Factors Affecting Demand for Plug-in Charging Infrastructure: An Analysis of Plug-in Electric Vehicle Commuters

The public sector and the private sector, which includes automakers and charging network companies, are increasingly investing in building charging infrastructure to encourage the adoption and use of plug-in electric vehicles (PEVs) and to ensure that current facilities are not congested. However, building infrastructure is costly and, as with road congestion, when there is significant uptake of PEVs, we may not be able to “build out of congestion.” We modelled the choice of charging location that more than 3000 PEV drivers make when given the options of home, work, and public locations. Our study focused on understanding the importance of factors driving demand such as: the cost of charging, driver characteristics, access to charging infrastructure, and vehicle characteristics. We found that differences in the cost of charging play an important role in the demand for charging location. PEV drivers tend to substitute workplace charging for home charging when they pay a higher electricity rate at home, more so when the former is free. Additionally, socio-demographic factors like dwelling type and gender, as well as vehicle technology factors like electric range, influence the choice of charging location

Side-Channel Watchdog: Run-Time Evaluation of Side-Channel Vulnerability in FPGA-Based Crypto-systems

Besides security against classical cryptanalysis, its important for cryptographic implementations to have sufficient robustness against side-channel attacks. Many countermeasures have been proposed to thwart side channel attacks, especially power trace measurement based side channel attacks. Additionally, researchers have proposed several evaluation metrics to evaluate side channel security of crypto-system. However, evaluation of any crypto-system is done during the testing phase and is not part of the actual hardware. In our approach, we propose to implement such evaluation metrics on-chip for run-time side channel vulnerability estimation of a cryptosystem. The objective is to create a watchdog on the hardware which will monitor the side channel leakage of the device, and will alert the user if that leakage crosses a pre-determined threshold, beyond which the system might be considered vulnerable. Once such alert signal is activated, proactive countermeasures can be activated either at the device level or at the protocol level, to prevent the impending side channel attack. A FPGA based prototype designed by us show low hardware overhead, and is an effective option that avoids the use of bulky and inconvenient on-field measurement setup

Exploiting Safe Error based Leakage of RFID Authentication Protocol using Hardware Trojan Horse

Radio-Frequency Identification tags are used for several applications requiring authentication mechanisms, which if subverted can lead to dire consequences. Many of these devices are based on low-cost Integrated Circuits which are designed in off-shore fabrication facilities and thus raising concerns about their trust. Recently, a lightweight entity authentication protocol called LCMQ was proposed, which is based on Learning Parity with Noise, Circulant Matrix, and Multivariate Quadratic problems. This protocol was proven to be secure against Man-in-the-middle attack and cipher-text only attacks. In this paper, we show that in the standard setting, although the authentication uses two $m$ bit keys, $\mathbf{K_1}$ and $\mathbf{K_2}$, knowledge of only $\mathbf{K_2}$ is sufficient to forge the authentication. Based on this observation, we design a stealthy malicious modification to the circuitry based on the idea of Safe-errors to leak $\mathbf{K_2}$ and thus can be used to forge the entire authentication mechanism. We develop a Field Programmable Gate Array prototype of the design which is extremely lightweight and can be implemented using four Lookup tables

CFD-based j-shaped blade design improvement for vertical axis wind turbines

The need for an increase in energy harvesting has led to novel ideas and designs to extract more power from wind. One innovative solution is through the use of J-shaped blades for Darrieus vertical axis wind turbines (VAWTs), which is based on the removal of a portion of a conventional blade, either on the pressure or suction side. Although improvements in the self-starting capabilities of VAWTs have been reported when using such blades, the literature only studied hollow blades, showing a hair-like structure. This work numerically investigates six different J-shaped designs. A turbine comprising NACA0015-based blades forms the base case and is used to evaluate the 2D numerical models. Results show that blades with an external cut systematically outperform those designed with an internal cut. In addition, all proposed cut-based designs are shown to improve the starting torque of the turbine, reaching a 135% increase compared to the base model

Understanding the Impact of Charging Infrastructure on the Consideration to Purchase an Electric Vehicle in California

UC-ITS-2031-34Supplemental Material: Survey QuestionsThis research makes explicit and tests an implicit assumption in policies promoting public investment in plug-in electric vehicle (PEV) charging infrastructure: even people who are not already interested in PEVs see public PEV charging. Data from a survey representing all car-owning households in California are combined with per capita counts of public PEV charging locations and PEV registrations to estimate a structural equation model for two central variables: the extent to which participants have already considered acquiring a battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV), and whether and how many places people see PEV charging. The model controls for socio-economic and demographic measures as well as participants\u2019 awareness, knowledge, and assessments of PEVs

Exploring the Impact of the Federal Tax Credit on the Decision to Lease or Purchase a PEV in California

USDOT Grant 69A3551747114Using a sample of approximately 7,000 California PEV drivers recruited from California Clean Vehicle Rebate Program applicants, two logistic regression models are specified to analyze responses by PEV lessees and purchasers to the question of what they would do in the absence of the federal tax credit. Possible responses include: Purchase/lease the same PEV, switch to a different PEV, switch to a conventional or hybrid (non-plug in) vehicle, or do not acquire a vehicle at all. Several key insights are found; higher discounts from the tax credit increase the probability of lessees indicating they would not lease a PEV at all. For purchasers, in addition to not purchasing any vehicle at all, the probability of purchasing a conventional vehicle, or another PEV also increase. These findings could have implications for California\u2019s ability to reach its ZEV milestones and are important to consider due to recent changes to the US federal tax credit. Our findings indicate that many PEV adopters would likely not adopt their PEV without the tax credit, potentially more so for leased compared to purchased vehicles. There are also unique results for lessees related to the impact of home ownership; renters are more likely than homeowners to lease a conventional vehicle than a PEV in the absence of the tax credit. This finding contributes to the literature which finds homeowners to be more likely to adopt a PEV than renters, emphasizing the importance of access to at-home charging for PEV adoption. These results show how incentives may be more influential for adoption decisions in the PEV lease market is, and the factors which are associated with consumers\u2019 PEV adoption behavior in the absence of the federal tax credit

Developing a Vehicle Cost Calculator to Promote Electric Vehicle Adoption among TNC Drivers

USDOT Grant 69A3551747114This research developed EV Explorer 2.0, an online vehicle cost calculator (VCC) to meet the requirements of transportation network company (TNC) drivers considering acquiring an electric vehicle (EV). The tool was built to also support the needs of other users considering an EV, including other types of gig economy drivers as well as the general population of non-professional drivers. EV Explorer 2.0 includes several important features and functionalities to support the TNC driver use case that are not found in any other available tool: (1) It allows users to estimate TCO for used vehicles as well as new (others only estimate TCO for new vehicles); (2) Outputs include ridehail-driving income estimates, accounting for EV trip bonuses offered by Uber, net driving costs; (3) Estimates of total cost of driving (TCD) include charging network membership fees and charging session fees (in addition to electricity prices). It also includes key features found in other leading tools, such as presenting and tailoring EV purchase/lease incentive estimates (based on a database we developed), and innovative features to benefit all users, such as animations conveying the social and environmental impacts of vehicle choice. Design features were informed and validated in user testing with TNC drivers who had expressed interest in EV adoption