Early battery electric vehicle (EV) adopters can access home chargers for reliable charging. As the EV market grows, residents of multi-unit dwellings may face barriers in owning EVs and charging them without garage or parking availability. To investigate the mechanisms that can bridge existing disparities in home charging and station deployment, we characterized the travel behavior of multi-unit dwelling residents and estimated their EV residential charging demand. This study classifies the travel patterns of multi-unit dwelling (MUD) residents by fusing trip diary data from the National Household Travel Survey and housing features from the American Housing Survey. A hierarchical agglomerative clustering method was used to cluster apartment complex residents’ travel profiles, considering attributes such as dwell time, daily vehicle miles traveled (VMT), income, and their residences’ US census division. We propose a charging decision model to determine the charging station placement demand in MUDs and the charging energy volume expected to be consumed, assuming that MUD drivers universally operate EVs in urban communities. Numerical experiments were conducted to gain insight into the charging demand of MUD residents in the US. We found that charging availability is indispensable for households that set out to meet 80% state of charge by the end of the day. When maintaining a 20% comfortable state of charge the entire day, the higher the VMT are, the greater the share of charging demand and the greater the energy use in MUD chargers. The upper-income group requires a greater share of MUD charging and greater daily kWh charged because of more VMT.
Although electromagnetic simulation tools are routinely employed for numerical dosimetry, the uncertainty in the calculation is rarely stated. The uncertainty quantification in specific absorption rate (SAR) calculations using realistic mobile phone models is addressed. Since the SAR calculation using the complex computer-aided design (CAD) model of a commercially available mobile phone is numerically expensive, the traditional Monte Carlo (MC) simulation approach proves to be impracticable for the purpose of uncertainty evaluation. Two alternative and computationally efficient techniques -the unscented transform (UT) and the polynomial chaos (PC) methods-are examined for the uncertainty quantification in the SAR calculation using a realistic model of a commercially available mobile phone. Both methods may be applied non-intrusively and the electromagnetic simulation tool may be considered as a black box with the uncertainty in the inputs -e.g. the relative permittivity values of the mobile phone materials-inducing an uncertainty in the output, e.g. the peak spatial-average SAR. The numerical simulation results show that both proposed methods are potential candidates for the uncertainty quantification in SAR calculations since only a few simulations are necessary to obtain results similar to those obtained after hundreds or thousands of MC simulations.
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