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The aim of this thesis is establishment of an overall framework of a novel control parameter optimization of automotive engine. Today, control parameters of an automotive engine have to be adjusted adequately and simultaneously to achieve plural criteria such as environmental emissions, fuel-consumption and engine torque. This process is called 'engine calibration'. Because many electronic control devices have been adopted for engine to satisfy these objectives, the complexity of engine calibration is increasing year to year. Recent progress in automatic control and instrumentation provides a smart environment called Hardware In the Loop Simulation (HILS) for engine calibration. In addition, Response Surface Methodology (RSM) based on statistical model is currently employed as the optimization method. Nevertheless, this approach is complicated by adequate model selection, precise model construction, and close model validation to confirm the precision of the model output. To cope with these problems, we noticed experiment-based optimization via HILS environment based on Multi-objective Evolutionary Algorithms (MOEAs), that is expected to be a powerful optimization framework for real world problems such as engineering design, as another automatic calibration approach. In experiment-based optimization, the parameters of a real system are optimized directly by optimization techniques in real time through experimentation. In this thesis, this approach is called Experiment-Based Evolutionary Multi-objective Optimization (EBEMO) and it is proposed as a novel automatic engine calibration technique. This approach can release us from burdens of model selection, construction, and validation. When using this technique, calibration can be done immediately after specifications have been changed after optimization. Hence, EBEMO promises to be an effective approach to automatic engine calibration. However, since conventional MOEAs face several difficulties, it is not easy to apply it to real engines. On the one hand, deterioration factors of the search performance of MOEAs in real environments have to be considered. For example, the observation noise of sensors included in output interferes with convergence of MOEAs. In addition, transient response by parameter switching also has similar harmful effects. Moreover, the periodicity of control inputs increase the complexity of the problems. On the other hand, the search time of MOEAs in real environments has to reduce because MOEAs require a tremendous number of evaluations. While we can obtain many measurements with HILS, severe limitations in the number of fitness evaluations still exist because the real experiments need real-time evaluations. Therefore, it is difficult to obtain a set of Pareto optimal solutions in practical time with conventional MOEAs. Additionally, plural MOPs defined by plural operating conditions of map-based controllers has to be optimized. In this thesis, to overcome the difficulties and to make EBEMO using the HILS environment feasible, five techniques are proposed. Each technique is developed through problem formulation, and their effectiveness are confirmed via numerical and real engine experiments. First, observation noise handling technique for MOEAs is considered. Because observation noise deteriorates the search ability of MOEAs, a memorybased fitness estimation method to exclude observation noise is introduced. Then, a crossover operator for periodic functions is proposed. Periodicity exists in engineering problems and leads to harmful effects on the performance of evolutionary algorithms. Moreover, the influence of transient response caused by parameter switching for dynamical systems is considered. In order to solve this problem, a solver of traveling salesman problems is used to determine the evaluation order of individuals. In addition, Pre-selection as acceleration method of MOEAs is proposed. In this technique, the generated offspring are pre-evaluated in the approximation model made by the search history, and then the promising offspring are evaluated in a real environment. Finally, parameterization of multi-objective optimization problems is considered. In engine calibration for maps, optimal control parameters have to be obtained at each operating condition such as engine speed and torque. This problem can be formulated in a form that needs to solve all of the plural multi-objective optimization problems defined by plural conditional variables. To solve this problem effectively, an interpolative initialization method is proposed. Through the real engine experiments, it was confirmed that EBEMO can achieve a practical search accuracy and time by using proposed techniques. In conclusion, the contribution of EBEMO for engine calibration is discussed. Additionally, the directions for future work are outlined. / Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(情報学) / 甲第14187号 / 情博第320号 / 新制||情||61(附属図書館) / 26493 / UT51-2008-N504 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 喜多 一, 教授 酒井 徹朗, 教授 片井 修 / 学位規則第4条第1項該当

no / The complexity of powertrain calibration has increased significantly with the development and introduction of new technologies to improve fuel economy and performance while meeting increasingly stringent emissions legislation with given time and cost constraints. This paper presents research to improve the model-based engine calibration optimization using an integrated sequential Design of Experiments (DoE) strategy for engine mapping experiments. This DoE strategy is based on a coherent framework for a model building - model validation sequence underpinned by Optimal Latin Hypercube (OLH) space filling DoEs. The paper describes the algorithm development and implementation for generating the OLH space filling DoEs based on a Permutation Genetic Algorithm (PermGA), subsequently modified to support optimal infill strategies for the model building - model validation sequence and to deal with constrained non-orthogonal variables space. The development, implementation and validation of the proposed strategy is discussed in conjunction with a case study of a GDI engine steady state mapping, focused on the development of an optimal calibration for CO₂ and particulate number (Pn) emissions. The proposed DoE framework applied to the GDI engine mapping task combines a screening space filling DoE with a flexible sequence of model building - model validation mapping DoEs, all based on optimal DoE test plan augmentation using space filling criteria. The case study results show that the sequential DoE strategy offers a flexible way of carrying out the engine mapping experiments, maximizing the information gained and ensuring that a satisfactory quality model is achieved.

