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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260006/13.jpg"" width=""300"" />Schematic of ECMS controller</div> Decreasing fuel consumption and emissions in automobiles continues to be an active research problem. A promising technology is powertrain hybridization. Study in this area usually focuses on the development of optimal power management control methods. The equivalent consumption minimization strategy (ECMS) is a widely used real-time control method used for determining the optimal trajectory of the power split between the engine and motor. Reports also cover applying ECMS to find an optimal gear changing strategy, but results are not always satisfactory in fuel economy and drivability. One possible reason for this is that gearbox dynamics are slow, but ECMS is based on instant optimization and neglects this time delay. This paper proposes a simple prediction strategy for improving ECMS performance used with gear changing control. The proposed controller improves fuel efficiency and drivability without the need of adding extra sensors to the automobile. The proposed method’s simplicity makes it suitable for implementation. </span>

In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model.

The electric engine cooling system, where the coolant pump and the radiator fan are driven by electric motors, admits advanced control methods to decrease auxiliary energy consumption. Recent publications show the fuel saving potential of optimal control strategies for the electric cooling system through offline simulations. These strategies often assume full knowledge of the drive cycle and compute the optimal control sequence by expensive global optimization methods. In reality, the full drive cycle is unknown during driving and global optimization not directly applicable on resource-constrained truck electronic control units. This paper reports state-of-the-art engineering achievements of exploiting vehicular onboard prediction for a limited time horizon and minimizing the auxiliary energy consumption of the electric cooling system through real-time optimization. The prediction and optimization are integrated into a model predictive controller (MPC), which is implemented on a dSPACE MicroAutoBox and tested on a truck on a public road. Systematic simulations show that the new method reduces fuel consumption of a 40-tonne truck by 0.36% and a 60-tonne truck by 0.69% in a real drive cycle compared to a base-line controller. The reductions on auxiliary fuel consumption for the 40-tonne and 60-tonne trucks are about 26% and 38%, respectively. Truck experiments validate the consistency between simulations and experiments and confirm the real-time feasibility of the MPC controller.

CONVENIENT is a project where prediction and integrated control are applied on several subsystems with electrified actuators. The technologies developed in this project are applied to a long-haul t ...

Inland waterway transportation (IWT) is highly efficient in terms of greenhouse gas emissions but lacks economic competitiveness when compared to other modes of transport. Digital information services that foster efficiency and sustainability of IWT are considered important elements for improving its attractiveness and thereby reducing greenhouse gas emissions of the transportation sector as a whole. Therefore, this paper addresses the question of what kind of digital information services are actually needed and should be provided, e.g., by port and waterway authorities, to stimulate a modal shift in favour of IWT. Though the concept of river information services (RIS) already provides a harmonised approach to information services in the sector, the current political and scientific discourse still lacks insight into what degree the currently available information services actually meet industry needs. Equally, possibilities to provide practical recommendations are limited. Therefore, this contribution fills this knowledge gap by providing data from the field gathered through a combination of qualitative and quantitative research methods. After elaborating on the underlying problem as well as the current state of research and practice, we will lay out observed information-relevant challenges to business actors and their respective needs. Based on this, practical recommendations for improvements of digital services and further avenues for research are derived.