Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Controlling Diesel Engines: <s> Because of the inherent advantages, i. e. increased fuel economy, reduced harmful emissions and better vehicle performance, hybrid electric vehicles (HEV), powered by internal combustion engine (ICE) and energy storage, are being given more and more attention. Since the extent of HEV improvement greatly depends on selection of the control strategy parameters, particle swarm optimization (PSO) algorithm is introduced to optimize the strategy parameters for fuel economy and emissions in this paper. Compared with one of the main strategies, Dividing RECTangles (DIRECT), the computation procedures of particle swarm optimization algorithm are discussed, and simulation study based on the model of series hybrid electric vehicle is given to illustrate the optimization validity of the particle swarm optimization algorithm. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Controlling Diesel Engines: <s> Plug-in hybrid electric vehicles (PHEVs) differ from hybrid electric vehicles (HEVs) with their ability to use off-board electricity generation to recharge their energy storage systems. In addition to possessing charge-sustaining HEV operation capability, PHEVs use the stored electrical energy during a charge-depleting (CD) operating period to displace a significant amount of petroleum consumption. The choice of CD operating strategy directly influences the benefit derived from the PHEV technology. This paper describes the application of the particle swarm optimization (PSO) algorithm for the optimization of the control parameters in plug-in hybrid electric vehicles (HEV). In this study, based on CD operating strategy, the fitness function is defined so as to maximize the vehicle engine fuel economy (FE). The driving performance requirements are then considered as constraints. The results from the computer simulation show the effectiveness of the approach and improvement in fuel economy (FE) while ensuring that the vehicle performance is not sacrificed. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Controlling Diesel Engines: <s> Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers. <s> BIB003 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Controlling Diesel Engines: <s> Based on the working mechanism of the speed controlling system on the diesel engine, the control program of the diesel engine speed controlling system was built using the FNN-GA syncretic theory, control strategy of the diesel engine speed controlling system combined the fuzzy neural network (FNN) and the Genetic Algorithm (GA) was developed, the fuzzy reasoning logic and the dynamic simulation model of the diesel engine speed control system were established, these performance simulation of the diesel engine speed controlling system, such as, the increasing load and the decreasing load, were put forward by the FNN-GA syncretic control strategy. The simulation result showed that the FNN-GA syncretic control strategy could make the performance of the diesel engine speed controlling system parameters maintain optimal at all times, the FNN-GA syncretic strategy had the good resolving power and could control accurately the diesel engine controlling speed performance. <s> BIB004 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Controlling Diesel Engines: <s> This paper describes a Genetic Programming based automatic design methodology applied to the maintenance of a stable generated electrical output from a series-hybrid vehicle generator set. The generator set comprises a three-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 does not rely on computationally expensive structures, yet retains inherent disturbance 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. <s> BIB005 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Controlling Diesel Engines: <s> The diesel engine simulation model build by the GT-POWER software was tested and verified. The advance angle of injection, the opening angle of intake valve and the opening angle of exhaust valve was calculated to get the virtual test sample of diesel engine running under the rated condition. The optimization model was built based on the genetic algorithm, and three parameters were optimized aimed at the economic efficiency under the constraint of the maximum pressure in cylinder and the exhaust temperature; the error between the optimization and the simulation result was below 3%. <s> BIB006 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Controlling Diesel Engines: <s> Abstract In this paper, based on experiment result in a hydrogen fueled engine, pre-ignition and transforming process from pre-ignition to backfire of the hydrogen fueled engine was analyzed. Moreover, mechanism of pre-ignition and backfire of hydrogen fueled engines was studied by analyzing chain reaction. The analysis shows that the temperature, pressure and rate of pressure rise have great influence on pre-ignition and backfire. And based on thermodynamic relations of heat release rate, both pre-ignition and backfire were also analyzed. Finally, optimization control model which has multi-variables, multi-objectives and multi-constraints was established. And the simulation was carried out through the genetic algorithm. Results have shown that the excess air ratio and ignition advance angle can be adjusted by weighted coefficients to optimize the power output and restrict the abnormal combustion. Thus, a useful method is shown to resolve the contradictions between restricting the abnormal combustion and improving hydrogen fueled engine’s power output. <s> BIB007 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Controlling Diesel Engines: <s> Abstract With its obvious advantages, hydrogen is expected to be greatly used as a new energy, which can reduce vehicle emissions effectively and efficiently. Besides, it can be adopted as an alternative fuel for petroleum fuel of internal combustion engine. However, due to its unique physical and chemical characteristics, hydrogen often leads to abnormal combustion and decay of power, which