Bart Saerens

Model predictive control of automotive powertrains - first experimental results

This paper illustrates the capabilities of model predictive control for the control of automotive powertrains. We consider the minimization of the fuel consumption of a gasoline engine through dynamic optimization. The minimization uses a mean value model of the powertrain and vehicle. This model has two state variables: the pressure in the engine manifold and the engine speed. The control input is the throttle valve angle. The model is identified on a universal dynamometer. Optimal state and control trajectories are calculated using Bock¿s direct multiple shooting method implemented in the software MUSCOD-II. The developed approach is illustrated both in simulation and experimentally for a test case where a vehicle accelerates from 1100 rpm to 3700 rpm in 30 s. The optimized trajectories yield minimal fuel consumption. The experiments show that the optimal engine speed trajectory yields a reduction of the fuel consumption of 12% when compared to a linear trajectory. Thus, it is shown that, even with a simple model, a significant amount of fuel can be saved without loss of the fun-to-drive.

Optimal Control Using Pontryagin’s Maximum Principle and Dynamic Programming

This chapter describes the application of Pontryagin’s Maximum Principle and Dynamic Programming for vehicle drivingwith minimum fuel consumption. The focus is on minimum-fuel accelerations. For the fuel consumption modeling, a six-parameter polynomial approximation is proposed. With the Maximum Principle, this consumption model yields optimal accelerations with a linearly decreasing acceleration as a function of the velocity. This linear acceleration behavior is also observed in real traffic situations by other researchers. Dynamic Programming is implemented with a backward recursion on a specially chosen distance grid. This grid enables the calculation of realistic gear shifting behaviour during vehicle accelerations. Gear shifting dynamics are taken into account.

Predictive Ecocruise Control System: Model Logic and Preliminary Testing

A vehicle predictive ecocruise control system is developed. It minimizes vehicle fuel consumption levels by utilizing roadway topographic information. The predictive ecocruise control system consists of three components: a fuel consumption module, a powertrain module, and an optimization algorithm. The developed system generates an optimal control plan by using roadway grade information obtained from a high-resolution digital map to control the vehicle speed within a preset speed window in a fuel-saving manner. The performance of the system is tested by simulating a vehicle trip on a section of Interstate 81 in the state of Virginia. The results demonstrate fuel savings up to 15% with execution times within real time.

Assessment of Alternative Polynomial Fuel Consumption Models for Use in Intelligent Transportation Systems Applications

The objective of this article is to identify appropriate low-degree polynomial fuel consumption models for use in intelligent transportation systems (ITSs), eco-drive assist systems, and microscopic traffic simulation software. The different models that are assessed include models found in the literature and new models developed using a subsearch regression based on the Akaike information criterion. The models are evaluated based on model structures and their effectiveness in predicting instantaneous vehicle fuel consumption levels. Measurement data obtained from an engine dynamometer, a chassis dynamometer, and on-road testing are used to conduct the study. The study demonstrates that several low-degree polynomial fuel consumption models with a quadratic control term are appropriate for use in ITS applications (R 2>0.9).

Fetal and infant health outcomes among immigrant mothers in Flanders, Belgium

To compare fetal and infant mortality between immigrant and native‐born mothers in Flanders, Belgium.