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Predicting end-of-process variables in internal combustion engines, such as brake-specific fuel consumption or pollutant emissions, is crucial for engine design decisions. However, errors in common multi-layer-perceptron-based artificial neural network models often match the magnitude of the expected fuel consumption improvements, potentially leading to incorrect decisions. This study introduces a hybrid model where artificial neural networks replace engine block elements, while the 1D gas circuit and turbocharger models are retained. To enhance metamodel accuracy, two modifications are proposed: incorporating a pulsating mass flow rate in the exhaust line to capture pulsating effects missing in mean-value engine models and using a hierarchical arrangement of several multi-layer perceptrons instead of a parallel arrangement. The pulsating mass flow rate approach improves the accuracy of all tested configurations by replicating pulsating effects from a detailed 1D engine model. Meanwhile, the hierarchical arrangement refines predictions of end-of-process variables, such as fuel consumption, by increasing the total layers, with a minimal trade-off in the accuracy of other variables. These findings are validated using a metamodel derived from a calibrated 1D engine model in GT-Suite. The proposed methods are expected to enhance the accuracy of data-driven artificial neural network approaches, contributing to more reliable engine design optimization.

Two-stroke Aviation Piston Engines are multivariable systems with severe non-linear dynamics, making their modeling challenging for control engineers. Although many studies have been conducted on simplified modeling of four-stroke gasoline engines and large-scale two-stroke diesel engines for automobiles and ships, only a few have focused on the general modeling of small two-stroke aviation engines. Thus, extensive research on a general modeling method for two-stroke aviation engines is required. A general non-linear Mean Value Engine Model (MVEM) of two-stroke aviation piston engines is developed. Various parts of the model are represented by appropriate empirical equations that require little engine data and easily fit different engines. The objective is to develop a general and accurate method for rapid engine modeling that captures the main dynamics and can create control systems for two-stroke aviation piston engines. The model is validated using HIRTH-3203 and NU-57 measurement data, and the results show that the issues of fitting simplicity and general applicability are well addressed. Finally, the air-fuel ratio control based on the model predictive control method is carried out on the HIRTH-3203 MVEM, which demonstrates the effectiveness of the proposed MVEM in the controller design and evaluation. The proposed model may support the application of new control technologies, such as adaptive control and intelligent control, in the two-stroke aviation piston engine systems.

In this article, to meet the requirements of marine engine room simulator on both the simulation speed and simulation accuracy, a mean value engine model (MVEM) for the 7S80ME-C9.2 marine two-stroke diesel engine was developed and validated in the MATLAB/Simulink environment. In consideration of the significant influence of turbocharger compressor on both the engine steady state performance and transient response, a novel compressor model (mass flow rate and isentropic efficiency model) based on a previous study carried out by the first author was proposed with the aim of achieving satisfactory simulation accuracy within the whole engine operating envelope. The predictive and extrapolative capability of the proposed compressor model was validated by carrying out simulation experiments and analyzing the simulation results under steady state condition and during transient process. To make the traditional MVEM capable of predicting in-cylinder pressure trace, the cylinder pressure analytic model proposed by Eriksson and Andersson for the four-stroke SI (spark ignition) engine was adapted to the 7S80ME-C9.2 marine two-stroke diesel engine based on the characteristic of in-cylinder pressure trace of this type of engine and then coupled to the MVEM developed in this paper. Since there is no need to solve any differential equation as it is done in the 0-D model, the advantage of MVEM in running speed is not impaired. For achieving satisfactory simulation accuracy by using the analytic model, the model parameters were calibrated elaborately by using engine measured data and a 0-D model and the relevant tuning procedure was discussed in detail.

