Application of Model-based Horizon Prediction to Enhance Control Algorithm Performance by Compensating for Time Delays in Automotive Drivelines

Accurate estimation of crucial quantities in automotive drivetrain systems is essential for optimizing performance, durability, and emissions. However, the presence of time delays, arising from tasks scheduling and communication latency between control units, can significantly hinder the effectiveness of advance control algorithms. Closed-loop performance is often limited by the equivalent time delay between the control action command, its effect on the system, and the measurement of the reaction. Frequently, commands and measurements originate from different sources, requiring precise coordination to accurately estimate the driveline response. This paper presents a novel model-based approach that integrates Kalman filtering with horizon prediction techniques to effectively address time-delay compensation. By leveraging the descriptive capabilities of physics-based models, the proposed method enables to overcome synchronization misalignment between commands, actuations and measurements. As information arrives from various sources, the algorithm processes it precisely to reconstruct the actual effect on the shafts. This enhanced estimation of critical quantities enables improved performance in advanced algorithms. The developed method is applied to two specific use cases: lash compensation in battery electric vehicles and engine pulse cancellation in hybrid powertrains. Simulation and in-vehicle results demonstrate the effectiveness of the proposed approach in accurately estimating critical driveline quantities allowing improved drivetrain control accuracy and overall drivability performance.