can be effectively dealt with by optimal control. In this paper, thermodynamic analysis methods, like mass transfer and energy transfer, is combined with state space analysis methods of nonlinear control system, so that an optimal control model of nonlinear combustion control system for hydrogen fueled engines can be established. Through illustrating the inner relations between combustion and energy transfer of hydrogen fueled engines and operating parameter, state parameter and performance index, this paper provides theoretical analysis and found experimental research for promoting abnormal combustion and performance index after controlling operating parameters optimally and changing state parameters of the cylinder. Based on nonlinear programming theory and multi-objective genetic algorithm, the methods of establishing the optimal control model and carrying out the optimal value of operating parameters are shown in this paper. What’s more, some new ways are proposed to integrate the optimal control aspects of multi-variate, multiple targets and multiple constraints for hydrogen fueled engines by changing the multi-objective and multi-constraint optimal control into the single-target integrated optimal control. <s> BIB008
Diesel engine control parameters were optimized in  , where a modified multiobjective PSO and a crossover approach were adopted. The authors addressed the optimization of the BSFC, exhaust gas emission, and soot. An engine speed control system of a diesel engine was developed in BIB004 , where a fuzzy neural network system was used for the controller and a genetic algorithm was used to optimize the controller. In BIB006 , the advance angle of injection, the opening angle of intake valve and the opening angle of exhaust valve of a diesel engine were calculated to get the virtual test sample of the engine. They then employed a genetic algorithm to optimize three parameters aiming to improve the economic efficiency under the constraint of the maximum pressure in cylinder and the exhaust temperature BIB006 . 2) Controlling Hybrid Electric Engines: Hybrid electric engines benefit both from an internal combustion engine and an electric engine. If electric power is available the vehicle uses the electricity, and when it is not the petrol fuel is used. One important elements in improving the performance of hybrid electric vehicles is the careful selection of the control strategy parameters of the engine, which influences the fuel economy and emissions. To optimize the control parameters of a hybrid electric engine, a PSO algorithm was used in BIB001 . Management of the energetic flows in a hybrid vehicle is also a matter of importance. In order to maximize the use of the electric engine of a Toyota Prius engine while minimizing the use of the internal combustion one, increase the driving pleasure, and reduce emissions and noise, a genetic algorithm was employed in  , which finds the optimal parameters of the control system of the engine. Maintaining a stably generated electrical output in a hybrid vehicle generator set is a crucial task. The generator set consists of a three-phase ac generator, the output of which is rectified to dc. The system time delay makes the control integration of the engine/generator combination more complex and is usually solved by model predictive design methods. The predictive methods add computational complexity to the system and rely on accurate system and delay models. To overcome this delay problem, genetic programming was used to design a controller BIB005 , which does not rely on the computationally expensive structures and yet encompasses the disturbance rejection properties. Another type of electric vehicles are the plug-in hybrid electric vehicle which differs from hybrid electric vehicles in that they can use off-board electricity generation to recharge their energy storage systems. In order to optimize the control parameters in plug-in hybrid electric vehicles, a PSO algorithm was used in BIB002 , where the fitness function was defined so as to maximize the vehicle engine fuel economy. In this method, the driving performance requirements were considered as constraints. The method was tested via computer simulations and experimental results showed improvements in fuel economy. 3) Controlling Hydrogen-Fueled Engines: Hydrogenfueled engines have great advantages over other combustion engines; so it is expected to be widely used in future engines. However, due to the unique physical and chemical characteristics of the fuel, it often leads to abnormal combustion and decay of power unless the control parameters are carefully set. Meta-heuristic algorithms have often been used in controlling hydrogen-fueled engines. In BIB003 , an optimization control model was proposed for a hydrogen-fueled engine which employed a MOEA to optimize the relationship between power, economy, emission, and operating parameters of the engine. In BIB007 , after analyzing the mechanism of preignition and backfire of hydrogen-fueled engines, a multivariable, multiobjective, and multiconstraint genetic algorithm was proposed to optimize the control parameters of the engine. Through their experimental analysis, the authors showed that the method was capable of resolving the contradictions between restricting the abnormal combustion and improving hydrogen-fueled engine's power output. In BIB008 , in order to manage the abnormal combustion and performance index of hydrogen-fueled engines, a theoretical analysis on the engines was provided to find the optimal control parameters. Using nonlinear programming and a MOEA, a control model was then designed and the optimal value of the operating parameters were found. 4) Controlling Natural Gas/Hydrogen Engines: In order to optimize the control parameters of a natural gas and hydrogen-fueled engine, a flexible model of the engine was developed, and then a genetic algorithm was used to optimize the model  . Finally, a multiobjective optimization genetic algorithm is used to optimize the engine with the objective of reducing the CH 4 , CO, NO x , and BSFC.