Dynamic measurement with in-cycle process excitation of HCCI combustion: The key to handle complexity of data-driven control?

Homogeneous Charge Compression Ignition (HCCI) is a low temperature combustion technique with a high potential for reducing emissions while simultaneously improving fuel consumption. However, the high sensitivity to changing boundary conditions and low combustion stability at the edges of the operating range has lead to implementation challenges. Additionally, cyclic coupling through internal exhaust gas recirculation causes cyclic variations of the process, resulting in incomplete combustion, or even misfiring. Thus, consecutive cycles must be decoupled to increase the process stability. To achieve an accurate description of the coupling effects on a cycle-to-cycle and an inner-cyclic timescale, a novel measurement methodology is presented to generate data with a high variance. For this purpose, an active process excitation is performed to capture all relevant interactions between operating and feedback variables to enable modeling of the coupling effects on both timescales. To demonstrate the potential of the methodology, the generated data is used to design multiple input, multiple output (MIMO) models for both cyclic and inner-cyclic timescales. Artificial neural networks are then utilized to address the highly nonlinear process by taking advantage of the large amount of training data. Inverse process models are then used to implement a pure cycle-to-cycle and a multiscale MIMO closed-loop controller. Compared to state-of-the-art rule-based control approaches, the process stability and its thermodynamic efficiency are significantly improved. For the multiscale MIMO controller, a reduction of the standard deviation of the indicated mean effective pressure and the combustion phasing of more than 65% is achieved. In particular, the additional inner-cyclic feedback loop achieves a remarkable reduction of the standard deviation of approximately 35% and a 1.2% higher indicated efficiency compared to the cycle-to-cycle MIMO controller. The dynamic measurement with active in-cycle process excitation has proven to be an enabler for data-driven MIMO control of HCCI on multiple timescales.

• Record URL:
  https://doi.org/10.1177/14680874221078264
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14680874
• Supplemental Notes:
  • © IMechE 2022.
• Authors:
  • Bedei, Julian
  • 0000-0001-8260-8754
  • Oberlies, Malte
  • Schaber, Patrick
  • 0000-0002-8663-5415
  • Gordon, David
  • 0000-0002-7999-8234
  • Nuss, Eugen
  • 0000-0002-5243-501X
  • Li, Liguang
  • Andert, Jakob
  • 0000-0002-6754-1907
• Publication Date: 2023-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1155-1174
• Serial:
  • International Journal of Engine Research
  • Volume: 24
  • Issue Number: 3
  • Publisher: Sage Publications, Incorporated
  • ISSN: 1468-0874
  • EISSN: 20413149

Subject/Index Terms

• TRT Terms: Combustion; Compression ignition engines; Control system applications; Engine cycle; Measurement; Neural networks
• Identifier Terms: Adaptive Nonlinear Model Predictive Controller
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01877388
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 27 2023 3:10PM