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.

In this paper, a new approach for finding the optimal low speed regenerative braking cutoff point for Electric Vehicles (EVs) is proposed. This method is based on real-time sensing of motor current and disabling the regenerative braking at low speeds when the motor current falls below zero. It requires no hardware modifications to the existing architecture and can be implemented by modifying the brake controller algorithm. The proposed controller is tested and validated on an EV test bench simulation model and energy consumption is compared to a case where a fixed cutoff point is considered at low speeds. The results indicate improvement in extracting maximum energy during regenerative braking by applying this method.

This paper discusses different factors that influence the regenerative braking capability of Electric Vehicles (EVs) at low speeds. A Low Speed Cutoff Point (LSCP) below which the regenerative braking process is no longer effective is considered as a determining factor to evaluate regenerative braking performance under various conditions. Different scenarios are studied and the effect of each scenario on the displacement of LSCP is analyzed using an EV traction motor simulation model in MATLAB/SIMULINK. The results show that the LSCP deviates under different operating conditions and should not be considered constant under various circumstances when designing a brake controller.

This paper presents a state bin transition (macro)model for a large homogeneous population of thermostatically controlled loads (TCLs). The energy use of these TCLs is coordinated with a novel bottom-up asynchronous, anonymous, and randomizing control paradigm called Packetized Energy Management (PEM). A macro-model for a population of TCLs is developed and then augmented with a timer to capture the duration and consumption of energy packets and with exit-ON/OFF dynamics to ensure consumer quality of service. PEM permits a virtual power plant (VPP) operator to interact with TCLs through a packet request mechanism. The VPP regulates the proportion of accepted packet requests to allow tight tracking of balancing signals. The developed macro-model compares well with (agent-based) micro-simulations of TCLs under PEM and can be represented by a controlled Markov chain.

In modern electric power grids, microgrids are utilized in order to improve electric power grids' technical, economical, and environmental performance. In other words, both operator and consumers benefit the gained improvements by microgrids. However, operators confront certain ordeals to plan and operate the specific microgrid. In this paper, Combined Heat and Power (CHP) units, renewable energy resources, auxiliary thermal units and Electric Vehicles (EVs) are taken into consideration as a specific microgrid in California in the United States. Also, stochastic price of gas and electricity is taken into account. Moreover, uncertainty of the output power associated with non-dispatchable units like wind power plants as well as stochastic availability of the EVs adds to the complexity of the problem. The proposed approach plans to minimize total cost associated with the utilities as well as to minimize released emissions into the environment. Hence, the mentioned problem is a Multi-objective Linear Programming (MIP) which is solved using GAMS software. Finally, it has been showed that obtained solution is robust against uncertain parameters since there is just insignificant difference between solutions under this condition compared to condition that stochastic parameters are not modeled.