Dilute combustion with exhaust gas recirculation (EGR) in spark-ignition engines presents a cost-effective method for achieving higher levels of engine efficiency. At high levels of EGR, however, cycle-to-cycle variability (CCV) of the combustion process is exacerbated by sporadic occurrences of misfires and partial burns. Previous studies have shown that temporal deterministic patterns emerge at such conditions and certain combustion cycles have a significant influence over future events. Due to the complexity of the combustion process and the nature of CCV, harnessing all the deterministic information for control purposes has remained challenging even with physics based 0-D, 1-D, and high-fidelity computational fluid dynamics (CFD) models. In this study, we present a data-driven approach to optimize the combustion process by controlling CCV adjusting the cycle-to-cycle fuel injection quantity. Readily available data from in-cylinder pressure was used to train a spiking neural network (SNN) which learns the optimal way to manage fuel injection in order to reduce CCV while maintaining acceptable levels of fuel consumption. SNNs are particularly well suited for powertrain control applications due to their ability to be deployed on FPGA-based neuromorphic hardware which are small, inexpensive, and have a low power demand. The high-performance computing (HPC) resources of Oak Ridge National Laboratory were used to run an evolutionary-based training approach for choosing the best SNN configuration that minimizes the size of the network while achieving the desired goal. The neuromorphic hardware with the optimized SNN deployed was connected to the rapid prototyping engine control system for real-time control implementation and tested on a single cylinder version of a GM LNF 4-cylinder engine. The results show a significant reduction of CCV with a small percentage of additional fuel used to stabilize the charge.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Dilute Combustion Control Using Spiking Neural Networks


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:

    Kongress:

    SAE WCX Digital Summit ; 2021



    Erscheinungsdatum :

    2021-04-06




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Cognitive processing using spiking neural networks

    Allen, Jacob N. / Abdel-Aty-Zohdy, Hoda S. / Ewing, Robert L. | IEEE | 2009


    Exploring Lottery Ticket Hypothesis in Spiking Neural Networks

    Kim, Youngeun / Li, Yuhang / Park, Hyoungseob et al. | British Library Conference Proceedings | 2022


    Motion representation with spiking neural networks for grasping and manipulation

    Tieck, Juan Camilo Vásquez | TIBKAT | 2021

    Freier Zugriff

    Spiking Neural Networks for Detecting Satellite Internet of Things Signals

    Dakic, Kosta / Al Homssi, Bassel / Walia, Sumeet et al. | IEEE | 2024


    Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition

    Cao, Y. / Chen, Y. / Khosla, D. | British Library Online Contents | 2015