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In this article, we propose a multihorizon model predictive control (MH-MPC) approach with applications to integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs). The proposed MH-MPC leverages preview and optimization over a short receding and a long shrinking horizon, where the accuracy of preview, model, and integration can be different over different horizons. Compared with a conventional MPC-based approach with a short prediction horizon and terminal cost, the MH-MPC improves fuel consumption to a level comparable to dynamic programming (DP) while still being computationally affordable. A statistical sensitivity analysis over real-world city driving cycles is conducted to demonstrate the robustness of MH-MPC to moderate levels of uncertainty in the long-term preview.

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 prediction 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 multirange 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 based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Low-Pressure Exhaust Gas Recirculation (LP-EGR) has been shown to be an effective means of improving fuel economy and suppressing knock in downsized, boosted, spark ignition engines. However, the transport delays inherent in LP-EGR systems can lead to combustion instability and misfire during tip-out events.

To determine the cardiorespiratory and anesthetic effects of 2, 6, and 20 mg kg(-1) IV alfaxalone in hydroxypropyl beta cyclodextrin (Alfaxan) in dogs.Blinded four-way crossover randomized by dose.Eight healthy adult purpose-bred mixed breed dogs (four male, four female) weighing between 12 and 28 kg.Four (0, 2, 6, 20 mg kg(-1)) IV treatments of alfaxalone were administered to each dog with a 3-hour washout period between doses. Measurements of heart rate, aortic systolic, mean, and diastolic blood pressures, pulmonary arterial and right atrial mean pressures, cardiac output, respiratory rate, tidal and minute volumes, and arterial blood pH, blood gases (PaO(2), PaCO(2)) were performed prior to and at predetermined intervals after drug administration. Systemic vascular resistance and rate pressure product were calculated. The quality of induction, maintenance, and recovery from anesthesia were categorically scored as was the response to noxious stimulation.The administration of alfaxalone resulted in dose-dependent changes in cardiovascular and respiratory parameters. Decreases in arterial blood pressure and increases in heart rate occurred at higher doses with most variables returning to baseline in 15-30 minutes. Respiratory rate, minute volume, and PaO(2) decreased and apnea was the most common side effect. The duration of anesthesia increased with dose, and induction, maintenance, and recovery were judged to be good to excellent with all doses studied.Alfaxalone produced good to excellent short-term anesthesia in unpremedicated dogs. Cardiorespiratory effects were minimal at lower doses. Anesthesia was judged to be good to excellent and associated with unresponsiveness to noxious stimulation for the majority of anesthesia. Hypoventilation and apnea were the most prominent and dose-dependent effects.