The calibration of modern internal combustion engines requires ever improving measurement data quality such that they comply with increasingly stringent emissions legislation. This study establishes methodology and a software tool to improve the quality of steady-state emissions measurements from engine dynamometer tests. Literature shows state of the art instrumentation are necessary to monitor the cycle-by-cycle variations that significantly alter emissions measurements. Test methodologies that consider emissions formation mechanisms invariably focus on thermal transients and preconditioning of internal surfaces. This work sought data quality improvements using three principle approaches. An adapted steady-state identifier to more reliably indicate when the test conditions reached steady-state; engine preconditioning to reduce the influence of the prior day’s operating conditions on the measurements; and test point ordering to reduce measurement deviation. Selection of an improved steady-state indicator was identified using correlations in test data. It was shown by repeating forty steady-state test points that a more robust steady-state indicator has the potential to reduce the measurement deviation of particulate number by 6%, unburned hydrocarbons by 24%, carbon monoxide by 10% and oxides of nitrogen by 29%. The variation of emissions measurements from those normally observed at a repeat baseline test point were significantly influenced by varying the preconditioning power. Preconditioning at the baseline operating condition converged emissions measurements with the mean of those typically observed. Changing the sequence of steady-state test points caused significant differences in the measured engine performance. Examining the causes of measurement deviation allowed an optimised test point sequencing method to be developed. A 30% reduction in measurement deviation of a targeted engine response (particulate number emissions) was obtained using the developed test methodology. This was achieved by selecting an appropriate steady-state indicator and sequencing test points. The benefits of preconditioning were deemed short-lived and impractical to apply in every-day engine testing although the principles were considered when developing the sequencing methodology.

Indiana University-Purdue University Indianapolis (IUPUI) / Stringent regulatory requirements and modern diesel engine technologies have engaged automotive manufacturers and researchers in accurately predicting and controlling diesel engine-out emissions. As a result, engine control systems have become more complex and opaquer, increasing the development time and costs. To address this challenge, Model-based control methods are an effective way to deal with the criticality of the system study and controls. And physics-based combustion engine modeling is a key to achieve it. This thesis focuses on development and validation of a physics-based model for both engine and emissions using model-based design tools from MATLAB & Simulink. Engine model equipped with exhaust gas circulation and variable geometry turbine is adopted from the previously done work which was then integrated with the combustion and emission model that predicts the heat release rates and NOx emission from engine. Combustion model is designed based on the mass fraction burnt from CA10 to CA90 and then NOx predicted using the extended Zeldovich mechanism. The engine models are tuned for both steady state and dynamics test points to account for engine operating range from the performance data. Various engine and combustion parameters are estimated using parameter estimation toolbox from MATLAB and Simulink by applying the least squared solver to minimize the error between measured and estimated variables. This model is validated against the virtual engine model developed in GT-power for Cummins 6.7L turbo diesel engine. To account for the harmonization of the testing cycles to save engine development time globally, a world harmonized stationary cycle (WHSC) is used for the validation. Sub-systems are validated individually as well as in a loop with a complete model for WHSC. Engine model validation showed promising accuracy of more than 88.4 percent on average for the desired parameters required for the NOx prediction. NOx estimation is accurate for the cycle except the warm-up and cool-down phase. However, NOx prediction during these phases is limited due to actual NOx measured data for tuning the model for real-time NOx estimation. Results are summarized at the end to compare the trend of NOx estimation from the developed combustion and emission model to show the accuracy of in-cylinder parameters and required for the NOx estimation.

Yes / This paper presents the development and implementation of an Analytical Target Cascading (ATC) Multi-disciplinary Design Optimisation (MDO) framework for the steady state engine calibration optimisation problem. The case is made that the MDO / ATC offers a convenient framework for the engine calibration optimisation problem based on steady state engine test data collected at specified engine speed / load points, which is naturally structured on 2 hierarchical levels: the “Global” level, associated with performance over a drive cycle, and “Local” level, relating to engine operation at each speed / load point. The case study of a gasoline engine equipped with variable camshaft timing (VCT) was considered to study the application of the ATC framework to a calibration optimisation problem. The paper describes the analysis and mathematical formulation of the VCT calibration optimisation as an ATC framework, and its Matlab implementation with gradient based and evolutionary optimisation algorithms. The results and performance of the ATC are discussed comparatively with the conventional two-stage approach to steady state calibration optimisation. The main conclusion from this research is that ATC offers a powerful and efficient approach for engine calibration optimisation, delivering better solutions at both “Global” and “Local” levels. Further advantages of the ATC framework is that it is flexible and scalable to the complexity of the calibration problem, and enables calibrator preference to be incorporated a priori in the optimisation problem formulation, delivering important time saving for the overall calibration development process. / The research work presented in this paper was funded by UK Technology Strategy Board (TSB) through the CREO (Carbon Reduction through Engine Optimisation) project.

