The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development.
The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities.
This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation. The ECU is operated on a Hardware-in-the-Loop (HiL) rig for measurement data generation.
To build surrogate models of these ECU functions, Neural Network model inputs are allocated categorized into two categories: function inputs as perceived by the logic level (White Box) software function, and curve/map fitting features representing the adjustment variables of the ECU function.
Factors influencing surrogate model accuracy such as, Neural Network hyperparameter optimization, input space amount and distribution as well as the parameter adjustment is investigated. Results show an increase in accuracy with the increasing number of implemented parameters, as well as the scalability of ECU function model representation with measurement data.
In addition to calibration purposes, the presented function representation method facilitates the use of plant models to replace time-consuming function construction and validation.