Control Of Internal Combustion Engines Research Articles

IMPROVEMENT OF INTELLIGENT INTERNAL COMBUSTION ENGINES

Research investigations of the solutions of internal combustion engine control problems based on artificial intelligence systems are quite relevant. The power of ship devices and mechanisms is increasing, the requirements for accuracy, reliability, speed and other indicators of the quality of control processes are increasing, increased operational requirements related to economy and efficiency of ship systems and equipment. Currently, there are a lot of studies which are successfully performed to create the installations with a higher degree of automation – adaptive (intelligent) internal combustion engines, including a class of marine diesel installations. This term means the Engine controlled by adaptive automatic control systems with elements of artificial intelligence that can apprehend and analyses quite complicated and changeable ambient and take a decision. However, the problematic issues of adapting a piston engine in operating conditions are still under study and theoretical conceptualization. In this article, a study of the scientific and technical problem of improving the operational qualities of intelligent marine diesel installations is carried out. The problem has a great technical importance and requires scientific substantiation of the directions of both improving the operational qualities and reliability of existing models, and identifying ways to modernize existing structures of marine diesel installations. Кeywords: internal combustion engines, marine diesel, intelligent engine, adaptive systems, adaptive motors, microprocessor controls, controlling algorithm, neural networks.

Using fully variable valve control for cylinder-individual reference tracking with emission-optimal internal exhaust gas recirculation

A fully variable valve train significantly increases the degree of freedom of the control of internal combustion engines. Cylinder deactivation, thermal management, alternative combustion strategies, and minimized pumping losses are just a few examples enabled by freely adaptable intake and exhaust valve timings. This paper presents a method to achieve the accurate tracking of load trajectories under stoichiometric conditions. A feedback controller is designed with a mixed-sensitivity H∞ synthesis method. The underlying system plant is modeled by a combination of a mean-value model of the cylinder-internal processes and a neural network to map the correlation between valve timings and cylinder charge. All experiments are conducted on a test bench with a spark-ignited engine equipped with an internally developed fully variable valve train called FlexWork. With this method, a mean absolute error of 0.07bar in indicated mean pressure and of 0.009 in air–fuel equivalence ratio is achieved for the tracking of the reference trajectory. Furthermore, a cost function dependent online optimization of the internal exhaust gas recirculation is conducted without affecting the tracking performance of the load and stoichiometry. Depending on the parametrization of the cost function, nitrogen oxide or hydrocarbon pollutants can be reduced by up to 46% or 17%, respectively.

Design of hybrid fault-tolerant control system for air-fuel ratio control of internal combustion engines using artificial neural network and sliding mode control against sensor faults

This paper proposes a novel hybrid fault-tolerant control system (HFTCS) with dedicated non-linear controllers: artificial neural network (ANN) and sliding mode control (SMC) for active and passive parts, respectively. The proposed system can provide both desirable properties of stability to unexpected fast disturbances and post-fault optimal performance. In the active fault tolerant control system (AFTCS) part, the fault detection and isolation (FDI) unit is designed through the use of ANN for the estimation of faulty sensor values in the observer model. In the passive fault-tolerant system (PFTCS) part, the air-fuel ratio (AFR) controller is designed using a robust SMC that allows systems to manage faults in predefined limits without estimation. In the proposed system, SMC will form the passive part to react instantly to faults while ANN will optimize post-fault performance with active compensation. Moreover, Lyapunov stability analysis was also performed to make sure that the system remains stable in both normal and faulty conditions. The simulation results in the Matlab/Simulink environment show that the designed controller is robust to faults in normal and noisy measurements of the sensors. A comparison with the existing works also demonstrates the superior performance of the proposed hybrid algorithm.

Application of PDE Constrained Optimization in Internal Combustion Engine Pollution Control

Aiming at the problem of secondary pollution of waters due to the difficulty of controlling the dosage of purifiers in the treatment of internal combustion engine pollution, a partial differential equation (referred to as PDE) constrained optimization algorithm based on l 1 -norm is proposed. The algorithm first converts the internal combustion engine control model of the scavenger dose into a constrained optimization problem with a l 1 -penalty term. Secondly, it introduces a dose constraint condition based on PDE and uses the inherent property of Moreau-Yosida regularization to establish a smooth minimization function. Finally, the semismooth Newton method is used to iteratively find the optimal solution. The results of the comparison experiment show that the algorithm in this paper has a great improvement in the results of Newton step number and dose area percentage.

