A primary need of Forensic science is to individualize missing persons that cannot be identified after death. With the use of advanced technology, Radio Frequency Identification (RFID) implant chips can drastically improve digital tracking and enable robust biological and legal identification. In this paper, I will discuss applications between different microchip technologies and indicate reasons why the RFID chip is more useful for forensic science. My results state that an RFID chip is significantly more capable of integrating a mass volume of background information, and can utilize implanted individuals’ DNA profiles to decrease the missing persons database backlogs. Since today’s society uses a lot of digital devices that can ultimately identify people by simple posts or geolocation, Forensic Science can harness that data as an advantage to help serve justice for the public in giving loved ones closure.

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.