Self-Learning Control Of Advanced Powertrain Systems

Increasing demand for improving fuel economy and reducing emissions without sacrificing performance have induced significant research and investment in advanced internal combustion engine technologies. These technologies, such as fuel injection systems, variable geometry turbocharging, variable valve actuation, and exhaust gas recirculation, have introduced a number of engine variables that can be controlled to optimize engine operation. In particular, computing the optimal values of these variables, referred to as engine calibration, has been shown to be especially critical for achieving high engine performance and fuel economy while meeting emission standards. Consequently, engine calibration is defined as a procedure that optimizes one or more engine performance criteria, e.g., fuel economy, emissions, or engine acceleration with respect to the engine controllable variables.

State-of-the-art calibration methods cannot guarantee continuously the optimal engine operation for the entire operating domain, especially in transient cases encountered in the driving styles of different drivers. We have developed the theoretical framework and control algorithms for making the engine of a vehicle into an autonomous intelligent system capable of learning its optimal calibration in real time while the driver is driving the vehicle. Through this new approach, the engine progressively perceives the driver’s driving style and eventually learns to operate in a manner that optimizes specified performance criteria, e.g., fuel economy, emissions, or engine acceleration. The engine’s ability to learn its optimum calibration is not limited, however, to a particular driving style. The engine can learn to operate optimally for different drivers by the time they indicate their identity before starting the vehicle through their car keys. The engine can then adjust its operation to be optimal for a particular driver based on what it has learned in the past regarding his/her driving style.

Relevant Publications:

  1. Di Cairano, S., Guardiola, C., Malikopoulos, A.A., Seigel, J. “Future Impact and Challenges of Automotive Control,” in The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future, John Wiley & Sons, 2023.
  2. Malikopoulos, A.A. Real-Time, Self-Learning Identification and Stochastic Optimal Control of Advanced Powertrain Systems, ProQuest, September 2011.
  3. Malikopoulos, A.A., Papalambros, P.Y., and Assanis, D.N., “Online Identification and Stochastic Control for Autonomous Internal Combustion Engines,” Dyn. Sys., Meas., Control, Vol.132, 2, pp.024504-9, 2010 (pdf).
  4. Malikopoulos, A.A., “Convergence Properties of a Computational Learning Model for Unknown Markov Chains,” Dyn. Sys., Meas., Control, Vol.131, 4, pp. 041011-7, 2009 (pdf).
  5. Malikopoulos, A.A., Papalambros, P.Y., and Assanis, D.N., “A Real-Time Computational Learning Model for Sequential Decision-Making Problems Under Uncertainty,” Dyn. Sys., Meas., Control, Vol. 131, 4, pp.041010-8, 2009 (pdf).
  6. Malikopoulos, A.A., Assanis, D.N., and Papalambros, P.Y., “Real-Time, Self-Learning Optimization of Diesel Engine Calibration,” Eng. Gas Turbines Power, Vol. 131, 2, pp. 022803-9, 2009 (pdf).
  7. Malikopoulos, A.A., “A Rollout Control Algorithm for Discrete-Time Stochastic Systems,” Proceedings of the 2010 ASME Dynamic Systems and Control Conference (DSCC), 2010 (pdf).
  8. Malikopoulos, A.A., “Convergence Properties of a Computational Learning Model for Unknown Markov Chains,” Proceedings of the 2008 ASME Dynamic Systems and Control Conference (DSCC), DSCC2008-2174, 2008 (pdf).
  9. Malikopoulos, A.A., Assanis, D.N. and Papalambros, P.Y., “Optimal Engine Calibration for Individual Driving Styles,” Proceedings of the Society of Automotive Engineers World Congress, SAE 2008-01-1367, 2008 (pdf).
  10. Malikopoulos, A.A., Papalambros, P.Y. and Assanis, D.N., “A State-Space Representation Model and Learning Algorithm for Real-Time Decision-Making Under Uncertainty,” Proceedings of the 2007 ASME International Mechanical Engineering Congress and Exposition, IMECE2007-41258, 2007 (pdf).
  11. Malikopoulos, A.A., Assanis, D.N. and Papalambros, P.Y., “Real-Time, Self-Learning Optimization of Diesel Engine Calibration,” Proceedings of the 2007 Technical Conference of the ASME Internal Combustion Engine Division, ICEF2007-1603, 2007 (pdf).
  12. Malikopoulos, A.A., Papalambros, P.Y. and Assanis, D.N., “A Learning Algorithm for Optimal Internal Combustion Engine Calibration in Real Time,” Proceedings of the 2007 ASME International Design Engineering Technical Conferences & Computers and Information In Engineering Conference, DETC2007/DAC-34718, 2007 (pdf).
  13. Malikopoulos, A.A., Filipi, Z. and Assanis, D.N., “Simulation of an Integrated Starter Alternator (ISA) for the HMMWV,” Proceedings of the Society of Automotive Engineers World Congress, SAE 2006-01-0442, 2006 (pdf).