Ivan Arsie

Models for the Prediction of Performance and Emissions in a Spark Ignition Engine - A Sequentially Structured Approach

A thermodynamic model for the simulation of performance and emissions in a spark ignition engine is presented. The model is part of an integrated system of models with a hierarchical structure developed for the study and the optimal design of engine control strategies. In order to reduce the uncertainty due to the mutual interference during the validation phase, the model has been developed accordingly with a hierarchical and sequential structure. The main thermodynamic model is based on the classical two zone approach. A multi-zone model is then derived form the two zone calculation, for a proper evaluation of temperature gradients in the burned gas region. The emissions of HC, CO and NOx are then predicted by three sub-models. In order to make the precision of emission models suitable for engine control design, an identification technique based on decomposition approach has been developed, for the definition of optimal model structure with a minimum number of parameters. The results of the thermodynamic cycle model validation, performed over more than 300 engine operating conditions, show a satisfactory level of agreement between measured and predicted data cycles. Afterward, the two step identification procedure has been applied for the emission models parameters identification. From this analysis, it has been found that the model precision achieved can be comparable with that obtained via conventional mapping procedures using black-box models, but with a drastic reduction of the experimental effort. Moreover, the proposed approach allows substantial computational time saving with respect to conventional identification techniques.

A procedure to enhance identification of recurrent neural networks for simulating air-fuel ratio dynamics in SI engines

The paper deals with the identification of recurrent neural networks (RNNs) for simulating the air-fuel ratio (AFR) dynamics into the intake manifold of a spark ignition (SI) engine. RNN are derived from the well-established static multi layer perceptron feedforward neural networks (MLPFF), that have been largely adopted for steady-state mapping of SI engines. The main contribution of this work is the development of a procedure that allows identifying a RNN-based AFR simulator with high generalization and limited training data set. The procedure has been tested by comparing RNN simulations with AFR transients generated using a nonlinear-dynamic engine model. The results show how training the network making use of inputs that are uncorrelated and distributed over the entire engine operating domain allows improving model generalization and reducing the experimental burden. Potential areas of application of the procedure developed can be either the use of RNN as virtual AFR sensors (e.g. engine or individual AFR prediction) or the implementation of RNN in the framework of model-based control architectures. rchitectures.

Optimal Design and Dynamic Simulation of a Hybrid Solar Vehicle

The paper deals with a detailed study on the optimal sizing of a solar hybrid car, based on a longitudinal vehicle dynamic model and considering energy flows, weight and costs. The model describes the effects of solar panels area and position, vehicle dimensions and propulsion system components on vehicle performance, weight, fuel savings and costs. It is shown that significant fuel savings can be achieved for intermittent use with limited average power, and that economic feasibility could be achieved in next future, considering the expected trends in costs and prices.

An adaptive estimator of fuel film dynamics in the intake port of a spark ignition engine

In order to meet the limits imposed on automotive emissions, engine control systems are required to constrain air/fuel ratio (AFR) in a narrow band around the stoichiometric value, due to the strong decay of catalyst efficiency in case of rich or lean mixture. An adaptive estimator, based on an extended Kalman filter, is proposed for the fuel film dynamics in the intake port of a spark ignition engine. The observer is based on a two states mean value model which accounts for the impingement of the injected fuel on the manifold walls and the evaporation process. The observer has been tested on a set of experimental transient maneuvers, showing a good accuracy in predicting the AFR.

A Computer Code for S.I. Engine Control and Powertrain Simulation

A computer code oriented to S.I. engine control and powertrain simulation is presented. The model, developed in Matlab-Simulink® environment, predicts engine and driveline states, taking into account the dynamics of air and fuel flows into the intake manifold and the transient response of crankshaft, transmission gearing and vehicle. The model, derived from the code O.D.E.C.S. for the optimal design of engine control strategies now in use at Magneti Marelli, is suitable both for simulation analysis and to achieve optimal engine control strategies for minimum consumption with constraints on exhaust emissions and driveability via mathematical programming techniques. The model is structured as an object oriented modular framework and has been tested for simulating powertrain system and control performance with respect to any given transient and control strategy. The adoption of a hierarchical structure based on different classes of models, ranging from black-box Neural Network to grey-box mean value dynamic models, allows a satisfactory accuracy with limited computational demand which makes it suitable for the optimization of engine control strategies. In the paper the whole model framework is described together with simulation results obtained for different transient manouevres and control strategies.

