This paper introduces a novel approach for dynamically detecting the lowest speed threshold at which regenerative braking is effective in Electric Vehicles (EVs). The control approach is based on real-time sensing of the motor controller DC link current and disabling regenerative braking when current changes direction while the motor is operating as a generator. Various factors influencing regenerative braking capability of EVs at low speed are discussed and simulation studies are carried out to illustrate the effect of each factor on the displacement of the low-speed threshold. Based on the results obtained from the simulation studies, a dynamic Low-Speed Cutoff Point (LSCP) detection method is proposed. This method requires no hardware modification to the vehicle braking architecture and can be implemented solely by modifying the brake controller. The proposed method is tested on an experimental EV test platform for a predetermined drive cycle. It is shown that in comparison to considering a constant low-speed threshold during braking, the amount of energy recaptured through the regenerative braking process can be improved by taking advantage of the proposed method.

This paper proposes a day-ahead market framework for congestion management in smart distribution networks. The presented scheme provides a platform for collaboration between distribution-level market operator (DMO) and data traffic operator (DTO) to alleviate congested feeders such that data transmission traffic between market participants is effectively managed in a smart grid. In addition, a decentralized mechanism is developed for collaboration of electric vehicle (EV) aggregators with common clients to take advantage of EVs not only as flexible loads but also as mobile distributed storage (MDS) for congestion management. Moreover, the proposed framework outlines an administrative action for distribution system operator (DSO) to support the market when the decentralized competitions among distributed generation (DG) aggregators and EV aggregators do not fully relieve a serious congestion. The proposed day-ahead congestion management scheme is validated on an unbalanced 136-bus distribution system massively integrated with wind turbine DGs (WTDGs), photovoltaic DGs (PVDGs), diesel-engine DGs (DEDGs), and EVs.

In this paper, an operational framework is proposed for peer-to-peer (P2P) energy trading between an electric vehicle (EV) charging station and a business entity equipped with solar generation that will significantly improve the benefit of both parties compared to having sole agreements with the utility. A dynamic pricing mechanism for available EVs in the charging station is developed based on the price of the stored energy that not only improves the profit of the owners, but also promotes the contribution of charging stations in P2P energy markets. To evaluate the proposed framework, the performance of the system under P2P energy sharing is compared with peer-to-grid (P2G) energy trading. The results show a 23.24% reduction in total cost of prosumers and an improvement of 10% in self-consumption of PV generation as well as 100% participation willingness of prosumers. The results also suggest that due to the availability of solar energy during the daylight hours and the possibility to trade the stored energy in EVs outside of home, P2P energy transactions under the proposed control framework will bring economic benefits for EV owners.

This paper examines existing and future direct current (DC) distribution systems with a wide range of applications in data centers, telecommunication systems, commercial buildings, residential homes, electric vehicles, spacecraft, and aircrafts. DC distribution systems have many advantages and disadvantages over their alternating current (AC) counterparts. There are a few surviving examples of DC distribution systems; among them are the telecommunication systems and data centers that use the low-voltage 48 Vdc systems. However, recently, there has been a move towards higher DC bus voltages. In this paper, a comparative study of different DC distribution architectures and bus structures is presented and voltage level selection is discussed for maximizing system efficiency and reliability, reducing system costs, and increasing the flexibility of the system for future expansion. Furthermore, DC distribution systems are investigated from a safety standpoint and the current global market for these distribution systems is also discussed.

This paper proposes a novel approach to efficiently distribute braking force of an electric vehicle (EV) between friction and regenerative braking with an ultimate goal of maximizing harvested energy during braking. The regenerative braking performance of an EV depends on various factors influenced by the driver behavior and driving conditions, which are challenging to measure or predict in real-time. In the proposed method, the performance map of the traction motor (TM) and its controller is used to define a boundary in which blending of regenerative and friction braking is performed with the goal of maximizing recaptured energy through the regenerative braking process. The proposed method is validated on an experimental EV hardware-in-the-loop (HIL) test bench setup for a predetermined drive cycle of Urban Dynamometer Driving Schedule (UDDS). It is shown that the amount of recaptured energy through the regenerative braking process can significantly increase by taking advantage of the proposed method compared to a case, which considers a constant boundary for brake distribution.

Coordinated energy management plays a main role in increasing the performance and economic benefits of future smart homes. This article focuses on the energy management of a smart home equipped with a Plug-in Electric Vehicle (PEV), household energy storage, and photovoltaics (PV), and it proposes an Energy Price Tag (EPT) for all energy storage devices connected to the smart home system. A hybrid approach that consists of optimization and a rule-based prioritization is presented. The proposed algorithm establishes the priority order between the PEV, household battery, and imported power from the grid based on the EPT of energy sources. The proposed energy management algorithm seeks for the minimum overall energy cost for the smart home and PEV owner, while satisfying household power demand and charging requirements of the storage devices. The performance of the energy management algorithm is examined on a typical smart home over a 24-hour period based on time-varying grid electricity price.