Design of Active Fault-Tolerant Control System for Air-Fuel Ratio control of Internal Combustion engine using nonlinear regression-based observer model.

Internal Combustion (IC) engines are prevalent in the process sector, and maintaining sufficient Air-Fuel Ratio (AFR) regulation in their fuel system is crucial for enhanced engine performance, fuel economy, and environmental safety. Faults in the AFR system's sensors cause the engine to shut down, hence, fault tolerance is essential. In order to avoid engine shutdown, this paper offers a novel Active Fault-Tolerant Control System (AFTCS) for air-fuel ratio control of an Internal Combustion (IC) engine in a process plant. In the Fault Detection and Isolation (FDI) unit, the proposed AFTCS uses a nonlinear regression-based observer model for analytical redundancy. The suggested system was simulated in the MATLAB / Simulink environment. The proposed system was tested at two different speeds (300 r/min and 600 r/min) and the results show that the system's response is within the acceptable bound without compromising the stability. The findings also demonstrate the higher fault tolerance capability for sensor defects of the AFR control system, particularly for the MAP sensor (at 300 r/min) in terms of reduced oscillatory response in comparison to the current literature. Compared to the linear regression-based and Genetic Algorithm (GA) based model, the nonlinear regression-based model results in a more accurate estimation of the faulty sensors. The proposed model is also efficient in terms of computation power and response time.

A Comparative Study of Design of Active Fault-Tolerant Control System for Air–Fuel Ratio Control of Internal Combustion Engine Using Particle Swarm Optimization, Genetic Algorithm, and Nonlinear Regression-Based Observer Model

In this article, three distinct strategies for designing an Active Fault-Tolerant Control System (AFTCS) for Air-Fuel Ratio (AFR) control of an Internal Combustion (IC) engine in a process plant to avoid engine shutdown, are presented. The proposed AFTCS employs a Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a Nonlinear Regression (NLR)-based observer model in the Fault Detection and Isolation (FDI) unit for analytical redundancy. A comparison between these three proposed techniques is carried out to determine the least expensive and most accurate approach. The results show that the nonlinear regression produces highly accurate results by consuming very low computational power, and its response time is also very low as compared to GA and PSO. The results obtained show that NLR requires 99.6% and 93.1% less computational time for throttle and MAP estimation, respectively, by reducing the estimation error to as low as 0.01. The simulation of the proposed system is carried out in the MATLAB/Simulink environment. The results prove the superior fault tolerance performance for sensor faults of the AFR control system, especially for the Manifold Absolute Pressure (MAP) sensor in terms of less oscillatory response as compared to that reported in existing literature.

Genetic algorithm based active fault-tolerant control system for air fuel ratio control of internal combustion engines

Internal Combustion (IC) engines are commonly used in the process industry, and proper Air-Fuel Ratio (AFR) regulation in their fuel system is essential for improved engine performance, fuel efficiency, and environmental protection. Since faults in the sensors of the AFR system cause the engine to shut down, fault tolerance is essentially required for them. In this paper, an advanced Active Fault-Tolerant Control System (AFTCS) for AFR control of an IC engine is proposed to prevent the engine shutdown. For analytical redundancy, the proposed AFTCS uses the Genetic Algorithm (GA) based observer model in the Fault Detection and Isolation (FDI) unit to generate estimated value for the faulty sensors using the values of other healthy sensors. The proposed framework was simulated in the MATLAB/Simulink environment to verify its effectiveness. In comparison to previous literature works, the results show that the AFR control system has superior fault tolerance output for sensor faults, especially for the MAP sensor, in terms of less oscillatory response. Finally, a comparison of the proposed GA-based AFTCS was carried out with the existing literature works and its superior performance was elaborated.

Design of active fault-tolerant control system for Air-fuel ratio control of internal combustion engines using fuzzy logic controller.

Fault-Tolerant Control Systems (FTCS) are used in critical and safety applications to improve performance and stability despite failure modes. As a result, costly production losses related to unusual and unplanned shutdowns can be prevented by incorporating these systems in the critical process plant machines. The Internal Combustion (IC) engines are highly used process plant machines and faults in their sensors will cause their shutdown instigating the need to install FTCS in them. In this paper, an Active Fault-Tolerant Control System (AFTCS) based on a Fuzzy Logic Controller (FLC) is suggested to improve the reliability of the Air-Fuel Ratio (AFR) control system of an IC engine. For analytical redundancy, a nonlinear Fuzzy Logic (FL) based observer is implemented in the proposed system for the Fault Detection and Isolation (FDI) unit for nonlinear sensors of the AFR system. Lyapunov stability analysis was used for designing a stable system in both faulty and normal conditions. To evaluate its performance, this system was developed in the MATLAB/Simulink platform. The simulation results show that the developed system is robust under sensor fault conditions, retaining stability with a minimum decrease of AFR. This study's comparison with the existing literature demonstrates that the proposed system is effective for maintaining the AFR in IC engines during sensor faulty conditions thus reducing shutdown of engine and production loss for increased profits.

