Technical Paper
Modelling and Optimization for Black Box Controls of Internal Combustion Engines using Neural Networks
2024-07-02
2024-01-2995
The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with reduced development times. The growing number of vehicle variants, sharing similar engines and control algorithms, requires different calibrations. Additionally, these engines feature an increasing number of calibration parameters, 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 logic-level (white box) model-based calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited functional 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.