Search Results

The vehicle driving cycles affect the performance of a hybrid vehicle control strategy, as a result, the overall performance of the vehicle, such as fuel consumption and emission. By identifying the driving cycles of a vehicle, the control system is able to dynamically change the control strategy (or parameters) to the best one to adapt to the changes of vehicle driving patterns. This paper studies the supervised driving cycle recognition using pattern recognition approach. With pattern recognition method, a driving cycle is represented by feature vectors that are formed by a set of parameters to which the driving cycle is sensitive. The on-line driving pattern recognition is achieved by calculating the feature vectors and classifying these feature vectors to one of the driving patterns in the reference database. To establish reference driving cycle database, the representative feature vectors for four federal driving cycles are generated using feature extraction method.

This paper studies the bi-directional power flow control between Plug-in Electric Vehicles (PEVs) and an electrical grid. A grid-tied charging system that enables both Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) charging/discharging is modeled using SimPowerSystems in Matlab/Simulink environment. A bi-directional AC-DC converter and a bi-directional DC-DC buck-boost converter are integrated to charge and discharge PEV batteries. For AC-DC converter control, Predictive Current Control (PCC) strategy is employed to enable grid current to reach a reference current after one modulation period. In addition, Phase Lock Loop (PLL) and a band-stop filter are designed to lock the grid voltage phase and reduce harmonics. Bi-directional power flow is realized by controlling the mode of the DC-DC converter. Simulation tests are conducted to evaluate the performance of this bi-directional charging system.

The increased market share of electric vehicles and renewable energy resources have raised concerns about their impact on the current electrical distribution grid. To achieve sustainable and stable power distribution, a lot of effort has been made to implement smart grids. This paper addresses Demand Response (DR) load control in a smart grid using Internet of Things (IoT) technology. A smart grid is a networked electrical grid which includes a variety of components and sub-systems, including renewable energy resources, controllable loads, smart meters, and automation devices. An IoT approach is a good fit for the control and energy management of smart grids. Although there are various commercial systems available for smart grid control, the systems based on open sources are limited. In this study, we adopt an open source development platform named Node-RED to integrate DR capabilities in a smart grid for DR load control. The DR system employs the OpenADR standard.