Date of Publication


Document Type

Master's Thesis

Degree Name

Master of Science in Mechanical Engineering

Subject Categories

Mechanical Engineering


Gokongwei College of Engineering


Mechanical Engineering

Thesis Advisor

Neil Stephen A. Lopez

Defense Panel Chair

Jose Bienvenido Manuel M. Biona

Defense Panel Member

Laurence A. Gan Lim
Ivan Henderson V. Gue


Electric vehicle (EV) use is growing at a steady rate globally. Many countries are planning to ban combustion engines starting 2030. One of the key issues needed to be addressed before the full-scale deployment of EVs is ensuring energy security. In this study, two different approaches will be presented to generate EV charging models – one is an improved model using an individual-based discrete event simulation, which will allow key characteristics of individual EV users to be modeled, including the availability of electric vehicle supply equipment (EVSE) outside homes and the charging threshold of EV users. This study will investigate the effect of varying the availability of EVSE on the electricity demand of EV charging. The second approach is the travel diary method in which the activities/location of respondents are recorded (30-minute time-step) by the means of a survey. The empirical data gathered through the survey are used in the simulation process. The travel data from the respondents were infused with algorithms to produce a collective EV charging model. The results of the two models were compared with each other and were used to investigate the potential effects of EV diffusion and expansion of EVSE infrastructure. The effect of large-scale EV charging on the power grid is also investigated. Results from both methods agree with previous studies that daily charging demands do not significantly vary. However, the results from both methods showed a significant shift in the charging schedule during weekends. Both simulation methods projected peak demands at around 8 AM and 7 PM. The varying of EVSE availability affects the electricity demands at specific periods. It was observed that the electricity demand during the usual time when people stay indoors decreases as the outdoor charging availability increases. The electricity demand during the usual time when people stay outdoors increases as the outdoor charging availability increases. The TDS simulations showed that the charging threshold is proportional to the charging demand. It was found that 1AM to 8AM is best time to apply TOU rates for EV charging.

Abstract Format







Electric vehicles; Battery charging stations (Electric vehicles)

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