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- All Subjects: EV Charging
- Creators: Electrical Engineering Program
The final testing circuit and Mock-RRAM are varied and complex but come together to be able to produce a measured value of the high resistance and low resistance state. This is done by the Arduino autonomously digitizing the anode voltage, cathode voltage, and output voltage. A ramp voltage that sweeps from 1V to -1V is applied to the Mock-RRAM acting as an input. This ramp voltage is then later defined as the anode voltage which is just one of the two nodes connected to the Mock-RRAM. The cathode voltage is defined as the other node at which the voltage drops across the Mock-RRAM. Using these three voltages as input to the Arduino, the Mock-RRAM path resistance is able to be calculated at any given point in time. Conducting many test cycles and calculating the high and low resistance values allows for a graph to be developed of the chaotic variation of resistance state values over time. This chaotic variation can then be analyzed further in the future in order to better predict trends and characterize the RRAM cell that was tested.
Furthermore, the interchangeability of many devices on the PCB allows for the testing system to do more in the future. Ports have been added to the final PCB in order to connect a packaged RRAM cell. This will allow for the characterization of a real RRAM memory cell later down the line rather than a Mock-RRAM as emulation. Due to the autonomous testing, very few human intervention is needed which makes this board a great baseline for others in the future looking to add to it and collect larger pools of data.
As more electric vehicles (EVs) are adopted, users need a solution to meet their expectations when it comes to Level 2 EV Charging (EVC). Currently, Adaptive Charging (AC) Techniques are used in multi-unit, public, settings. In the future, AC should be utilized to provide an optimized charging experience for the EV user in a single-unit residential application. In this experiment, an Electric Vehicle simulation tool was created using Python. A training dataset was generated from Alternative Fuels and Data Center (EVI-Pro) using charging data from Phoenix, Arizona. Similarly, the utility price plan chosen for this exercise was SRP Electric Vehicle Price plan. This will be the cost-basis for the thesis. There were four cases that were evaluated by the simulation tool. (1) Utility Guided Scheduling (2) Automatic Scheduling (3) Off-Site Enablement (4) Bidirectional enablement. These use-cases are some of the critical problems facing EV users when it comes to charging at home. Each of these scenarios and algorithms were proven to save the user money in their daily bill. Overall, the user will need some sort of weighted scenario that considers all four cases to provide the best solution to the user. All four scenarios support the use of Adaptive Charging techniques in residential level 2 electric vehicle chargers. By applying these techniques, the user can save up to 90% on their energy bill while offsetting the energy grid during peak hours. The adaptive charging techniques applied in this thesis are critical to the adoption of the next generation electric vehicles. Users need to be enabled to use the latest and greatest technology. In the future, individuals can use this report as a baseline to use an Artificial Intelligence model to make an educated case-by-case decision to deal with the variability of the data.