Integration of optimal engine and driveline controllers to minimize driveline clunk and shuffle

Abstract

Automotive control algorithms have been moving from traditional, rule-based control algorithms to optimal, model-based control algorithms. While the model-based algorithms have shown to provide better control performance over a wide range of use cases with reduced calibration efforts, it is challenging to integrate multiple model-based controllers that are developed independently to address different control objectives. In this work, a nonlinear model predictive engine controller designed for torque tracking is integrated with a reference governor-based driveline controller designed to reduce vehicle drivability problems known as clunk and shuffle. The design of both controllers is discussed and their individual performance is demonstrated for real-world test conditions and realistic driving scenarios. Then, the integration between the two optimal controllers is discussed and a proof of concept use case showing the coordinated control between the two optimal controllers is presented. The results illustrate that without coordination between the two controllers neither of them are able to meet their original control objectives.