A MODEL OF A HYBRID POWER PLANT WITH WIND TURBINES AND COMPRESSED AIR ENERGY STORAGE

After a general overview of Hybrid Power Plants (HPP) and Compressed Air Energy Storage (CAES), the authors present a thermo-economic model for the simulation and optimization of a HPP consisting of a wind turbine coupled with CAES. In the proposed scheme, during periods of excess power production, atmospheric air is compressed in a multistage compressor and cooled; when there is power demand, the compressed air is heated in multiple expansion stages using the stored heat and conventional thermal sources. Such plants can offer significant benefits in terms of flexibility in matching a fluctuating power demand, particularly when renewable sources, characterized by high and often unpredictable variability, are utilized. The possible advantages in terms of energy and cost savings with respect to other solutions must be carefully assessed, critically depending on performance and efficiencies of each sub-system, most of them operating in transient and off-design conditions. To this purpose, a thermodynamic model composed of several sub-systems describing wind turbine, multi-stage compressor, intercooler, aftercooler, heat recovery system, compressed air storage and turbine has been developed in Matlab/Simulink® environment. In the paper, several scenarios are compared by simulation and optimization analysis and a parametric study of the plant performance with respect to the main design variables is presented.

Modeling and Analysis of Transient Behavior of Polymer Electrolyte Membrane Fuel Cell Hybrid Vehicles

The paper focuses on the simulation of a hybrid vehicle with proton exchange membrane fuel cell as the main energy conversion system. A modeling structure has been developed to perform accurate analysis for powertrain and control system design. The models simulate the dynamics of the main powertrain elements and fuel cell system to give a sufficient description of the complex interaction between each component under real operating conditions. A control system based on a multilevel scheme has also been introduced and the complexity of control issues for hybrid powertrains have been discussed. This study has been performed to analyze the energy flows among powertrain components. The results highlight that optimizing these systems is not a trivial task and the use of precise models can improve the powertrain development process. Furthermore, the behavior of system state variables and the influence of control actions on fuel cell operation have also been analysed. In particular, the effect of introducing a rate limiter on the stack power has been investigated, evidencing that a 2 kW/s rate limiter increased the system efficiency by 10% while reducing the dynamic performance of the powertrain in terms of speed error .

INTEGRATION OF WIND TURBINES WITH COMPRESSED AIR ENERGY STORAGE

Some of the major limitations of renewable energy sources are represented by their low power density and intermittent nature, largely depending upon local site and unpredictable weather conditions. These problems concur to increase the unit costs of wind power, so limiting their diffusion. By coupling storage systems with a wind farm, some of the major limitations of wind power, such as a low power density and an unpredictable nature, can be overcome.After an overview on storage systems, the Compressed Air Energy Storage (CAES) is analyzed, and the state of art on such systems is discussed. A Matlab/Simulink model of a hybrid power plant consisting of a wind farm coupled with CAES is then presented. The model has been successfully validated starting from the operating data of the McIntosh CAES Plant in Alabama. Time‐series neural network‐based wind speed forecasting are employed to determine the optimal daily operation strategy for the storage system. A detailed economic analysis has been carried out: inv...

Information Based Selection of Neural Networks Training Data for S.I. Engine Mapping

The paper deals with the application of two techniques for the selection of the training data set used for the identification of Neural Network black-box engine models; the research starts from previous studies on Sequential Experimental Design for regression based engine models. The implemented methodologies rely on the Active Learning approach (i.e. active selection of training data) and are oriented to drive the experiments for the Neural Network training. The methods allow to select the most significant examples leading to an improvement of model generalization with respect to a heuristic choice of the training data. The data selection is performed making use of two different formulation, originally proposed by MacKay and Cohn, based on the Shannon’s Statistic Entropy and on the Mean Error Variance respectively. These techniques have been applied to assist the training of artificial Neural Networks for the estimation of engine torque and exhaust emissions of an S.I. engine, to be embedded into a powertrain dynamic model for the optimal design of engine control strategies (O.D.E.C.S.), now in use at Magneti Marelli.

Rule-Based Optimization of Intermittent ICE Scheduling on a Hybrid Solar Vehicle

ABSTRACT In the paper, a rule-based (RB) control strategy is proposed to optimize on-board energy management on a Hybrid Solar Vehicle (HSV) with series structure. Previous studies have shown the promising benefits of such vehicles in urban driving in terms of fuel economy and carbon dioxide reduction, and that economic feasibility could be achieved in a near future. The control architecture consists of two main loops: one external, which determines final battery state of charge (SOC) as function of expected solar contribution during next parking phase, and the second internal, whose aim is to define optimal ICE-EG power trajectory and SOC oscillation around the final value, as addressed by the first loop. In order to maximize the fuel savings achievable by a series architecture, an intermittent ICE scheduling is adopted for HSV. Therefore, the second loop yields the average power at which the ICE is operated as function of the average values of traction power demand and solar power. Expected solar contribution can be estimated starting from widely available solar databases and by processing past solar energy data measured on the vehicle. Neural Networks predictors, previously stored data and/or GPS derived information are suitable to estimate average power requested for vehicle traction. Extensive simulation analyses were carried out to test the performance of the RB algorithm, also comparing it to Genetic Algorithms-based optimization strategies previously developed by the authors. The results confirm the high potentialities offered by the proposed RB control strategy to perform real-time energy management on hybrid solar vehicles. The proposed rule-based optimization is currently under-implementation in an NI® cRIO control unit, thus allowing to perform experimental tests on a real HSV prototype developed at University of Salerno.