Neural Modeling and Control of Diesel Engine with Pollution Constraints

The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.Comment: 15 page

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines

The high thermal efficiency and reliability of the compression-ignition engine makes it the first choice for many applications. For this to continue, a reduction of the pollutant emissions is needed. One solution is the use of machine learning (ML) and model predictive control (MPC) to minimize emissions and fuel consumption, without adding substantial computational cost to the engine controller. ML is developed in this paper for both modeling engine performance and emissions and for imitating the behaviour of an Linear Parameter Varying (LPV) MPC. Using a support vector machine-based linear parameter varying model of the engine performance and emissions, a model predictive controller is implemented for a 4.5 Cummins diesel engine. This online optimized MPC solution offers advantages in minimizing the \nox~emissions and fuel consumption compared to the baseline feedforward production controller. To reduce the computational cost of this MPC, a deep learning scheme is designed to mimic the behavior of the developed controller. The performance in reducing NOx emissions at a constant load by the imitative controller is similar to that of the online optimized MPC compared to the Cummins production controller. In addition, the imitative controller requires 50 times less computation time compared to that of the online MPC optimization.Comment: Submitted to Advances in Automotive Control - 10th AAC 202

Regression Modelling Estimation of Marine Diesel Generator Fuel Consumption and Emissions

This study aims to estimate the fuel consumption of marine diesel generators onboard. Objective technical specifications and operational data on the ship\u27s power generating plants and port calls were collected from an oceangoing oil/chemical tanker and used to develop the mathematical model of the plant in the Python and MATLAB environment. The model consists of alternators, prime movers and load distributions of the ship’s power generating plant and provides information on fuel consumption in metric tons calculated based on hours of operation and specific fuel consumption data. Regression models have helped predict future fuel consumption for the plant and the optimal model for the dataset was identified by comparing four different algorithms. As the results have shown the Ordinary Least Squares Regression to be optimum, it was used to make one, five, and ten-year predictions. The predictions for one-year, five-year, and ten-year periods are 4,322,436, 10,684,860, and 18,615,472 t respectively. The selected model predicts fuel consumption with R2 of 0.999, MAE of 3.932, and RMSE of 2.935. Fuel consumption predictions facilitated plant emission calculation

Minimizing engine emissions using state-feedback control with LQR and artificial intelligence fuel estimator

This paper presents a novel engine controller targeting the reduction of gas emissions. Toxic emissions, such as Carbon Monoxide (CO) and Nitric Oxide (NOx) affect the environment and the authorities aim to limit their amount by law. Emissions are formed during the high temperature combustion process, and can be optimised by adjusting some engine operating parameters. In this paper, the model describing emissions output of the engine as a function of engine control parameters is represented as a state-space system. A closed-loop controller is developed by using statefeedback control algorithm. The closed-loop gain, K, is obtained from the LQR tuning principles. The fuel estimator developed in previous works is used in order to reduce the model from the 8th order. The results show that the controller is able to control emission to the minimum in all constraints while keeping engine running in the same performance

Data driven techniques for on-board performance estimation and prediction in vehicular applications.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Attention-Based Neural Network for Solving the Green Vehicle Routing Problem in Waste Management

23.08.23: Trekkes tilbake fra visning som løsning på at oppgaven ble ferdigstilt fra studieadministrasjonen litt for fort/IHTIThe transport sector is a major contributor to the emission of greenhouse gases and air pollution. As urbanization and population growth continue to increase, the demand for transportation services grows, emphasizing the need for sustainable practices. Therefore, incorporating sustainability into the transport sector can effectively reduce its negative impacts on the environment and optimize the utilization of resources. This thesis aims to address this issue by proposing a novel method that integrates neural networks into the development of a green vehicle routing model. By incorporating environmental considerations, particularly fuel consumption, into the optimization process, the model seeks to generate more sustainable route solutions. The integration of machine learning techniques, specifically an attention-based neural network, demonstrates the potential of combining machine learning with operations research for effective route optimization. While the effectiveness of the green vehicle routing problem (GVRP) has been demonstrated in providing sustainable routes, its practical applications in real-world scenarios are still limited. Therefore, this thesis proposes the implementation of the GVRP model in a real-world waste collection routing problem. The study utilizes data obtained from Remiks, a waste management company responsible for waste collection and handling in Tromsø and Karlsøy. The findings of this study highlight the promising synergy between machine learning and operations research for further advancements and real-world applications. Specifically, the application of the GVRP approach to waste management issues has been shown to reduce emissions during the waste collection process compared to routes optimized solely for distance minimization. The attention-based neural network approach successfully generates routes that minimize fuel consumption, outperforming distance-optimized routes. These results underscore the importance of leveraging the GVRP to address environmental challenges while enhancing decision-making efficiency and effectiveness. Overall, this thesis provides insights for developing sustainable and optimized routes for real-world problems