This paper proposes the use of support vector machines to reconstruct the indicated torque from crankshaft velocity in a six-cylinder spark ignition engine. Real-time knowledge of indicated torque is typically important for engine diagnostics, and recently, an engine idle speed controller capable of reducing the effects of cyclic combustion variability through the use of indicated torque information was proposed. While measurements of in-cylinder pressure can be used to determine indicated torque, these sensors are generally deemed prohibitively expensive for implementation in a production engine. Estimation methods, particularly traditional model based estimation, are typically computationally expensive and require independent data throughout the cylinder expansion stroke. Overlap of the expansion strokes in a six-cylinder engine complicates the problem and limits the ability of traditional model based approaches in fully reconstructing the torque production process.
Intelligent estimation techniques can identify the key characteristics of the instantaneous engine speed as a function of crank angle for the combustion stroke, thereby allowing an optimal subset of the available information to be utilised. A support vector machine approach is used in this paper due to the inherent optimisation of the training procedure, resulting in an optimised prediction matrix. Torque estimation is then obtained through simple mathematical evaluations, allowing fast reconstruction of indicated torque. A comparison between reconstruction performances at different operating points is presented as an evidence of the capabilities of the support vector machines.
This paper demonstrates that indicated torque reconstruction with support vector machines in a six-cylinder engine is not only possible, but it can be done with sufficient accuracy for the purpose of advanced engine control applications.