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Developing intelligent energy management systems with high adaptability and superiority is necessary and significant for Hybrid Electric Vehicles (HEVs). This paper proposed an ensemble learning-based scheme based on a learning automata…
The number of electric vehicles is rapidly increasing worldwide. This growth brings significant environmental benefits but also introduces new challenges: uncoordinated charging can place stress on the grid, particularly during peak hours.…
Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. It has been shown to outperform conventional methods in…
The integration of electric vehicles (EVs) into power grids via Vehicle-to-Grid (V2G) system technology is increasing day by day, but these phenomena present both advantages and disadvantages. V2G can increase grid reliability by providing…
While battery electric vehicles (BEVs) play a key role for decarbonizing the transport sector, their impact on the power sector heavily depends on their charging strategies. Here we systematically analyze various combinations between…
We develop a system-level design for the provision of Ancillary Service (AS) for control of electric power grids by in-vehicle batteries, suitably applied to Electric Vehicles (EVs) operated in a sharing service. An architecture for…
Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drivers often fail to find the proper spots for charging, because…
The shift towards electric vehicles (EVs) is crucial for establishing sustainable and low-emission urban transportation systems. However, the success of this transition depends on the strategic placement of the charging infrastructure. This…
Fluctuations in electricity tariffs induced by the sporadic nature of demand loads on power grids has initiated immense efforts to find optimal scheduling solutions for charging and discharging plug-in electric vehicles (PEVs) subject to…
Forecasting the flexibility potential of Vehicle-to-Everything (V2X) systems is important for the future of energy networks, where the integration of renewable energy sources and electric vehicles poses significant challenges. In this…
Electric vehicles (EVs) have enjoyed increasing adoption because of the global concerns about the petroleum dependence and greenhouse gas emissions. However, their limited driving range fosters the occurrence of charging requests deriving…
Electric vehicle (EV) fleets are expected to become an increasingly important source of flexibility for power system operations. However, accurately capturing the flexibility potential of numerous and heterogeneous EVs remains a significant…
Flexibility in power demand, diverse usage patterns and storage capability of plug-in electric vehicles (PEVs) grow the elasticity of residential electricity demand remarkably. This elasticity can be utilized to form the daily aggregated…
With continuous advancements in science and technology, there is increasing focus on environmental sustainability, leading to heightened interest in autonomous electric vehicles (AEVs). AEVs hold significant potential for enhancing electric…
An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle's battery and the electric power grid. In order to flatten the load profile of the electricity system, EV scheduling…
Environment sensing and fusion via onboard sensors are envisioned to be widely applied in future autonomous driving networks. This paper considers a vehicular system with multiple self-driving vehicles that is assisted by multi-access edge…
Vehicle charging infrastructure targets in Europe currently rely on uniform benchmarks and overlook the flexibility that could be provided by future smart charging (V1G) and vehicle to grid operation (V2G). To address this gap, we…
End-to-end autonomous driving (E2E-AD) has emerged as a promising paradigm that unifies perception, prediction, and planning into a holistic, data-driven framework. However, achieving robustness to varying camera viewpoints, a common…
This paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based…
The rise of vehicle automation has generated significant interest in the potential role of future automated vehicles (AVs). In particular, in highly dense traffic settings, AVs are expected to serve as congestion-dampeners, mitigating the…