Statistical Analysis of Knock Intensity Probability Distribution and Development of 0-D Predictive Knock Model for a SI TC Engine

Cavina, Nicolò; Brusa, Alessandro; Rojo, Nahuel; Corti, Enrico

doi:10.4271/2018-01-0858

Cited by 12 publications

(23 citation statements)

References 16 publications

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“…The calibrated analytical MFB50 model calculates the ∆SA to reach the target combustion phase. The analytical knock model [2] includes the influence of the fuel RON for the accurate estimation of the engine knock tendency. In the pure open-loop controller described in the first part of this activity, the fuel RON is manually set by the operator at the test cell.…”

Section: Adaptive Strategiesmentioning

confidence: 99%

“…The analytical knock model is used to convert the target MAPO98 into the corresponding mean Pmax [2]. The choice of 98th percentile is arbitrary.…”

Section: Analytical Knock Model Validation and Fuel Ron Learning Strategymentioning

confidence: 99%

“…As described by the authors in detail in [2], the effects on the knock intensity of the mixture enrichment, the cooling of the intake air temperature and the increasing fuel RON are modeled as an additive contribution to the Pmax value. The result can be seen as a mere translation of MAPO98 curve along the Pmax axis (Figure 3).…”

Section: Analytical Knock Model Validation and Fuel Ron Learning Strategymentioning

confidence: 99%

“…Equation (2) shows how to convert a target MAPO98 into a Pmax value and it is thus implemented in the block that includes the reversed analytical knock model:…”

Section: Analytical Knock Model Validation and Fuel Ron Learning Strategymentioning

confidence: 99%

“…In the Part 1 of the whole activity a robust and accurate open-loop controller is developed and validated, and the main implemented models are described, including the control-oriented piston damage model [1] and the analytical knock model [2]. Other important models introduced in the open-loop controller are the analytical maximum in-cylinder pressure (Pmax) and 50% of mass of fuel burned (MFB50) ones, described by the authors in [3].…”

Section: Introductionmentioning

confidence: 99%

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Development and Experimental Validation of an Adaptive, Piston-Damage-Based Combustion Control System for SI Engines: Part 2—Implementation of Adaptive Strategies

et al. 2021

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This work focuses on the implementation of innovative adaptive strategies and a closed-loop chain in a piston-damage-based combustion controller. In the previous paper (Part 1), implemented models and the open loop algorithm are described and validated by reproducing some vehicle maneuvers at the engine test cell. Such controller is further improved by implementing self-learning algorithms based on the analytical formulations of knock and the combustion model, to update the fuel Research Octane Number (RON) and the relationship between the combustion phase and the spark timing in real-time. These strategies are based on the availability of an on-board indicating system for the estimation of both the knock intensity and the combustion phase index. The equations used to develop the adaptive strategies are described in detail. A closed-loop chain is then added, and the complete controller is finally implemented in a Rapid Control Prototyping (RCP) device. The controller is validated with specific tests defined to verify the robustness and the accuracy of the adaptive strategies. Results of the online validation process are presented in the last part of the paper and the accuracy of the complete controller is finally demonstrated. Indeed, error between the cyclic and the target combustion phase index is within the range ±0.5 Crank Angle degrees (°CA), while the error between the measured and the calculated maximum in-cylinder pressure is included in the range ±5 bar, even when fuel RON or spark advance map is changing.

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Section: Adaptive Strategiesmentioning

confidence: 99%

“…The analytical knock model is used to convert the target MAPO98 into the corresponding mean Pmax [2]. The choice of 98th percentile is arbitrary.…”

Section: Analytical Knock Model Validation and Fuel Ron Learning Strategymentioning

confidence: 99%

Section: Analytical Knock Model Validation and Fuel Ron Learning Strategymentioning

confidence: 99%

“…Equation (2) shows how to convert a target MAPO98 into a Pmax value and it is thus implemented in the block that includes the reversed analytical knock model:…”

Section: Analytical Knock Model Validation and Fuel Ron Learning Strategymentioning

confidence: 99%