSGA II). The obtained results provide a comprehensive insight on the impacts of the involved engine operating settings on in-cylinder combustion characteristics, engine performance and emissions of the investigated marine DF engine. By performing the settings optimisation at three engine operating points, settings that lead to reduced BSFC are identified, whilst the NOx emissions comply with the Tier III NOx emissions regulation. The proposed novel method is expected to support the combustion analysis and enhancement of marine DF engines during the design phase, whilst the derived optimal solution is expected to provide guidelines of DF engine management for reducing operating cost and environmental footprint.Dual fuel (DF) engines have been an attractive alternative of traditional diesel engines for reducing both the environmental impact and operating cost. The major challenge of DF engine design is to deal with the performance-emissions trade-off via operating settings optimisation. Nevertheless, determining the optimal solution requires large amount of case studies, which could be both time-consuming and costly in cases where methods like engine test or Computational Fluid Dynamics (CFD) simulation are directly used to perform the optimisation. This study aims at developing a novel combustion characterisation method for marine DF engines based on the combined use of three-dimensional (3D) simulation and zero-dimensional/one-dimensional (0D/1D) simulation methods. The 3D model is developed with the CONVERGE software and validated by employing the measured pressure and emissions. Subsequently, the validated 3D model is used to perform a parametric study to explore the engine operating settings that allow simultaneous reduction of the brake specific fuel consumption (BSFC) and NOx emissions at three engine operation conditions (1457 r/min, 1629 r/min and 1800 r/min). Furthermore, the derived heat release rate (HRR) is employed to calibrate the 0D Wiebe combustion model by using Response Surface Methodology (RSM). A linear response model for the Wiebe combustion function parameters is proposed by considering each Wiebe parameter as a function of the pilot injection timing, equivalence ratio and natural gas mass. The 0D/1D model is established in the GT-ISE software and used to optimise the performance-emissions trade-off of the reference engine by employing the Nondominated Sorting Genetic Algorithm II (NSGA II). The obtained results provide a comprehensive insight on the impacts of the involved engine operating settings on in-cylinder combustion characteristics, engine performance and emissions of the investigated marine DF engine. By performing the settings optimisation at three engine operating points, settings that lead to reduced BSFC are identified, whilst the NOx emissions comply with the Tier III NOx emissions regulation. The proposed novel method is expected to support the combustion analysis and enhancement of marine DF engines during the design phase, whilst the derived optimal solution is expected to provide guidelines of DF engine management for reducing operating cost and environmental footprint

A Self regenerating diesel emissions particulate trap using a non-thermal plasma

There is great concern about the adverse effects associated with exposure to diesel exhaust. There is increasing evidence that diesel exhaust particulate matter (PM) is carcinogenic and may cause cancer. Non-cancerous lung damage and respiratory problems are also associated with exposure to diesel exhaust as well as acid rain and smog. Diesel exhaust PM is very easily respirable once emitted into the atmosphere and therefore poses a significant health problem. A diesel engine emissions particle removal system which utilizes Electrostatic Precipitation (ESP) and Non Thermal Plasma (NTP) technologies was studied for trapping and oxidizing micron sized particles (0.01 to 10 microns) in the exhaust. Particles are first charged in a mono polar manner in a NTP in the diesel exhaust stream, and then collected on an electrically grounded precipitation surface. Gaseous radicals produced in the NTP oxidize the precipitated particles to provide a continuously regenerating system. This device is targeted to help meet recently instituted US Environmental Protection Agency (EPA) Tier II as well as upcoming European (Euro 4, 5) and Japanese diesel particulate emissions standards. This system can be coupled with a suitable catalyst or other emissions treatment technologies to produce a complete exhaust aftertreatment system. Analytical and empirical methods were used to model the proposed Self Regenerating Diesel Emissions Particulate Trap. The analysis showed that a total particle precipitation efficiency of greater than 95% could be obtained using less than 0.5% of total engine energy output at a vehicle speed of 120 km/hr for a compact diesel powered vehicle. It was determined that the energy requirement for producing gaseous radicals in the exhaust stream is higher than is needed for particle charging and precipitation. It was also determined that the conversion of radicals can be accomplished using less than 2% of the total engine output. The results of the model developed shows that the proposed device would be effective reducing diesel PM emissions on a heavy-duty vehicle

The effect of compression ratio on the performance of a direct injection diesel engine

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis considers the effect of compression ratio on the performance of a direct injection diesel engine. One aspect of engine performance is considered in great detail, namely the combustion performance at increased clearance volume. This aspect was of particular interest because variable compression ratio (VCR) systems normally operate by varying the clearance volume. The investigation relied upon results obtained both from experimental and computer simulating models. The experimental tests were carried out using a single-cylinder direct-injection diesel engine, under simulated turbocharged conditions at a reduced compression ratio. A number of one-dimensional computer models were developed; these simulate the induction and compression strokes, and the fuel spray trajectories in the presence of air swirl. The major objectives of the investigation were: to assess the benefits of VCR in terms of improvements in output power and fuel economy; to assess the effects on combustion of increased clearance volume, and investigate methods for ameliorating resulting problems; develop computational models which could aid understanding of the combustion process under varying clearance volume conditions. It was concluded that at the reduced compression ratio of 12.9:1 (compared to the standard value of 17.4:1 for the naturally-aspirated engine), brake mean effective pressure (BMEP) could be increased by more than 50%, and the brake specific fuel consumption (BSFC) could be reduced by more than 20%. These improvements were achieved without the maximum cylinder pressure or engine temperatures exceeding the highest values for the standard engine. Combustion performance deteriorated markedly, but certain modifications to the injection system proved successful in ameliorating the problems. These included: increase in the number of injector nozzle holes from 3 to 4, increase in injection rate by about 28%, advancing injection timing by about 6°CA. In addition, operation with weaker air fuel ratio, in the range of 30 to 40:1 reduced smoke emissions and improved BSFC. Use of intercooling under VCR conditions provided only modest gains in performance. The NO emission was found to be insensitive to engine operating conditions (fixed compression ratio of 12.9:1), as long as the peak cylinder pressure was maintained constant. Engine test results were used in order to assess the accuracy of four published correlations for predicting ignition delay. The best prediction of ignition delay with these correlations deviated by up to 50% from the measured values. The computer simulation models provided useful insights into the fuel distribution within the engine cylinder. It also became possible to quantify the interaction between the swirling air and the fuel sprays, using two parameters: the crosswind and impingement velocities of the fuel spray when it impinges on the piston-bowl walls. Tentative trends were identified which showed that high crosswind velocity coincided with lower smoke emissions and lower BSFC

Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems