Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> We study the combinatorial optimization task of choosing the smoothest map from a given family of maps, which is motivated from motor control unit calibration. The problem is of a particular interest because of its characteristics: it is NP-hard, it has a direct and important industrial application, it is easy-to-state and it shares some properties of the wellknown Ising spin glass model. Moreover, it is appropriate for the application of randomized algorithms: for local search heuristics because of its strong 2-dimensional local structure, and for Genetic Algorithms since there is a very natural and direct encoding which results in a variable alphabet. We present the problem from two points of view, an abstract view with a very simple definition of smoothness and the real-world application. We run local search, Genetic and Memetic Algorithms. We compare the direct encoding with unary and binary codings, and we try a 2-dimensional encoding. For a simple smoothness criterion, the Memetic Algorithm clearly performs best. However, if the smoothness citerion is more complex, the local search needs many function evaluations. Therefore we prefer the pure Genetic Algorithm for the application. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> Several combinatorial optimisation problems occur during the calibration of combustion engines. In this work, it is shown that three particular process steps benefit from genetic algorithms: First, the D-optimal experimental design is improved by the use of an appropriate crossover operator. Thereby the heuristics DETMAX or k-exchange perform a local search. The second problem concerns the optimal test bed scheduling for a more efficient and thus less expensive execution of measurements. This higher dimensional variant of the Travelling Salesman Problem (TSP) is solved by a hybrid genetic algorithm using adjacency coded individuals and a 2-opt heuristic as a local search. Finally, well-defined look-up tables, that lead to smooth maps, are composed from multiple valued look-up tables. Again a genetic algorithm finds better solutions than local search heuristics. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> The engine calibration procedure developed in Renault involves solving a constrained global optimization problem. This is a medium scale problem with a great number of linear and non linear constraints and containing a large number of local minima. Therefore, we use a strategy combining global and local optimization. We propose a new global stochastic optimization algorithm called Multistoch generating a fixed number of starting points for a local optimization procedure (see [2]). These set of points are called a grid. This algorithm although different from simulated annealing (see [3]) involves a gibbs measure. This measure is used to select a point in the grid. Around this selected point we generate a new candidate point which according to his value will modify the grid. After a description of the engine calibration problem some theorical and numerical results on the algorithm are presented. <s> BIB003 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> A new approach for automotive engine torque and air-fuel ratio control is presented in this paper. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in the present project. Adaptive critic designs are defined as designs that approximate dynamic programming in the general case, i.e., approximate optimal control over time in noisy, nonlinear environment. The present work uses a system, called "critic", to approximate the cost function in dynamic programming and thus to achieve optimal control. The goals of the present learning control design for automotive engines are emissions reduction and maintenance of optimal performance under various operating conditions. Using the data obtained from a test vehicle, we first develop a neural network model of the engine. A neural network controller is then designed based on the idea of approximate dynamic programming to achieve optimal control. In the simulation studies, the initial controller is trained using the neural network engine model developed rather than the actual engine. We have developed and tested self-learning neural network controllers for both engine torque and exhaust airfuel ratio control. For both control problems, good transient performance of the neural network controller has been observed. A distinct feature of the present technique is the controller's real-time adaptation capability based on real vehicle data which allows the neural network controller to be further refined and improved in real-time vehicle operation through continuous learning. <s> BIB004 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> Many combinatorial optimization problems occur in the calibration of modern automotive combustion engines. In this contribution, simple hill-climbing algorithms (HCs) for three special problems are incorporated in Memetic Algorithms (MAs) using specific crossover and mutation operators. First, the k-exchange algorithm as a well known technique of D-optimal design of experiments (DOE) is improved. Second, a (near-)optimum test bed measurement scheduling (TBS) as a variant of the traveling salesman problem (TSP) is calculated, and third, the final design of look-up tables (LTD) for electronic control units is optimized. It is shown that in all cases MAs that work on locally optimal solutions calculated by the corresponding HCs significantly improve former results using Genetic Algorithms (GAs). The algorithms have been successfully applied at BMW Group Munich. <s> BIB005 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> This paper proposes a multi-objective structure for the optimization of an engine control unit mapping. In this way, first an integrated engine-vehicle model is developed. The objective functions for the optimization problem can be defined from this model. After describing the structure of the optimization problem, two different multi-objective genetic algorithms, namely distance-based Pareto genetic algorithm and non-dominated sorting genetic algorithm (together with entropy-based multi-objective genetic algorithm), are proposed and implemented. The results demonstrate the superiority of this computerized structure to the manual mapping methods and also more generality of the multi-objective methods compared to single-objective ones. <s> BIB006 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> Since multi-objective optimizations are becoming more important in engine calibration, the paper investigates multi-objective genetic algorithms in the application of engine optimization. Although several multi-objective genetic algorithms have been developed and some have been applied successfully in the automotive industry, it is difficult to determine which multi-objective genetic algorithm outperforms others in finding the set of optimal solutions or Pareto-optimal front in a practical multi-objective optimization problem. Based on some widely used multi-objective genetic algorithms, the paper proposes a combined scheme to deal with the difficulties in finding the optimal solution set during the engine calibration process. In the proposed approach the real-coded representation is employed in the genetic algorithm and the elitist strategy is applied for each multi-objective genetic algorithm used. To assess the proposed approach, two computational examples are given to minimize the brake specific fuel ... <s> BIB007 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved. <s> BIB008 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> Abstract The automotive industry is, based on the sales, the most important industry section in Germany and other countries. Because of many various boundary conditions, like CO 2 and fuel reduction, the calibration of the engine control unit is growing up for the development of the whole system automotive. Traditional calibration methods fail because of the increasing complexity of the optimization tasks and the huge measurement effort. A one dimensional engine process simulation in combination with a physical combustion model can significantly contribute to essential parts of engine calibration. For an accurate reproduction of the reality the simulation model must be calibrated. Factors are used as calibration parameters, which take influence on various combustion values. With the help of an automatic optimization routine the time effort can be reduced significantly for the adjustment with constant or increasing accuracy. Therefore, the main target of this contribution is the improvement of the simulation and optimization techniques. For this aspect an evolutionary algorithm, which is a combination of a genetic algorithm and an evolutionary strategy, is used for the optimzation task. Evolutionary optimization methods are stochastic searching methods, which are leaned on the naturally biological evolution. They work simultaneously with a number of potential solutions and they are variable, robust and powerful. They give the opportunity to solve complex and multi-criteria problems in a reasonable time. Further advantages of this approach are the better results and the time saving. The presented evolutionary algorithm for the automatic calibration of simulation models for the virtual engine application has been evaluated by simulations. <s> BIB009 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> This study aims to determine optimal biodiesel ratio that can achieve the goals of fewer emissions, reasonable fuel economy and wide engine operating range. Different advanced machine learning techniques, namely ELM (extreme learning machine), LS-SVM (least-squares support vector machine) and RBFNN (radial-basis function neural network), are used to create engine models based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. Based on the engine models, two optimization methods, namely SA (simulated annealing) and PSO (particle swarm optimization), are employed and a flexible objective function is designed to determine the optimal biodiesel ratio subject to various user-defined constraints. A case study is presented to verify the modeling and optimization framework. Moreover, two comparisons are conducted, where one is among the modeling techniques and the other is among the optimization techniques. Experimental results show that, in terms of the model accuracy and training time, ELM with the logarithmic transformation is better than LS-SVM and RBFNN with/without the logarithmic transformation. The results also show that PSO outperforms SA in terms of fitness and standard deviation, with an acceptable computational time. <s> BIB010 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> In the automotive industry the calibration of the engine control unit is getting more and more complex because of many various boundary conditions, like the demand on CO 2 and fuel reduction. One important calibration problem is the parameter estimation for the model of the intake system of the combustion engine. This system is modeled by a physically motivated system, which can be parameterized by black-box models, like neural nets and characteristic diagrams, whose parameters must be set by an intelligent optimizer. Further, two contradictory aims must be considered and the engineer expects at the end of the optimization a pareto-front, where he can choose the best settings for the application from the pareto-optimal parameter estimations. To solve this multi-criteria optimization task an evolutionary algorithm is used, which is a combination of a genetic algorithm and an evolutionary strategy. This evolutionary algorithm is like all other stochastic searching methods leaned on the naturally biological evolution. It combines the well-known covariance matrix adaption for the mutation of the individuals with the S-metric selection for the multi-criteria fitness assignment of the individuals. It also improves these combination by the use of many subpopulations, which work parallel on various clusters, and by the use of an intelligent DoE-strategy for the initialization of the start individuals. With these improvements the developed evolutionary algorithm can easily fit the model of the intake system to test bed measurements and can provide the user a pareto-optimal set of parameters, on which he can choose on his own that ones, which he find most plausible. <s> BIB011 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Control Unit Calibration <s> An Atkinson cycle engine with geometrical compression ratio (GCR) of 12.5 has been designed by maximizing fuel economy at full load operating conditions based on the Artificial Neural Network Method [1]. However, the Atkinson cycle engine generally operates at part load conditions especially in the middle to high load range. Optimization of the fuel economy for part load is more important in reducing the total fuel consumption. The Atkinson cycle engine applies the load control strategy that combines the intake valve closure (IVC) timing and electrically throttling control (ETC), which has an impact to the fuel economy. Moreover, the exhaust valve opening (EVO) timing, spark angle (SA) and air–fuel-ratio (AFR) also affect the fuel economy. If calibrating these operating variables over the entire operating range through experiments, the difficulty and cost will become a big issue. A physical model based optimization scheme by coupling MATLAB genetic algorithm (GA) and 1-D GT-Power simulation models of the Atkinson cycle engine are proposed. The GT-Power models were improved to accurately simulate the part load conditions, by calibrating parameters of the combustion and heat transfer sub-models using experimental data taken at various speed–load points covering the entire operating range. The fuel economy was optimized based on the part-load calibrated GT-Power models using the Genetic Algorithm. After each speed–load point was optimized, the control maps for the IVC timings, SA, etc. were obtained. Then these numerically optimized control maps were input into the engine control unit (ECU) as the initial values of the engine calibration, which were further experimentally optimized. The experimental results show that the part-load GT-Power models have sufficient prediction accuracy, with maximal error of 8.5%. After optimized by GA, the fuel economy was greatly improved over the operating range, with the maximal improvement up to 7.67%. <s> BIB012
