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

Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach

This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such as production capacity, economics and assumed engine characteristics. For the purpose of predicting performance, combustion and emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, the brake thermal efficiency (BTE) dropped as engine speed increased for biofuel and diesel fuel mixes, but brake-specific fuel consumption (BSFC) increased. The BSFC increased by 11.9% when diesel compared to using biofuel with diesel blends. The mixes cut both maximum cylinder pressure and NOx emissions. The biofuel-diesel fuel proved more successful, with maximum reduction of 9.8% and 22.2 at rpm, respectively. The biofuel and diesel blend significantly improved carbon dioxide (CO2) and smoke emissions. The biofuel blends offer significant advantages by decreeing exhaust pollutants and enhancing engine performance

Development of a virtual methodology based on physical and data-driven models to optimize engine calibration

Virtual engine calibration exploiting fully-physical plant models is the most promising solution for the reduction of time and cost of the traditional calibration process based on experimental testing. However, accuracy issues on the estimation of pollutant emissions are still unresolved. In this context, the paper shows how a virtual test rig can be built by combining a fully-physical engine model, featuring predictive combustion and NOx sub-models, with data-driven soot and particle number models. To this aim, a dedicated experimental campaign was carried out on a 1.6 liter EU6 diesel engine. A limited subset of the measured data was used to calibrate the predictive combustion and NOx sub-models. The measured data were also used to develop data-driven models to estimate soot and particulate emissions in terms of Filter Smoke Number (FSN) and Particle Number (PN), respectively. Inputs from engine calibration parameters (e.g., fuel injection timing and pressure) and combustion-related quantities computed by the physical model (e.g., combustion duration), were then merged. In this way, thanks to the combination of the two different datasets, the accuracy of the abovementioned models was improved by 20% for the FSN and 25% for the PN. The coupled physical and data-driven model was then used to optimize the engine calibration (fuel injection, air management) exploiting the Non-dominated Sorting genetic algorithm. The calibration obtained with the virtual methodology was then adopted on the engine test bench. A BSFC improvement of 10 g/kWh and a combustion reduction of 3.0 dB in comparison with the starting calibration was achieved

Comparison of Data Mining and Mathematical Models for Estimating Fuel Consumption of Passenger Vehicles

A number of analytical models have been described in the literature to estimate the fuel consumption of vehicles, most of which require a wide range of vehicle and trip related parameters as input data, which might limit the practical applicability of these models if such data were not readily available. To overcome this drawback, this study describes the development of three data mining models to estimate fuel consumption of a vehicle, including linear regression, artificial neural network and support vector machines. The paper presents comparison results with five instantaneous fuel consumption models from the literature using real data collected from three passenger vehicles on three routes. The results indicate that while the prediction accuracy of the instantaneous fuel consumption models varies across the data sets, those obtained by the regression models are significantly better and more robust against changes in input data

Radial-Basis-Function-Network-Based Prediction of Performance and Emission Characteristics in a Bio Diesel Engine Run on WCO Ester

Radial basis function neural networks (RBFNNs), which is a relatively new class of neural networks, have been investigated for their applicability for prediction of performance and emission characteristics of a diesel engine fuelled with waste cooking oil (WCO). The RBF networks were trained using the experimental data, where in load percentage, compression ratio, blend percentage, injection timing, and injection pressure were taken as the input parameters, and brake thermal efficiency (BTE), brake specific energy consumption (BSEC), exhaust gas temperature (T[subscript exh]), and engine emissions were used as the output parameters. The number of RBF centers was selected randomly. The network was initially trained using variable width values for the RBF units using a heuristic and then was trained by using fixed width values. Studies showed that RBFNN predicted results matched well with the experimental results over a wide range of operating conditions. Prediction accuracy for all the output parameters was above 90% in case of performance parameters and above 70% in case of emission parameters

