Illinois Institute of Technology

About: Illinois Institute of Technology is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Electric power system & Wireless network. The organization has 10188 authors who have published 21062 publications receiving 554178 citations. The organization is also known as: IIT & Illinois Tech.

A new battery/ultra-capacitor hybrid energy storage system for electric, hybrid and plug-in hybrid electric vehicles

Abstract: In this paper, a new battery/ultra-capacitor hybrid energy storage system (HESS) is proposed for electric drive vehicles including electric, hybrid electric, and plug-in hybrid electric vehicles. Compared to the conventional HESS design, which requires a larger DC/DC converter to interface between the ultra-capacitor and the battery/DC link, the new design uses a much smaller DC/DC converter to maintain the voltage of the ultra-capacitor at a value higher than the battery voltage. In addition, the battery directly provides power when the ultracapacitor voltage drops below the battery voltage. Therefore, a relatively constant load profile is created for the battery. In addition, the battery is not used to directly harvest energy from the regenerative braking; thus, the battery is isolated from random charges, which will increase the life of the battery. The proposed topology has the possibility of utilizing the system configuration for fast charging via the ultra-capacitor.

Transactive Real-Time Electric Vehicle Charging Management for Commercial Buildings With PV On-Site Generation

Abstract: In the future smart grids, it is important for prosumers to manage uncertainties from distributed renewable energy sources such as photovoltaic (PV) generation. As a type of distributed energy resource, electrical vehicles (EVs) are regarded as a promising solution to the problem. In this paper, a transactive real-time EV charging management scheme is proposed for the building energy management system (BEMS) of commercial buildings with PV on-site generation and EV charging services. Instead of direct EV charging control, the proposed EV charging management scheme applies a transactive energy concept-based approach to address real-time EV charging management. With the proposed scheme, the BEMS can schedule its net electricity exchange with the external grid under the uncertainties of PV generation and EV parking and maximize its profit in the real-time operation. Meanwhile, the EV owners need not provide the BEMS with further private information (such as future driving plans) but only their real-time charging requirements and preference setting of the response to the BEMS’s pricing signal in the proposed scheme. As such, the BEMS as a charging service provider only requires the minimal necessary information from EV owners. The EV owners’ charging requirements, preference setting of the response curves and their required reimbursements for the response are respected by the real-time charging management and their contributions to the demand response are reimbursed by the BEMS. Case studies with real world driving data from the Danish National Travel Survey were carried out to verify the proposed framework.

Deep Reinforcement Learning for EV Charging Navigation by Coordinating Smart Grid and Intelligent Transportation System

Abstract: A coordinated operation of smart grid (SG) and intelligent transportation system (ITS) provides electric vehicle (EV) owners with a myriad of power and transportation network data for EV charging navigation. However, the optimal charging navigation would be a challenging task owing to the randomness of traffic conditions, charging prices and waiting time at EV charging station (EVCS). In this paper, we propose a deep reinforcement learning (DRL)-based EV charging navigation, aiming at minimizing the total travel time and the charging cost at EVCS. First, we utilize the deterministic shortest charging route model (DSCRM) to extract feature states out of collected stochastic data and then formulate EV charging navigation as a Markov Decision Process (MDP) with an unknown transition probability. The proposed DRL-based approach will approximate the solution, which can adaptively learn the optimal strategy without any prior knowledge of uncertainties. Case studies are carried out within a practical zone in Xi’an city, China. Numerous experimental results verity the effectiveness of the proposed approach and illustrate its adaptation to EV driver preferences. The coordination effect of SG and ITS on reducing the waiting time and the charging cost in EV charging navigations is also analyzed.