Next-Cycle Optimal Fuel Control for Cycle-to-Cycle Variability Reduction in EGR-Diluted Combustion

Journal Article · Tue Dec 22 00:00:00 EST 2020 · IEEE Control Systems Letters

DOI:https://doi.org/10.1109/lcsys.2020.3046433· OSTI ID:1756233

Maldonado Puente, Bryan ^[1]; Kaul, Brian ^[1]; Schuman, Catherine ^[1]; Young, Steven ^[1]; Mitchell, Parker ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Dilute combustion using exhaust gas recirculation (EGR) is a cost-effective method for increasing engine efficiency. At high EGR levels, however, its efficiency benefits diminish as cycle-to-cycle variability (CCV) intensifies. Here, cycle-to-cycle fuel control was used to reduce CCV by injecting additional fuel in operating conditions with sporadic misfires and partial burns. An optimal control policy was proposed that utilizes 1) a physics-based model that tracks in-cylinder gas composition and 2) a one-step-ahead prediction of the combustion efficiency based on a kernel density estimator. The optimal solution, however, presents a tradeoff between the reduction in combustion CCV and the increase in fuel injection quantity required to stabilize the charge. Such a tradeoff can be adjusted by a single parameter embedded in the cost function. Simulation results indicated that combustion CCV can be reduced by as much as 65% by using at most 1% additional fuel. Although the control design presented here does not include fuel trim to maintain λ=1 for three-way catalyst compatibility, it is envisioned that this approach would be implemented alongside such an external controller, and the theoretical contribution presented here provides a first insight into the feasibility of CCV control using fuel injection.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1756233
• Journal Information: IEEE Control Systems Letters, Journal Name: IEEE Control Systems Letters Journal Issue: 0 Vol. 0; ISSN 2475-1456
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
automotive control
energy systems
grey-box modeling
stochastic optimal control