This paper proposes the extended state observer (ESO)-based active disturbance rejection control (ADRC) for the speed tracking of an autonomous vehicle. Uncertainties, both in the vehicle plant and in the sensors, such as nonlinear uncertainties due to the powertrain dynamics, variations in rolling resistance and air resistance, are all estimated in real-time by an extended state observer (ESO). Furthermore, a simple vehicle longitudinal dynamics model, including a mean value engine model (MVEM), is implemented to obtain the parameters in ADRC and design a feedforward controller to enhance the controller’s performance. The proposed controller is validated through CarSim®/Simulink® simulations and road tests. The simulation validates the adaptiveness of the proposed controller against the well-tuned proportional integral derivative (PID) controller, and the speed tracking error of the proposed controller is within 1.26% in simulation. Simulation results also show that fuel consumption can be improved by 3.6% by changing the accelerator pedal depth and positive rate. Finally, the road tests are completed under four kinds of road conditions, and the maximum tracking error is smaller than 0.5 km/h.

Mean-value engine models (MVEMs) are frequently applied in system-level simulations of vehicle powertrains. In particular, MVEMs are a common choice in engine simulators, where real-time execution is mandatory. In the case of real-time applications with prescribed, fixed sampling times, the use of explicit integration schemes is almost mandatory. Thus the stability of MVEMs is one of the main limitations when it comes to optimizing their performance. It is limited either by the minimum size of the gas volume elements or by the maximum integration time step. An innovative approach that addresses both constraints arises from the fact that the mass flow through the transfer elements of the MVEM is not modelled considering the quasi-steady assumption, but instead the mass-flow is calculated using a single transient momentum balance (TMB) equation. The proposed approach closely resembles phenomena in the physical model, since it considers both the flow-field history and the inertial effects arising from the time variation of the mass flow. It is shown in this paper that a consideration of the TMB equation improves the stability and/or the computational speed of the MVEMs, whereas it also makes it possible to capture physical phenomena in a more physically plausible manner.

Stoichiometric air-to-fuel ratio (lambda) control plays a significant role on the performance of three way catalysts in the reduction of exhaust pollutants of Internal Combustion Engines (ICEs). The classic controllers, such as PI systems, could not result in robust control of lambda against exogenous disturbances and modeling uncertainties. Therefore, a Model Predictive Control (MPC) system is designed for robust control of lambda. As an accurate and control oriented model, a mean value model of a Spark Ignition (SI) engine is developed to generate simulation data of the engine's subsystems. Based on the simulation data, two neural networks models of the engine are generated. The identified Multi-Layer Perceptron (MLP) neural network model yields small verification error compared with that of the adaptive Radial Base Function (RBF) neural network model. Consequently, based on the MLP engine's model, the MPC system is performed through a nonlinear constrained optimization within gradient descent algorithm. The performance of the MPC system is compared with that of a first order Sliding Mode Control (SMC) system. According to simulation results, the tracking accuracy of lambda by the MPC system is close to that of the SMC system. However, the MPC system results in considerably smoother injected fuel signal.

The international marine shipping industry is responsible for the transport of around 90% of the total world trade. Low-speed two-stroke diesel engines usually propel the largest trading ships. This engine type choice is mainly motivated by its high fuel efficiency and the capacity to burn cheap low-quality fuels. To reduce the marine freight impact on the environment, the International Maritime Organization (IMO) has introduced stricter limits on the engine pollutant emissions. One of these new restrictions, named Tier III, sets the maximum NOx emissions permitted. New emission reduction technologies have to be developed to fulfill the Tier III limits on two-stroke engines since adjusting the engine combustion alone is not sufficient. There are several promising technologies to achieve the required NOx reductions, Exhaust Gas Recirculation (EGR) is one of them. For automotive applications, EGR is a mature technology, and many of the research findings can be used directly in marine applications. However, there are some differences in marine two-stroke engines, which require further development to apply and control EGR.The number of available engines for testing EGR controllers on ships and test beds is low due to the recent introduction of EGR. Hence, engine simulation models are a good alternative for developing controllers, and many different engine loading scenarios can be simulated without the high costs of running real engine tests. The primary focus of this thesis is the development and validation of models for two-stroke marine engines with EGR. The modeling follows a Mean Value Engine Model (MVEM) approach, which has a low computational complexity and permits faster than real-time simulations suitable for controller testing. A parameterization process that deals with the low measurement data availability, compared to the available data on automotive engines, is also investigated and described. As a result, the proposed model is parameterized to two different two-stroke engines showing a good agreement with the measurements in both stationary and dynamic conditions.Several engine components have been developed. One of these is a new analytic in-cylinder pressure model that captures the influence of the injection and exhaust valve timings without increasing the simulation time. A new compressor model that can extrapolate to low speeds and pressure ratios in a physically sound way is also described. This compressor model is a requirement to be able to simulate low engine loads. Moreover, a novel parameterization algorithm is shown to handle well the model nonlinearities and to obtain a good model agreement with a large number of tested compressor maps. Furthermore, the engine model is complemented with dynamic models for ship and propeller to be able to simulate transient sailing scenarios, where good EGR controller performance is crucial. The model is used to identify the low load area as the most challenging for the controller performance, due to the slower engine air path dynamics. Further low load simulations indicate that sensor bias can be problematic and lead to an undesired black smoke formation, while errors in the parameters of the controller flow estimators are not as critical. This result is valuable because for a newly built engine a proper sensor setup is more straightforward to verify than to get the right parameters for the flow estimators.

