Marco Sorrentino

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.

Control Oriented Modeling of Solid Oxide Fuel Cell Auxiliary Power Unit for Transportation Applications

This paper reports on the development of a control-oriented model for simulating a hybrid auxiliary power unit (APU) equipped with a solid oxide fuel cell (SOFC) stack. Such a work is motivated by the strong interest devoted to SOFC technology due to its highly appealing potentialities in terms of fuel savings, fuel flexibility, cogeneration, low-pollution and low-noise operation. In this context, the availability of a model with acceptable computational burden and satisfactory accuracy can significantly enhance both system and control strategy design phases for APUs destined to a wide application area (e.g., mild-hybrid cars, trains, ships, and airplanes). The core part of the model is the SOFC stack, surrounded by a number of ancillary devices: air compressor/blower, regulating pressure valves, heat exchangers, prereformer, and postburner. Since the thermal dynamics is clearly the slowest one, a lumped-capacity model is proposed to describe the response of SOFC and heat exchangers to load (i.e., operating current) variation. The stack model takes into account the dependence of stack voltage on operating temperature, thus adequately describing the typical voltage undershoot following a decrease in load demand. On the other hand, due to their faster dynamics the mass transfer and electrochemistry processes are assumed instantaneous. The hybridizing device, whose main purpose is to assist the SOFC system (i.e., stack and ancillaries) during transient conditions, consists of a lead-acid battery pack. Battery power dependence on current is modeled, taking into account the influence of actual state of charge on open circuit voltage and internal resistance. The developed APU model was tested by simulating typical auxiliary power demand profiles for a heavy-duty truck in parked-idling phases. Suited control strategies also were developed to avoid operating the SOFC stack under severe thermal transients and, at the same time, to guarantee a charge sustaining operation of the battery pack. In order to assess the benefits achievable by introducing the SOFC-APU on board of a commercial truck, the simulated fuel consumption was compared with the fuel consumed by idling the thermal engine. From the simulation carried out, it emerges how the SOFC-APU allows achieving a potential reduction in fuel consumption of up to 70%.

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 .

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.

PEM Fuel Cells Control by means of the Perturb and Observe Technique

In this paper the use of an adaptive technique aimed at controlling a polymeric electrolyte membrane fuel cell is introduced. It is demonstrated that a hill climbing-based method acting on the compressor speed and/or the backpressure valve opening is able to improve the performance of the fuel cell system with respect to those ones obtained by means of classical feed forward control approaches. Moreover, the proposed technique is able to ensure better performances even if well known aging mechanisms deteriorate cell efficiency. Numerical results based on experimentally derived models confirm the potential of the proposed control method and its intrinsic reliability

RECURRENT NEURAL NETWORKS FOR AIR-FUEL RATIO ESTIMATION AND CONTROL IN SPARK-IGNITED ENGINES

Abstract The paper focuses on the experimental identification and validation of recurrent neural network (RNN) models for air-fuel ratio (AFR) estimation and control in spark-ignited engines. Suited training procedures and experimental tests are proposed to improve RNN precision and generalization in predicting AFR transients for a wide range of operating scenarios. The reference engine has been tested by means of an integrated system of hardware and software tools for engine test automation and control strategies prototyping. The simulations performed on the test-sets show the ability of the RNN to reproduce the target patterns with satisfactory accuracy. Finally, real time implementation of RNN has been accomplished by developing and testing an inverse neural network controller acting on the injection time to limit AFR excursions from stoichiometry.

Transient Analysis of PEM Fuel Cell for Hybrid Vehicle Application

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 multi-level scheme has also been introduced and the complexity of control issues for hybrid powertrains have been discussed. Such a study has been performed to analyze the energy flows among the 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 analyzed. Particularly, the effects of the introduction of a rate limiter on the stack power have been investigated, evidencing that a 2 kW/s rate limiter increased the system efficiency by 10% while reducing the dynamic performances of the powertrain in terms of speed error (i.e. 25 %).Copyright

Hybrid Solar Vehicles

In the last years, increasing attention is being spent towards the applications of solar energy to electric and also to hybrid cars. But, while cars only fed by sun do not represent a practical alternative to cars for normal use, the concept of a hybrid electric car assisted by solar panels appears more realistic (Letendre et al., 2003; Fisher, 2009). The reasons for studying and developing a Hybrid Solar Vehicle can be summarized as follows: • fossil fuels, largely used for car propulsion, are doomed to depletion; their price tends to increase, and is subject to large and unpredictable fluctuations; • the CO2 generated by the combustion processes occurring in conventional thermal engines contributes to the greenhouse effects, with dangerous and maybe dramatic effects on global warming and climatic changes; • the worldwide demand for personal mobility is rapidly growing, especially in China and India; as a consequence, energy consumption and CO2 emissions related to cars and transportation are increasing; • solar energy is renewable, free and largely diffused, and Photovoltaic Panels are subject to continuous technological advances in terms of cell efficiency; their diffusion is rapidly growing, while their cost, after a continuous decrease and an inversion of the trend occurred in 2004, is continuing to decrease (Fig. 1);

Introducing Sunshine Forecast to Improve On-Board Energy Management of Hybrid Solar Vehicles

Abstract Management strategies of Hybrid Solar Vehicles differ from Hybrid Electric Vehicles, which usually adopt charge sustaining strategies, because the battery can be recharged also during parking time by solar energy. Therefore, at the end of driving the final state of charge (SOC) is required to be low enough to allow full storage of solar energy captured in the next parking phase, whereas the adoption of an unnecessary constantly-low value of final SOC would give additional energy losses and compromise battery lifetime. The effects of different strategies of selection of final SOC are studied by simulation over hourly solar data at different months, and the benefits achievable by estimating the energy expected in next parking phase are assessed.