Development of an Artificial Neural Network to Predict In-Use Engine Emissions

A method to predict in-use diesel engine emissions is developed based on engine dynamometer and in-use data acquired at the West Virginia University Center for Alternative Fuels, Engines, and Emissions. (WVU CAFEE). The model accounts for the effects of road grade on generated emissions; a need for this model is evident in literature. Current modeling methods do not account for the effects of road grade, and have been shown to under-predict NOx by as much as 57%. It is determined through present research and a review of relevant literature that an artificial neural network (ANN) was the most applicable modeling method.;A modular ANN was developed to predict the heavy duty diesel engine emissions. The two modules were trained independently, the first module was trained with data acquired through in-use testing, and the second module was trained with data acquired via engine dynamometer testing. The first module predicted the engine speed and torque associated with the inputs of road grade and vehicle speed, while the second ANN employed the first ANN\u27s outputs, and predicts the emitted quantities of NOx, CO2, HC, and CO. A series of training and verification runs are conducted in order to determine the optimum ANN characteristics. Once the ANN was finalized, it was trained with and employed to predict the emissions associated with a variety of routes.;When the ANN was trained with a combination of in-use and engine dynamometer data, the ANN is able to predict NOx emissions associated with that same route within 6% of the measured values. The average difference between the measured and predicted CO2 values for the same training and verification scenario mentioned above was less than 15%. It was also demonstrated that the ANN was able to predict emissions that are associated with routes that differ from those by which it is trained. When the ANN was trained with in-use data from a specific route, it was able to predict the NOx and CO2 emissions associated with a different route with percent differences from the measured values of 20% or less

Review of air fuel ratio prediction and control methods

Air pollution is one of main challenging issues nowadays that researchers have been trying to address.The emissions of vehicle engine exhausts are responsible for 50 percent of air pollution. Different types of emissions emit from vehicles including carbon monoxide, hydrocarbons, NOX, and so on. There is a tendency to develop strategies of engine control which work in a fast way. Accomplishing this task will result in a decrease in emissions which coupled with the fuel composition can bring about the best performance of the vehicle engine.Controlling the Air-Fuel Ratio (AFR) is necessary, because the AFR has an enormous impact on the effectiveness of the fuel and reduction of emissions.This paper is aimed at reviewing the recent studies on the prediction and control of the AFR, as a bulk of research works with different approaches, was conducted in this area.These approaches include both classical and modern methods, namely Artificial Neural Networks (ANN), Fuzzy Logic, and Neuro-Fuzzy Systems are described in this paper.The strength and the weakness of individual approaches will be discussed at length

The application of black box models to combustion processes in the internal combustion engine

The internal combustion engine has been under considerable pressure during the last few years. The publics growing sensitivity for emissions and resource wastage have led to increasingly stringent legislation. Engine manufacturers need to invest significant monetary funds and engineering resources in order to meet the designated regulations. In recent years, reductions in emissions and fuel consumption could be achieved with advanced engine technologies such as exhaust gas recirculation (EGR), variable geometry turbines (VGT), variable valve trains (VVT), variable compression ratios (VCR) or extended aftertreatment systems such as diesel particulate filters (DPF) or NOx traps or selective catalytic reduction (SCR) implementations. These approaches are characterised by a highly non-linear behaviour with an increasing demand for close-loop control. In consequence, successful controller design becomes an important part of meeting legislation requirements and acceptable standards. At the same time, the close-loop control requires additional monitoring information and, especially in the field of combustion control, this is a challenging task. Existing sensors in heavy-duty diesel applications for incylinder pressure detection enable the feedback of combustion conditions. However, high maintenance costs and reliability issues currently cancel this method out for mass-production vehicles. Methods of in-cylinder condition reconstruction for real-time applications have been presented over the last few decades. The methodical restrictions of these approaches are proving problematic. Hence, this work presents a method utilising artificial neural networks for the prediction of combustion-related engine parameters. The application of networks for the prediction of parameters such as emission formations of NOx and Particulate Matters will be shown initially. This thesis shows the importance of correct training and validation data choice together with a comprehensive network input set. In addition, an application of an efficient and accurate plant model as a support tool for an engine fuel-path controller is presented together with an efficient test data generation method. From these findings, an artificial neural network structure is developed for the prediction of in-cylinder combustion conditions. In-cylinder pressure and temperature provide valuable information about the combustion efficiency and quality. This work presents a structure that can predict these parameters from other more simple measurable variables within the engine auxiliaries. The structure is tested on data generated from a GT-Power simulation model and with a Caterpillar C6.6 heavy-duty diesel engine