Utilization of Rooftop Solar Units to Generate Electricity and Hydrogen: A Technoeconomic Analysis

This study is aimed at scrutinizing the domestic solar energy potential for electricity and hydrogen production. Under the first scenario, it is sought to evaluate electricity generation for household purposes using RSUs (rooftop solar units). Then, under the second scenario, solar hydrogen production is analyzed for the purpose of meeting a hydrogen vehicle demand. For this, one of the aptest cities, Yazd, located in the center of Iran is investigated. Furthermore, a real-world electric load needed by an usual household in Yazd is deemed as the demand for electricity. To analyze the two scenarios, a system consisting of an 8.2 kW RSU for power generation, a battery for electricity storage, and a 1 kW electrolyzer for hydrogen yield is proposed. Also, to acquire a broader vision, predictions are made for the next 10, 20, 30, and 40 years. The results regarding the first scenario implied that COE (Cost of Electricity) would be, respectively, 0.067, 0.145, 0.136, and 0.127 $/kWh. In addition to supplying the electricity required by the house, 2,687 $/yr could be earned by selling the excess electricity generated, and 5,759 kg of CO2 would be avoided a year. The findings as to the second scenario showed that LCOH (levelized cost of hydrogen) would equate to 3.62, 6.53, 6.34, and 5.93 $/kg, respectively, for the aforementioned project lifetimes. Furthermore, 2,464 $/yr would be the revenue after selling the surplus electricity, and 7,820 kg of CO2 would be saved, annually.

Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.

A queueing-theoretic framework for vehicle dispatching in dynamic car-hailing

With the rapid development of smart mobile devices, the car-hailing platforms (e.g., Uber or Lyft) have attracted much attention from the academia and the industry. In this paper, we consider a dynamic car-hailing problem, namely maximum revenue vehicle dispatching (MRVD), in which rider requests dynamically arrive and drivers need to serve riders such that the entire revenue of the platform is maximized. We prove that the MRVD problem is NP-hard and intractable. To handle the MRVD problem, we propose a queueing-based vehicle dispatching framework, which first uses existing machine learning models to predict the future vehicle demand of each region, then estimates the idle time periods of drivers through a double-sided queueing model for each region. With the information of the predicted vehicle demands and estimated idle time periods of drivers, we propose two batch-based vehicle dispatching algorithms to efficiently assign suitable drivers to riders such that the expected overall revenue of the platform is maximized during each batch processing. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets. In summary, our methods can achieve 3% ~ 10% increase on overall revenue without sacrificing on running speed compared with the state-of-the-art solutions.

The Effectiveness of Environmental Taxes in Reducing CO2 Emissions in Passenger Vehicles: The Case of Mediterranean Countries

The transport sector is the biggest source of CO2 emissions in Europe. It is responsible for over a quarter of all greenhouse gas emissions. Passenger vehicles, alone, account for nearly 41% of these emissions, resulting in human health impacts. To meet the Paris climate commitments, cars and vans should be decarbonized until 2050. Such a transformation requires general changes, such as how the vehicles are owned, taxed, and driven. The European Federation for Transport and Environment revealed that Mediterranean countries tend to emit less per vehicle compared to the northern and central Europeans. Intriguingly, this does not necessarily correspond to motorization rates. In this article, we assess whether the observed reductions in CO2 emissions in the Mediterranean countries can be attributed to vehicle taxation on CO2 emissions. We apply panel data econometric techniques using data on annual registrations from 2008 to 2018 and model the demand for new-vehicle purchases and their responsiveness to changes in both CO2-based taxation and circulation tax. Our results show the determinants of new-vehicle demand and the change in the emissions rate in each country under the taxation currently adopted. We found that fiscal policies can have an important role in reducing the emission in the Mediterranean countries.

Modeling Capacity of Through Movement at Signalized Intersection Affected by Short Left-Turn Bay under Different Signal Settings

Through movement capacity is an essential factor used to reflect intersection performance, especially for signalized intersections, where a large proportion of vehicle demand is making through movements. Generally, left-turn spillback is considered a key contributor to affect through movement capacity, and blockage to the left-turn bay is known to decrease left-turn capacity. Previous studies have focused primarily on estimating the through movement capacity under a lagging protected only left-turn (lagging POLT) signal setting, as a left-turn spillback is more likely to happen under such a condition. However, previous studies contained assumptions (e.g., omit spillback), or were dedicated to one specific signal setting. Therefore, in this study, through movement capacity models based on probabilistic modeling of spillback and blockage scenarios are established under four different signal settings (i.e., leading protected only left-turn [leading POLT], lagging left-turn, protected plus permitted left-turn, and permitted plus protected left-turn). Through microscopic simulations, the proposed models are validated, and compared with existing capacity models and the one in the Highway Capacity Manual (HCM). The results of the comparisons demonstrate that the proposed models achieved significant advantages over all the other models and obtained high accuracies in all signal settings. Each proposed model for a given signal setting maintains consistent accuracy across various left-turn bay lengths. The proposed models of this study have the potential to serve as useful tools, for practicing transportation engineers, when determining the appropriate length of a left-turn bay with the consideration of spillback and blockage, and the adequate cycle length with a given bay length.

How to Evaluate the Performance of Traction Battery for Electric Vehicles

With the rapid development of new energy vehicle technology, how to test and evaluate the performance of a traction battery has become a key issue in the field of new energy vehicle testing technology. Starting from the technical requirements of vehicle traction battery, this mini review introduces the key contents of battery performance test and evaluation from vehicle level and battery level respectively. Furthermore, based on the actual vehicle demand, the influence of electricity quality and feedback current on the performance of battery is introduced, and the feasibility of multi-stage reuse of traction battery is discussed.

Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand

A car-sharing system has been playing an important role as an alternative transport mode in order to avoid traffic congestion and pollution due to a quick growth of usage of private cars. In this paper, we propose a novel vehicle relocation system with a major improvement in threefolds: (i) data preprocessing, (ii) demand forecasting, and (iii) relocation optimization. The data preprocessing is presented in order to automatically remove fake demands caused by search failures and application errors. Then, the real demand is forecasted using a deep learning approach, Bidirectional Gated Recurrent Unit. Finally, the Minimum Cost Maximum Flow algorithm is deployed to maximize forecasted demands, while minimizing the amount of relocations. Furthermore, the system is deployed in the real use case, entitled “CU Toyota Ha:mo,” which is a car-sharing system in Chulalongkorn University. It is based on a web application along with rule-based notification via Line. The experiment was conducted based on the real vehicle usage data in 2019. By comparing in real environment in November of 2019, the results show that our model even outperforms the manual relocation by experienced staff. It achieved a 3% opportunity loss reduction and 3% less relocation trips, reducing human effort by 17 man-hours/week.