Software strategy for internal combustion engine and electric motor control on a hybrid electric vehicle equipped with belt starter generator and automated manual transmission

In order to fulfil the latest EU emission standards, the average amount of CO2 emissions for each vehicle sold has to be reduced to avoid fines for manufacturers. One of the simplest and cheapest ways of hybridization is the belt starter generator (BSG), which makes it suitable for an entry-level range of vehicles. This paper presents a software strategy developed for internal combustion engine (ICE) and belt starter generator (BSG) control on a vehicle equipped with automated manual transmission and also the drivability, comfort and emission performances improvements expected by using this strategy.

Observer-based Purge Estimation for Robust Air Fuel Ratio Control of Internal Combustion Engines

This paper presents a purge estimation method for a robust air fuel ratio control of internal combustion engines. The air fuel ratio control is a primary emission control mechanism of gasoline engines, and purge is one of the most critical disturbances that can lead to significant air fuel ratio excursions. In this work, Luenberger-like unknown input observers are proposed to estimate purge flow rate and purge fuel fraction using existing sensors available in production engine management systems. The convergence properties of the proposed observers are investigated analytically and numerically. The estimation method allows to improve accuracy of the air fuel ratio control by compensating for the purged fuel, thereby it allows aggressive purge in a wide range of operational conditions to meet stringent evaporative emission standards.

Unified Fault-Tolerant Control for Air-Fuel Ratio Control of Internal Combustion Engines with Advanced Analytical and Hardware Redundancies

Unified Fault-Tolerant Control for Air-Fuel Ratio Control of Internal Combustion Engines with Advanced Analytical and Hardware Redundancies

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

REDUCTION OF TOXICITY OF BURNT DIESEL GASES WITH THE ADDITIONAL FEEDING SYSTEM

As a result of the introduction of new environmental emission standards, manufacturers of motor vehicles and fuels have to use a variety of technologies, systems and units to reduce harmful substances in the exhaust gases of diesel engines. Modern development of automobile internal combustion engines takes place in the following directions: increase of fuel economy and ecological purity; subincrease of unit power and weight reduction; creation of hybrid power plants with an internal combustion engine (ICE); dieselization of transport; increasing reliability of work and resources; use of alternative fuels (gas, hydrogen); control of internal combustion engines with the help of microprocessor technology. One of the ways to solve the problem is to develop technologies related to the installation of additional equipment on an existing engine in order to reduce harmful emissions into the environment.

In-Cylinder Oxygen Concentration Estimation Based on Virtual Measurement and Data Fusion Algorithm for Turbocharged Diesel Engines

In-cylinder oxygen concentration (ICOC) is critical for advanced combustion control of internal combustion engines, and is hard to be accessed in commercial measurements. In existing research, ICOC is predicted by conventional dynamical model based on mass/energy conservation, which suffers from uncertainties such as inaccuracy of volumetric efficiency or the error of orifice geometry. In this paper, we enhance the ICOC estimation by implementing two vital strategies. Firstly, we introduce a method called virtual measurement to resist the conventional model uncertainties, in this method we modeling the ICOC as a function of ignition delay which can be obtained by measuring the in-cylinder pressure. Secondly, we apply Kalman filter to fuse the ICOC results from the conventional dynamical model and the virtual measurement. The data fusion algorithm turns the estimation to a predictor-corrector fashion, which further improves the overall accuracy and robustness. The proposed approach is validated through a calibrated GT-Power engine model. The results show that the estimation error can be achieved form at worst 0.03 to at best 0.01 on steady state.

Design and experimental validation for nonlinear control of internal combustion engines with EGR and VVT

This paper presents a feedback control design scheme for internal combustion engines with the exhaust gas recirculation and the variable valve timing. A second-order nonlinear dynamical model is pr...

