Performance Analysis of Data-Driven Plant Models on Embedded Systems
Data-driven plant models are well established in engine base calibration to cope with the ever increasing complexity of today’s electronic control units (ECUs). The engine, drive train, or entire vehicle is replaced with a behavioral model learned from a provided training data set. The model is used for offline simulations and virtual calibration of ECU control parameters, but its application is often limited beyond these use cases. Depending on the underlying regression algorithm, limiting factors include computationally expensive calculations and a high memory demand. However, development and testing of new control strategies would benefit from the ability to execute such high fidelity plant models directly in real-time environments. For instance, map-based ECU functions could be replaced or enhanced by more accurate behavioral models, with the implementation of virtual sensors or online monitoring functions.