No / This paper presents the development of an Analytical Target Cascading (ATC) Multidisciplinary Design Optimization (MDO) framework for a steady-state engine calibration optimization problem. The implementation novelty of this research is the use of the ATC framework to formulate the complex multi-objective engine calibration problem, delivering a considerable enhancement compared to the conventional 2-stage calibration optimization approach [1]. A case study of a steady-state calibration optimization of a Gasoline Direct Injection (GDI) engine was used for the calibration problem analysis as ATC. The case study results provided useful insight on the efficiency of the ATC approach in delivering superior calibration solutions, in terms of “global” system level objectives (e.g. improved fuel economy and reduced particulate emissions), while meeting “local” subsystem level requirements (such as combustion stability and exhaust gas temperature constraints). The ATC structure facilitated the articulation of engineering preference for smooth calibration maps via the ATC linking variables, with the potential to deliver important time saving for the overall calibration development process.

The calibration of modern internal combustion engines requires ever improving measurement data quality such that they comply with increasingly stringent emissions legislation. This study establishes methodology and a software tool to improve the quality of steady-state emissions measurements from engine dynamometer tests. Literature shows state of the art instrumentation are necessary to monitor the cycle-by-cycle variations that significantly alter emissions measurements. Test methodologies that consider emissions formation mechanisms invariably focus on thermal transients and preconditioning of internal surfaces. This work sought data quality improvements using three principle approaches. An adapted steady-state identifier to more reliably indicate when the test conditions reached steady-state; engine preconditioning to reduce the influence of the prior day’s operating conditions on the measurements; and test point ordering to reduce measurement deviation. Selection of an improved steady-state indicator was identified using correlations in test data. It was shown by repeating forty steady-state test points that a more robust steady-state indicator has the potential to reduce the measurement deviation of particulate number by 6%, unburned hydrocarbons by 24%, carbon monoxide by 10% and oxides of nitrogen by 29%. The variation of emissions measurements from those normally observed at a repeat baseline test point were significantly influenced by varying the preconditioning power. Preconditioning at the baseline operating condition converged emissions measurements with the mean of those typically observed. Changing the sequence of steady-state test points caused significant differences in the measured engine performance. Examining the causes of measurement deviation allowed an optimised test point sequencing method to be developed. A 30% reduction in measurement deviation of a targeted engine response (particulate number emissions) was obtained using the developed test methodology. This was achieved by selecting an appropriate steady-state indicator and sequencing test points. The benefits of preconditioning were deemed short-lived and impractical to apply in every-day engine testing although the principles were considered when developing the sequencing methodology.

Yes / The work presented in this paper is motivated by a complex multivariate engineering problem associated with engine mapping experiments, which require efficient Design of Experiment (DoE) strategies to minimise expensive testing. The paper describes the development and evaluation of a Permutation Genetic Algorithm (PermGA) to support an exploration-based sequential DoE strategy for complex real-life engineering problems. A known PermGA was implemented to generate uniform OLH DoEs, and substantially extended to support generation of Model Building–Model Validation (MB-MV) sequences, by generating optimal infill sets of test points as OLH DoEs, that preserve good space filling and projection properties for the merged MB + MV test plan. The algorithm was further extended to address issues with non-orthogonal design spaces, which is a common problem in engineering applications. The effectiveness of the PermGA algorithm for the MB-MV OLH DoE sequence was evaluated through a theoretical benchmark problem based on the Six-Hump-Camel-Back (SHCB) function, as well as the Gasoline Direct Injection (GDI) engine steady state engine mapping problem that motivated this research. The case studies show that the algorithm is effective at delivering quasi-orthogonal space-filling DoEs with good properties even after several MB-MV iterations, while the improvement in model adequacy and accuracy can be monitored by the engineering analyst. The practical importance of this work, demonstrated through the engine case study, also is that significant reduction in the effort and cost of testing can be achieved. / The research work presented in this paper was funded by the UK Technology Strategy Board (TSB) through the Carbon Reduction through Engine Optimization (CREO) project.

Automotive engines are becoming increasingly technically complex and associated legal emissions standards more restrictive, making the task of identifying optimum actuator settings to use significantly more difficult. Given these challenges, this research aims to develop a process for engine calibration optimisation by exploiting advanced mathematical methods. Validation of this work is based upon a case study describing a steady-state Diesel engine calibration problem. The calibration optimisation problem seeks an optimal combination of actuator settings that minimises fuel consumption, while simultaneously meeting or exceeding the legal emissions constraints over a specified drive cycle. As another engineering target, the engine control maps are required as smooth as possible. The Multidisciplinary Design Optimisation (MDO) Frameworks have been studied to develop the optimisation process for the steady state Diesel engine calibration optimisation problem. Two MDO strategies are proposed for formulating and addressing this optimisation problem, which are All At Once (AAO), Collaborative Optimisation. An innovative MDO formulation has been developed based on the Collaborative Optimisation application for Diesel engine calibration. Form the MDO implementations, the fuel consumption have been significantly improved, while keep the emission at same level compare with the bench mark solution provided by sponsoring company. More importantly, this research has shown the ability of MDO methodologies that manage and organize the Diesel engine calibration optimisation problem more effectively.