Data-Driven Air-Fuel Path Control Design for Robust RCCI Engine Operation

Abstract

Reactivity controlled compression ignition (RCCI) is a highly efficient and clean combustion concept, which enables the use of a wide range of renewable fuels. Consequently, this promising dual fuel combustion concept is of great interest for realizing climate neutral future transport. RCCI is very sensitive for operating conditions and requires advanced control strategies to guarantee stable and safe operation. For real-world RCCI implementation, we face control challenges related to transients and varying ambient conditions. Currently, a multivariable air–fuel path controller that can guarantee robust RCCI engine operation is lacking. In this work, we present a RCCI engine controller, which combines static decoupling and a diagonal MIMO feedback controller. For control design, a frequency domain-based approach is presented, which explicitly deals with cylinder-to-cylinder variations using data-driven, cylinder-individual combustion models. This approach enables a systematic trade-off between fast and robust performance and gives clear design criteria for stable operation. The performance of the developed multivariable engine controller is demonstrated on a six-cylinder diesel-E85 RCCI engine. From experimental results, it is concluded that the RCCI engine controller accurately tracks the five desired combustion and air path parameters, simultaneously. For the studied transient cycle, this results in 12.8% reduction in NOx emissions and peak in-cylinder pressure rise rates are reduced by 3.8 bar/deg CA. Compared to open-loop control, the stable and safe operating range is increased from 25 °C up to 35 °C intake manifold temperature and maximal load range is increased by 14.7% up to BMEP = 14.8 bar.

Keywords

• dual fuel control
• combustion modelling
• combustion engine system control
• data-driven models
• model-based control
• alternative fuels