Numerical simulation of dynamic processes in transmission of vehicle

The article considers the issues of digital modeling of dynamic processes in the transmissions of transport vehicles. The purpose of this research was to develop an algorithm for numerical mathematical modeling of dynamic processes in the transmissions of transport vehicles using modern digital software packages. The method includes a systematic approach to the study of dynamic processes during switching, based on modeling the operation of the gearbox together with the internal combustion engine (taking into account its dynamic, speed and load characteristics). The order of application of the MATLab - Simulink, Simscape software packages for numerical simulation of dynamic processes is considered. Using the fundamental blocks of these applications, models of physical components are created: the internal combustion engine, friction clutches, gearboxes, elastic shafts, damping devices, and tractor power transmission control systems. A digital model of the tractor transmission is created, its design scheme is given, and the initial characteristics are set. It was used to simulate dynamic processes in the tractor gearbox. The main attention in this paper is paid to the application of the proposed method for calculating the dynamic processes in the gearbox during gear changes under load with forward and reverse switching, including the simultaneous use of several friction clutches.

Model Based Control Method for Diesel Engine Combustion

With the increase of information processing speed, more and more engine optimization work can be processed automatically. The quick-response closed-loop control method is becoming an urgent demand for the combustion control of modern internal combustion engines. In this paper, artificial neural network (ANN) and polynomial functions are used to predict the emission and engine performance based on seven parameters extracted from the in-cylinder pressure trace information of over 3000 cases. Based on the prediction model, the optimal combustion parameters are found with two different intelligent algorithms, including genetical algorithm and fish swarm algorithm. The results show that combination of quadratic function with genetical algorithm is able to obtain the appropriate combustion control parameters. Both engine emissions and thermal efficiency can be virtually predicted in a much faster way, such that enables a promising way to achieve fast and reliable closed-loop combustion control.

Experimental Study on Static Characteristics of Turbocharged Internal Combustion Engine with Low CBM Components

In order to improve the operation efficiency of internal combustion engine, the static relationship between the concentration and flow rate of coalbed methane and the operation parameters of internal combustion engine was studied. Based on the experiments, the effects of CBM component concentration and gas flow rate on air-fuel ratio, exhaust gas temperature, exhaust gas flow rate and compressor outlet pressure are summarized. It provides a certain technical support for the optimization control of internal combustion engine to change gas flow rate to adapt to the change of coal-bed methane composition.

TRACTOR GEARBOX: RESEARCH OF SHIFTING GEARS’ PROCESSES

Introduction. The process of shifting gears under load is one of the most complex types of dynamic process in the transmissions of transport, road and construction vehicles. Currently, the existing calculation methods do not fully reflect the features of the engine and automated gearbox interaction by switching stages under load using more than two friction clutches at the same time. The paper discusses the method of calculating the dynamic characteristics of the gearbox with the simultaneous use of both two and four friction clutches by shifting gears under load with forward and reverse switching. The authors present the results of the dynamic characteristics of the theoretical study of the automated gearbox with frictional stages’ switching under load and without breaking the power flow.Materials and methods. The main method was based on a system approach to the dynamic processes study and on the modeling of the gearbox operation together with the internal combustion engine. The authors took into account the regulatory, speed and load engine characteristics, resistive torque variation, rate of pressure rise in clutches by shifting to higher and lower gears. The paper performed theoretical modeling of processes using the MATLab package and the Simulink application. Using the main blocks of this application, the authors created models of physical components: an internal combustion engine, friction clutches, gear reducers, elastic shafts, damping devices and tractor power transmission control systems.Results. As a result, the authors obtained calculated data of the dynamic processes, variations in speed and rotational torque on the gearbox shafts; the load on the engine by shifting gears at different values of the start and finish time in switching on and off the gearbox friction controls. The paper presented the relevant graphs of the processes.Discussion and conclusions. The developed method of calculating the dynamic characteristics of the gearbox allows simulating workflows by shifting to higher and lower gears. This method takes into account both the parameters of the gearbox design and of the control actions: the rate of switching on the friction clutches; the time intervals of overlapping the “on and off” clutches. Therefore, in relation to the considered gearbox, the authors determine the values of the optimal time control commands of the friction by shifting.Financial transparency: the authors have no financial interest in the presented materials or methods. There is no conflict of interest.

Factors affecting the accuracy of control of internal combustion engine with a common rail fuel system

The article presents an overview of the operation of the battery fuel system with multiple injection, as well as the factors affecting the control accuracy of an internal combustion engine with a battery fuel system. The amount of preliminary fuel supply and the delay between preliminary and subsequent fuel supply by the electrohydraulic nozzle, as well as the tolerances for the design parameters of the electrohydraulic nozzle, are considered as influencing factors. Keywords wave phenomena; repeated injection; battery fuel system; electro hydraulic injector