An Iterative Histogram-Based Optimization of Calibration Tables in a Powertrain Controller

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance. The calibration table then has its values optimized by the Genetic Algorithm to a single value that better matches the reference performance. A novel Iterative Histogram procedure is then used to identify which cells from the new table have the greatest contribution to the performance mismatch between the model and the reference. These values are optimized and the histogram is reassessed. This process is repeated until the mismatch target is achieved or the model results show saturation in its performance. The iterative nature of this process results in a powerful tool that gives its users the ability to easily conduct a simulation while simultaneously monitoring the results of each iteration until the target is met.