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Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/ infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed preview beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multi-range vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium- and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a Neural Network (NN), while the long-range preview is predicted based on a Bayesian Network (BN). The predictions are updated in real-time and incorporated into a multi-horizon model predictive controller (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of the design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) are reported.

This paper presents a model-based, off-line method for analyzing the performance of individual components in an operating gas turbine. This integrated model combines sub-models of the combustor efficiency, the combustor pressure loss, the hot-end heat trans-fer, the turbine inlet temperature, and the turbine performance. As part of this, new physics-based models are proposed for both the combustor efficiency and the turbine. These new models accommodate operating points that feature the flame extending beyond the combustor and combustion occurring in the turbine. Systematic model reduction is undertaken using experimental data from a prototype, microgas turbine rig built by the group. This so called gas turbine air compressor (GTAC) prototype utilizes a single com-pressor to provide cycle air and a supply of compressed air as its sole output. The most general model results in sensible estimates of all system parameters, including those obtained from the new models that describe variations in both the combustor and turbine performance. As with other microgas turbines, heat losses are also found to be significant. [DOI: 10.1115/1.4007731