Self-optimising control of an SI-engine using a torque sensor

Larsson, Stefan; Andersson, Ingemar

doi:10.1016/j.conengprac.2007.05.009

Cited by 27 publications

(7 citation statements)

References 28 publications

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“…This is because the dynamic of indicated torque during transients is complex, caused by the breathing and combustion process. As all four cylinders are equipped with in-cylinder pressure sensors, the T ind for validation was calculated from the measured in-cylinder pressure P cyl and engine geometry using Equation (9).…”

Section: Engine Torques Validation Variable Sourcementioning

confidence: 99%

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A Control-Oriented Engine Torque Online Estimation Approach for Gasoline Engines Based on In-Cycle Crankshaft Speed Dynamics

et al. 2019

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Engine brake torque is a key feedback variable for the optimal torque split control of an engine–motor hybrid powertrain system. Due to the limitations in available sensors, however, engine torque is difficult to measure directly. For torque estimation, the unknown external load torque and the overlap of the expansion stroke between cylinders introduce a great disturbance to engine speed dynamics. This makes the conventional cycle average engine speed-based estimation approach unusable. In this article, an in-cycle crankshaft speed-based indicated torque estimation approach is proposed for a four-cylinder engine. First, a unique crankshaft angle window is selected for load torque estimation without the influence of combustion torque. Then, an in-cycle angle-domain crankshaft speed dynamic model is developed for engine indicated torque estimation. To account for the effects of model inaccuracy and unknown external disturbances, a “total disturbance” term is introduced. The total disturbance is then estimated by an adaptive observer using the engine’s historical operating data. Finally, a real-time correction method for the friction torque is proposed in the fuel cut-off scenario. Combining the aforementioned torque estimators, the brake torque can be obtained. The proposed algorithm is implemented in an in-house developed multi-core engine control unit (ECU). Experimental validation results on an engine test bench show that the algorithm’s execution time is about 3.2 ms, and the estimation error of the brake torque is within 5%. Therefore, the proposed method is a promising way to accurately estimate engine torque in real-time.

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Section: Engine Torques Validation Variable Sourcementioning

confidence: 99%

“…For instance, the cylinder's pressure may suffer from its harsh thermal environment. These factors limit the application of this method in stock engines [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Control-Oriented Engine Torque Online Estimation Approach for Gasoline Engines Based on In-Cycle Crankshaft Speed Dynamics

et al. 2019

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“…This article first considers extremum seeking (ES) control algorithms for iteration solution of online optimization problems in spark advance optimization applications. Three classes of ES are successively reviewed: stochastic approximation-based ES [7,[12][13][14][15][16][17][18][19][20], sinusoid-based ES [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], and natural perturbation-based ES [36][37][38][39]. This article then focuses on stochastic threshold control algorithms for iterative solution of probability control problems in knock limit control applications [2,10,11,[40][41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

A survey on online learning and optimization for spark advance control of SI engines

Zhang

Shen

2018

Sci. China Inf. Sci.

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One of the most important factors affecting fuel efficiency and emissions of automotive engines is combustion quality that is usually controlled by managing spark advance (SA) in spark ignition (SI) engines. With increasing sensing capabilities and enhancements in on-board computation capability, online learning and optimization techniques have been the subject of significant research interest. This article surveys the literature of learning and optimization algorithms with applications to combustion quality optimization and control of SI engines. In particular, this paper reviews extremum seeking control algorithms for iterative solution of online optimization problems, stochastic threshold control algorithms for iterative solution of probability control of stochastic knock event, as well as feedforward learning algorithms for iterative solution of operating-point-dependent feedforward adaptation problems. Finally, two experimental case studies including knock probabilistic constrained optimal combustion control and on-board map learning-based combustion control are carried out on an SI gasoline engine.

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“…Since then, both fuel economy and emissions have improved, but still more progress can be expected in the future. One of the limitations in engine control development lies in the scarce online information about the controlled process, the combustion [1].…”

Section: Introductionmentioning

confidence: 99%

An ANN-GA Framework for Optimal Engine Modeling

Tahboub

Barghash

Arafeh

et al. 2016

Mathematical Problems in Engineering

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Internal combustion engines are a main power source for vehicles. Improving the engine power is important which involved optimizing combustion timing and quantity of fuel. Variable valve timing (VVT) can be used in this respect to increase peak torque and power. In this work Artificial Neural Network (ANN) is used to model the effect of the VVT on the power and genetic algorithm (GA) as an optimization technique to find the optimal power setting. The same proposed technique can be used to improve fuel economy or a balanced combination of both fuel and power. Based on the findings of this work, it was noticed that the VVT setting is more important at high speed. It was also noticed that optimal power can be obtained by changing the VVT settings as a function of speed. Also to reduce computational time in obtaining the optimal VVT setting, an ANN was successfully used to model the optimal setting as a function of speed.

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Self-optimising control of an SI-engine using a torque sensor

Cited by 27 publications

References 28 publications

A Control-Oriented Engine Torque Online Estimation Approach for Gasoline Engines Based on In-Cycle Crankshaft Speed Dynamics

A Control-Oriented Engine Torque Online Estimation Approach for Gasoline Engines Based on In-Cycle Crankshaft Speed Dynamics

A survey on online learning and optimization for spark advance control of SI engines

An ANN-GA Framework for Optimal Engine Modeling

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