This chapter describes mean value engine models (MVEM) that have applications in analysis and design of control and diagnosis systems. The first section provides an overview of sensors and actuators. The next five sections respectively discuss flow restriction models, throttle flow models, mass flow into the cylinders, volumes and intake manifold. The seventh section covers fuel path and air‐fuel ratio. Torque generation is the topic in the eighth and ninth sections, with in‐cycle torque variations in the former and MVEM torque in the latter. Thermal effects with engine‐out and exhaust temperatures as well as intercoolers are modeled next. Finally, the chapter discusses mechanics and modeling of the electronic throttle body.

The digital twin concept proves to be highly advantageous for smart ships as it allows the digital model to adapt in real time with changes in the physical ship. The mean value engine model (MVEM) is investigated with three different compressor mass flow rate models: a 2D interpolation model (2D), an experience-based M-JENSEN (MJ) model, and a physics-based Kang Song (KS) model. Each model is studied on a MAN 7S80ME engine with ABB A270 turbochargers. The root-mean-square errors (RMSE) for the test data are approximately 2.5 m 3 / s for the 2D model, 2.8 m 3 / s for the KS model, and 0.56 m 3 / s for the MJ model. For the training data, the RMSEs are around 0.0, 3.0, and 0.69 m 3 / s , respectively. When coupling each model to the MVEM, it is observed that the calculated values differ only in the turbocharger speed. The RMREs under steady-state processes are 0.54% for MVEM with the 2D model, 1.63% for MVEM with the KS model, and 0.48% for MVEM with the MJ model. Under dynamic processes where the engine load is changed from 100% to 80%, the RMREs are 1.59%, 2.40%, and 1.58%, respectively. Among the studied models, the 2D model exhibits instability, the MJ model shows the highest accuracy, while the KS model performs the worst. However, all models demonstrate comparable results when used in the MVEM. Moreover, the physics-based KS model is a favorable choice for digital twin applications due to its excellent extrapolation ability and low dependence on measured data.

Development of intelligent ships requires marine diesel engine simulation models of high accuracy and fast response. In addition, with advent of tighter shipping air emissions regulations, such models are required to have emission prediction capabilities. In this article, such a model was developed and validated for a 30,000-ton bulk carrier main engine using MATLAB/Simulink. The simulation is based on mean value model, which predicts both the steady-state and dynamic performance of the engine. The results show that the steady-state performance parameters of the main engine are predicted within 2.2% error, and the exhaust emissions parameters are predicted within 7% error as compared to the bench test data from the engine manufacturer. The Maximum Continuous Rating (MCR) points at 100%, 75%, 50% and 25% of the E3 duty cycle were investigated with emphasis according to the diesel propulsion characteristics. In dynamic simulation, it is found that the compressor pressure fluctuation is greater than that of the exhaust pressure with the load variation. Furthermore, the compressor and the exhaust pipe have a similar temperature drop value (about 60 K) when the engine load changes from 100% to 50% MCR, and the exhaust pipe temperature fluctuation is more significant when the load varies from 50% to 25% MCR. The above results show the model’s good transient capability in simulating the dynamic characteristics of the engine. This model can be used especially for the development and control of marine diesel engines in intelligent ships as well as training-oriented marine engine and ship simulators.