Replacing Volumetric Efficiency Calibration Look-up Tables with Artificial Neural Network-based Algorithm for Variable Valve Actuation

Signal processing incorporating Artificial Neural Networks (ANN) has been shown to be well suited for modeling engine-related performance indicators [ 1 , 2 , 3 ] that require multi-dimensional parametric calibration space. However, to obtain acceptable accuracy, traditional ANN implementation may require processing resources beyond the capability of current engine controllers. This paper explores the practicality of implementing an ANN-based algorithm performing real-time calculations of the volumetric efficiency (VE) for an engine with variable valve actuation (phasing and lift variation). This alternative approach was considered attractive since the additional degree of freedom introduced by variable lift would be cumbersome to add to the traditional multi-dimensional table-based representation of VE. Practical considerations for neural network training will be discussed, and the proposed algorithm is subjected to resource limitation typical of embedded control modules that are being applied in today's automotive market; RAM, ROM, computational resources, and fixed-point math. The ANN-based algorithm discussed here is shown to be competitive to a conventional implementation of multi-dimensional calibration tables serving the same purpose.