Different drivers are nowadays leading spacecraft toward an increased level of on-board autonomy. In this paper, we survey model-based techniques as a vehicle to implement highly autonomous on-board capabilities for spacecraft mission planning and execution. In this respect, spacecraft reconfiguration approaches based on Markovian Decision Process are explored, and then compared with other model-based alternatives. The integration of planning systems and dynamic reprogramming capabilities into the on-board software is presented. Finally, operational concepts for mission planning and execution in recent European space projects as well as the implementation of in-flight adaptive mission operations via on-board control procedures are also analyzed.

The smart grid concept enables demand-side management, including electric vehicles (EVs). Thus way, some ancillary services can be provided in order to improve the power system stability, reliability, and security. The high penetration level of renewable energy resources causes some problems to independent system operators, such as lack of primary reserve and active power balance problems. Nowadays, many countries are encouraging the use of EVs which provide a good chance to utilize them as a virtual power plant (VPP) in order to contribute to frequency event. This paper proposes a new control method to use EV as VPP for providing primary reserve in smart grids. The primary frequency reserve helps the power system operator to intercept the frequency decline and to improve the frequency response of the whole system. The proposed method calculates the electric vehicles’ primary reserve based on EVs’ information, such as the state of charge (SOC), the arriving time and the vehicle’s departure time. The effectiveness of the proposed scheme is verified by several simulation scenarios on a real-world modern power system with different generating units, such as conventional power plants, renewable energy resources, and electric vehicles.

Cell balancing is used in battery systems to guarantee uniform charge and discharge of their cells during operations, and aims at improving the performance of the whole battery pack. Onboard battery performance and lifespan are particularly important in Electric Vehicles (EVs), since they have a direct impact on their autonomy. This paper proposes a Deep Reinforcement Learning (DRL)-based framework for Dynamic Reconfigurable Batteries (DRBs), where the capability of dynamically reconfiguring their cell topology can be exploited to attain cell balancing in EV applications. Thanks to the model-free nature and the robustness/adaptability properties of DRL-based solutions, the resulting trained agent is able to reach DRB cell balancing, while also taking into account both operational and modelling aspects of the combined EV-DRB system (e.g., all the events included in a typical driving cycle, the EV regenerative braking, and the heterogeneity of the DRB cells). The training process is carried out by using standard driving cycles, while the resulting trained agent is validated with a dataset based on real driving profiles. Finally, the performance achieved by the DRL policies is compared with some heuristic/rule-based approaches inspired from the literature.