1,721,045 research outputs found
A Model for the Dynamic Simulation of Hybrid Vehicles with Pem Fuel Cell
No full textThe paper focuses on the development of a dynamic model for a hybrid vehicle equipped with a PEM fuel cell and a battery pack. An integrated structure of mathematical models has been implemented, whose development is based on previous studies carried out by the research group on the modeling of energy conversion systems for conventional and hybrid vehicles. The whole model simulates real driving cycles aiming at providing the data needed for selecting the best control strategies in terms of performance and fuel economy. A comparison with thermal-hybrid vehicles and conventional systems performance is presented together with an energy analysis carried out for both the single stack and its auxiliaries and the overall system (vehicle
Experimental Validation of a Recurrent Neural Network for Air-Fuel Ratio Dynamic Simulation in S.I. I.C. Engines
No full textThe paper deals with the simulation of the wall wetting dynamics in SI engines, making use of Recurrent Neural Networks (RNN). RNN are derived from the Multi Layer Feedforward Neural Networks, largely adopted for static mapping, by considering feedback connections between output and input layers. A Multi Input-Single Output structure has been adopted, assuming injected fuel, manifold pressure and engine speed as external input variables; the Air-Fuel Ratio at the exhaust gas oxygen sensor location has been considered as system output. The RNN has been trained (i.e. identified) and tested vs. a set of transient data measured on a commercial 4 cylinders SI engine at the test bench. The results show a good level of accuracy confirming the suitability of RNN for both HIL simulation or off-line identification of classical Mean Value Models with a drastic reduction of the calibration effort
Recurrent Neural Networks for AFR Estimation and Control in Spark Ignition Automotive Engines
No full textDevelopment and Real-Time Implementation of Recurrent Neural Networks for AFR Prediction and Control
No full textThe paper focuses on the experimental identification and
validation of recurrent neural networks (RNN) for realtime
prediction and control of air-fuel ratio (AFR) in spark-ignited engines. Suited training procedures and experimental tests are proposed to improve RNN precision and generalization in predicting both forward and inverse AFR dynamics 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 comparison between RNNs
simulation and experimental trajectories showed the
high accuracy and generalization capabilities
guaranteed by RNNs in reproducing forward and inverse
AFR dynamics.
Then, a fast and easy-to-handle procedure was set-up to
verify the potentialities of the inverse RNN to perform
feed-forward control of AFR. Preliminary experimental
tests indicate how the inverse RNN controller
performance are comparable and in some cases even
better than those guaranteed by the commercial ECU
the reference engine is equipped with. Therefore RNNbased
control of AFR emerges as a high potential alternative to reduce calibration efforts and to improve control performance as compared to the currently adopted techniques
Development of Recurrent Neural Networks for Virtual Sensing of NOx Emissions in Internal Combustion Engines
No full textA Neural Network Air-Fuel Ratio Estimator for Control and Diagnostics in Spark-Ignited Engines
No full textOptimal energy management for hybrid electric vehicles based on dynamic programming and receding horizon
No full textFuel consumption and emissions in parallel hybrid electric vehicles (HEVs) are directly linked to the way the load request to the wheels is managed between the internal combustion engine and the electric motor powered by the battery. A significant reduction in both consumption and emissions can be achieved by optimally controlling the power split on an entire driving mission (full horizon—FH). However, the entire driving path is often not predictable in real applications, hindering the fulfillment of the advantages gained through such an approach. An improvement can be achieved by exploiting more information available onboard, such as those derived from Advanced Driver Assistance Systems (ADAS) and vehicle connectivity (V2X). With this aim, the present work presents the design and verification, in a simulated environment, of an optimized controller for HEVs energy management, based on dynamic programming (DP) and receding horizon (RH) approaches. The control algorithm entails the partial knowledge of the driving mission, and its performance is assessed by evaluating fuel consumption related to a Worldwide harmonized Light vehicles Test Cycle (WLTC) under different control features (i.e., horizon length and update distance). The obtained results show a fuel consumption reduction comparable to that of the FH, with maximum drift from optimal consumption of less than 10%
An Integrated System of Models for Performance and Emissions in Si Engines: Development and Identification
No full textRecurrent Neural Network based Air-Fuel Ratio Observer for SI Internal Combustion Engines
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