In engine calibration, the optimization cycle is decomposed into a set of stationary operation points of the engine based on its speed and torque. Then for each of these operating points the best parameters are found. The functions defining these parameters on the whole engine operating points are called the engine maps. Choosing the smoothest map from a given family of maps in engine control unit calibration is an optimization problem that belongs to the class of NP-hard problems BIB001 . The characteristic of this problem makes it appropriate for local search heuristics and other meta-heuristic algorithms. In the first major study in this area a memetic algorithm, which combines a genetic algorithm and local search was adopted in BIB001 to solve the problem. Apart from using local search, the operators of genetic algorithms, like crossover, can also be manipulated to improve the performance of the algorithm. For example, in BIB002 and BIB005 , it was shown that three process steps in engine calibration could be regarded as optimization problems that would benefit from genetic algorithms. The first step is the D-optimal experimental design, which is optimized with a local search algorithm and an appropriate crossover operator. The second step is the optimal test bed scheduling which can be seen as a traveling salesman problem and was solved by a hybrid genetic algorithm with 2-opt heuristic local search. And finally, the third problem is the look-up table which was optimized through a genetic algorithm. This method was successfully applied to a BMW engine in Munich BIB002 . Combining global and local optimization, a new hybrid algorithm called Multistoch was proposed for engine calibration in BIB003 . In this method, a fixed number of starting points, called grid, are generated for a local optimization procedure, and using a Gibbs measure, a point in the grid is selected. This selected point is then used to generate a new candidate point according to its fitness and modify the grid. The other way of improving the performance of the algorithm is to benefit from statistical properties of the solutions. The optimization of the engine control unit mapping was targeted in BIB006 , where a model of an integrated engine vehicle was developed. Then a distance-based Pareto genetic algorithm and a nondominated sorting genetic algorithm (NSGA), together with an entropy-based multiobjective EA (MOEA), were proposed and implemented on the engine model to optimize the engine with the objectives of reducing emission and fuel consumption. The results demonstrated the superiority of this method over the manual mapping methods. In BIB009 , a combination of a genetic algorithm and an evolution strategy was used for the automatic calibration of a virtual engine of a BMW engine. Combining different algorithms is a way of taking advantage of both the algorithms. One effort in this area is to take advantage of different multiobjective evolutionary optimization algorithms. A combined scheme was proposed in BIB007 , which dealt with the difficulties in finding the optimal solution set during the engine calibration process. The method used a real-coded representation in the genetic algorithm and an elitist strategy was applied to each MOEA. The algorithms were applied to a calibration problem with the goal of minimizing the brake specific fuel consumption (BSFC) and to maximize the output power torque simultaneously. Wu et al. BIB007 reported a good performance for their proposed method. Some researchers combine two different optimization algorithms, so the weaknesses of one algorithm is covered by the advantages of the other one. In traditional engine compensation map calibration, the parameters are normally set by trial and error as it is almost impossible to derive the exact mathematical model of the system. A new multiinput/output least-squares support vector committee machine was proposed by Wong et al. BIB010 to construct the engine compensation control system models on some experimental data. The authors employed a nonlinear regression algorithm to reduce the dimension of the parameters of the engine control system before the modeling stage. Then genetic algorithms and PSO algorithms were applied to parameter optimization to determine the optimal calibration maps. One important problem in calibration is the parameter estimation for the model of the intake system of engine. The problem is multiobjective, which was tackled in BIB011 by a new combination of a genetic algorithm and an evolution strategy. The hybrid algorithm combines the covariance matrix adaptation of the mutation with the S-metric selection for the multicriteria fitness assignment of the individuals. The S-metric selection is a time-consuming process, specially when the problem has many objectives. However, the engine calibration problem is even more time consuming, which may take months or even years. In engine calibration, it is usually gathering the data from the real engine that is the most time consuming and financially expensive task, so employing S-metric might not significantly affect the total development time. The algorithm also uses a number of sub-populations and an intelligent DoE-strategy for the population initialization. In Atkinson cycle engines, optimizing the fuel economy for part load is more important in reducing the total fuel consumption. In such engines, the intake valve closure timing, electrically throttling control, exhaust valve opening timing, spark angle, and air-to-fuel ratio affect the fuel economy. In order to optimize the fuel efficiency of an Atkinson cycle engine, in BIB012 , some experimental data taken from various speed-load points covering the entire operating range were used to generate a neural model of the engine. Then a genetic algorithm was used to find the best parameters offering the most efficient fuel consumption. The experimental results demonstrated a greatly improved fuel economy across the operating range. In engine calibration, not only the performance and emission under the current state have to be taken into account, but also maintaining the performance under various operating conditions is a matter of importance. Considering this, an adaptive self-learning control method was proposed in BIB004 and BIB008 , which was based on heuristic dynamic programming. This paper used data from a test vehicle with a V8 engine to train a neural network controller which was used for controlling engine torque and exhaust air-to-fuel ratio.