Evaluation of a statistical method to use prior information in the estimation of combustion parameters

Abstract: Ion current sensing, where information about the combustion process in an SI-engine is gained by applying a voltage over the spark gap, is currently used to detect and avoid knock and misfire. Several researchers have pointed out that information on peak pressure location and air/fuel ratio can be gained from the ion current and have suggested several ways to estimate these parameters. Here a simplified Bayesian approach was taken to construct a lowpass-like filter or estimator that makes use of prior information to improve estimates in crucial areas. The algorithm is computationally light and could, if successful, improve estimates enough for production use. The filter was implemented in several variants and evaluated in a number of simulated cases. It was found that the proposed filter requires a number of trade-offs between variance, bias, tracking speed and accuracy that are difficult to balance. For satisfactory estimates and trade-off balance the prior information must be more accurate than was available. It was also found that similar a task, constructing a general Bayesian estimator, has already been tackled in the area of particle filtering and that there are promising and unexplored possibilities there. However, particle filters require computational power that will not be available to production engines for some years.