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Vehicle to Grid Implementation and Battery Management Optimization

  • Author(s): Zhao, Yuchen
  • Advisor(s): Barth, Matthew
  • et al.
Creative Commons 'BY' version 4.0 license
Abstract

ABSTRACT OF THE DISSERTATION

Vehicle to Grid Implementation and Battery Management Optimization

by

Yuchen Zhao

Master of Science, Graduate Program in Electrical Engineering

University of California, Riverside, September 2017

Dr. Matthew Barth, Chairperson

The need for energy independence and rising environmental pollution concerns are factors that drive the growing popularity of electric vehicles (EV), including electric and plug-in hybrid cars. Studies indicate the for 90% of the Americans who use their cars to get to work every day, on average, passenger vehicles are driven 1 to 2 hours a day, the remaining 22 to 23 hours they are parked, most often either at home or at work. The average distance driven each day is around 30 miles. However, a typical electrical vehicle has range of about 100 miles, thus, there would be around 70 miles’ battery capacity left in the vehicle. Thus, we can make use of the remaining battery capacity in EV as a potential energy supply. To perform adequately at highway speeds, electric vehicles require an output peak of 100 kW when they are accelerating. Therefore, the vehicle electronics are already sized for power output at levels above standard AC vehicle connection, giving electric vehicles the potential and ability to provide energy to outside when parked for most of time. In addition, the EVs typically have common battery standards, which provide storage capability that can be effectively harnessed. It happens when the vehicles are integrated to the energy grid and can provide substantial power available to and from the grid with the existing vehicle systems. The entire communication technology of using the EVs as a distributed energy resource to receive and sent back power is known as the Vehicle-to-Grid or V2G technology.

The University of California, Riverside has started a Vehicle to Grid project as a part of Sustainable Integrated Grid Initiative (SIGI) system in 2014. This project uses light duty electric vehicles and a large trolley electric bus as supplementary energy storage source to provide energy to building, thereby reducing the electric bill cost. In addition, it’s necessary to consider batteries life cycle and EVs’ driving schedule. Thus, EVs are not ready to supply power all the time and the charging and discharging events would reduce the batteries capacity. Therefore, this thesis introduces an Adaptive Peak Control (APC) algorithm to control the electric system so that batteries would discharge occasionally according to the building load trends while simultaneously protecting batteries in the same time.

This thesis introduces a Vehicle to Grid research system which mainly focuses on: (1) the testing of electric vehicle battery performance during charging and discharging events, (2) the construction and implementation of the connection between an electric trolley and the energy grid; and (3) battery charging and discharging management optimization considering price policy and battery life cycle behavior. It is important to note that allowing EVs to charge and discharge without any control may cause the increase of peak load and electric bill cost, or may cause the insufficient discharges that are unable to lower the peak load. Within smart controls, the EV energy source can lower the bill cost by discharging during on-peak price period and charging during off-peak price period to store energy. In this thesis, we utilize Adaptive Peak Control (APC) algorithm as a real-time control method that manages discharging operation of an electric trolley and helps the building better manage its energy. Simulation results are illustrated (using MATLAB), showing the advantage of this algorithm. The proposed method is compared with a constant threshold model predictive control (CT-MPC) method, which is also used for battery management.

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