Predictive information can significantly improve energy efficiency in a hybrid powertrain, especially for a long drive. Ideally the prediction of a sufficiently long horizon can bring the maximum benefit, however during this horizon the traffic can change, making it impossible to continue the chosen optimal strategy. Due to the limited vision of vehicles' sensors, it is difficult to acquire information far away. Against this background, this article proposes a predictive optimal control strategy in the connected environment. It combines real-world vehicle-to-everything (V2X) information and cloud communication to allow for swift reaction and loss mitigation. More specifically, V2X information is used to detect impending changes on the route and the cloud is used to map these changes onto new constraints on the operation of the HEV. Instead of using "static" V2X information directly, a more realistic prediction method considering the dynamic of traffic is developed. Besides, an update strategy is adopted to timely cope with uncertainties. The performance of the approach is shown by a case study based on real driving cycles in Austria. The results show that the proposed structure is able to improve the performance (mainly fuel efficiency) up to 6.9%.

Eco-driving is a way to improve performance — mainly energy consumption — of road vehicles by computing an optimal speed and gear shifting profile based on vehicle data and road profile, e.g. slopes or speed limits. It mainly focuses on long haul scenarios such as highways, considering longitudinal movement only. Lateral acceleration of a vehicle is a critical quantity both in terms of comfort and safety, but its impact on fuel consumption or emissions is rarely considered or believed to be limited [1], as it does not affect directly the operating point of the engine. However, on country roads which usually present much stronger curvatures, lateral acceleration may be a critical constraint. In this paper, the impact of lateral acceleration limits on optimal solutions to multi-objective eco-driving is investigated. It is found that it may play an even more critical role than longitudinal acceleration with respect to fuel consumption and NOx emission. As a consequence, the choice of limits to lateral acceleration on curvy roads should be set very carefully in order to achieve a balance between energy saving, drivers comfort and travel time. The results of this work are validated on a high-feasibility Hardware-in-the-loop (HIL) system calibrated with data from Real Driving Emissions tests.

Offshore wind farm (OWF) is considered as a perfect zero‐carbon energy source for the future power system. However, the growing offshore distance and water depth of OWF make the OWF HVDC transmission technique a more promising solution than HVAC due to higher cost‐efficiency and reliability. In this paper, the current situation of OWF‐HVDC projects is introduced at first. Then, novel converter topologies with the higher power density and cost‐efficiency are presented, including the hybrid modular multilevel converter (MMC), alternative arm converter (AAC), and diode rectifier (DR). Next, several OWF HVDC transmission system topologies are introduced, including terminal‐hybrid, station‐hybrid and all‐DC delivered system. Furthermore, the key technologies for OWF HVDC operation and control are summarized, including grid‐forming control strategy for offshore wind turbines, stability analysis method, corresponding stability enhancement measures and frequency support control strategies. Additionally, the fault ride‐through and protection strategies for different fault locations have been presented. Finally, the main conclusions and prospects for OWF HVDC are summarized.

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