Abstract

Electric buses are particularly suitable for city and suburban routes due to zero local exhaust and noise emissions. The operation schedule interval defines the charging power, bus fleet size and total cost of ownership of a bus. We propose a novel cost-benefit method for the scheduling of an electric city bus fleet on a single route. Three different charging infrastructure scenarios were considered. In the first scenario, only one charging station was used. The second scenario considered two charging stations that were located at the same terminus. In the third scenario, two charging stations were located at opposite terminuses. The costs and utilization rates of the buses were analyzed with operation intervals up to 40 minutes. The first scenario with a single charging station had the lowest costs for the entire bus fleet system when the utilization rate was considered. Furthermore, the results show that certain schedule intervals are more cost-beneficial in terms of vehicle specific life-cycle costs than others. In the future, the proposed method is expanded to aid the design of bus network scheduling under energy demand uncertainty. Peer reviewed

Abstract

Cities have become the most common living environment for humans. With this rising urbanization, urban design has become vital for these growing cities. While measuring objective data like traffic congestion or air quality is important, it does not tell the whole story of how people live in the cities or how cities should be developed to make them more livable. In future for a true smart city a more humane component is needed to understand how the population of cities actually interact with and feel about their surroundings. Surveys are a great and a necessary tool for this and they are already being used in the design process. However, they require effort and and a lot of silent information can be missed. The surveying process also doesn't happen in real time. We suggest that social media data could be used to gather more information about human- environment interaction in cities and compliment the surveys. We show a working prototype of a tool that creates an emotional map of a city by mining social media data for sentiments and heatmapping them. This kind of method could prove to be an useful tool for urban designers, who could take advantage of the visual intuition of humans and see instantly where and how emotional hotspots arise. It could also be of interest for emotion researchers, who could get data on what it really means to be happy for a human being - for example eating an ice cream at the beach - instead of only linking conceptual words (such as happy) to external stimuli (such as smiling). Peer reviewed

Abstract

Parasitic power requirement is a key criterion in selection of suitable battery thermal management system (TMS) for EV applications. This paper presents a hybrid TMS with negative parasitic requirements, designed by integrating phase change material (PCM) with thermoelectric devices. The proposed system does not require any power consumption to maintain tight control over battery cell temperature during aggressive use and repetitive cycling. In addition, it can recover a portion of waste heat produced during the typical operation of EV battery packs."/jats:p" "jats:p"Commercially available LiFeP04 20 Ah pouch cell has been chosen as a test battery sample for validating the conceptual design presented herein. The commercial battery cells, submerged in a PCM-filled polycarbonate casing, are subjected to a cyclic discharge process to elucidate their heat generation characteristics at 27 °C. Charging and discharging is conducted at 0.5C and 1C, respectively. A thermoelectric circuit is used to recover the heat energy absorbed by the PCM and to convert it to electrical energy. The manuscript further details some of the major findings of this experiment.

Abstract

Vehicle-to-building (V2B) technology permits bypassing the power grid in order to supply power to a building from electric vehicle (EV) batteries in the parking lot. This paper investigates the hypothesis stating that the increasing number of EVs on our roads can be also beneficial for making buildings sustainably greener on account of using V2B technology in conjunction with local photovoltaic (PV) generation. It is assumed that there is no local battery storage other than EVs and that the EV batteries are fully available for driving, so that the EVs batteries must be at the intended state of charge at the departure time announced by the EV driver. Our goal is to exploit the potential of the EV batteries capacity as much as possible in order to permit a large area of solar panels, so that even on sunny days all PV power can be used to supply the building needs or the EV charging at the parking lot. A system architecture and collaboration protocols that account for uncertainties in EV behaviour are proposed. The proposed approach is proven in simulation covering one year period for three locations in different climatic regions of the US, resulting in the electricity bill reductions of 15.8%, 9.1% and 4.9% for California, New Jersey and Alaska, respectively. These results are compared to state-of-the-art research in combining V2B with PV or wind power generation. It is concluded that the achieved electricity bill reductions are superior to the state-of-the-art, because previous work is based on problem formulations that exploit only a part of the potential EV battery capacity. Document type: Article

Abstract

Evaluating potential of shifting to low-carbon transport modes requires considering limited travel-time budget of travelers. Despite previous studies focusing on time-relevant modal shift, there is a lack of integrated and transferable computational frameworks, which would use emerging smartphone-based high-resolution longitudinal travel datasets. This research explains and illustrates a computational framework for this purpose. The proposed framework compares observed trips with computed alternative trips and estimates the extent to which alternatives could reduce carbon emission without a significant increase in travel time.. The framework estimates potential of substituting observed car and public-transport trips with lower-carbon modes, evaluating parameters per individual traveler as well as for the whole city, from a set of temporal and spatial viewpoints. The illustrated parameters include the size and distribution of modal shifts, emission savings, and increased active-travel growth, as clustered by target mode, departure time, trip distance, and spatial coverage throughout the city. Parameters are also evaluated based on the frequently repeated trips. We evaluate usefulness of the method by analyzing door-to-door trips of a few hundred travelers, collected from smartphone traces in the Helsinki metropolitan area, Finland, during several months. The experiment’s preliminary results show that, for instance, on average, 20% of frequent car trips of each traveler have a low-carbon alternative, and if the preferred alternatives are chosen, about 8% of the carbon emissions could be saved. In addition, it is seen that the spatial potential of bike as an alternative is much more sporadic throughout the city compared to that of bus, which has relatively more trips from/to city center. With few changes, the method would be applicable to other cities, bringing possibly different quantitative results. In particular, having more thorough data from large number of participants could provide implications for transportation researchers and planners to identify groups or areas for promoting mode shift. Finally, we discuss the limitations and lessons learned, highlighting future research directions. Document type: Article

Abstract

Recently, the use of renewable energy sources (RES) and electric vehicles (EVs) has been rapidly increased worldwide. As a result of the highly fluctuating nature of RES, the charging and discharging rates of EVs significantly have to be increased, and so the lifespan of EV batteries decreases. In this paper, an optimization-based method is proposed to smooth voltage fluctuations due to various RES types by optimally controlling the charging and discharging power of EVs and the reactive power of the RES inverters. To extend the lifespan of the EV battery, EV power fluctuations and their minimum preset state of charge (SOC) are considered in the proposed optimization model. For this purpose, a new multi-objective function is formulated, including (1) voltage fluctuations, (2) EV power fluctuations, and (3) the deviation of SOC of EVs from their minimum desired level. The use of the hull moving average (HMA) is proposed to mitigate voltage fluctuations, which eliminates the lag problem of the widely used movingaverage methods. The gravitational search algorithm (GSA) is utilized to accurately solve the optimization model. The simulation results demonstrate the effectiveness of the proposed method to smooth voltage fluctuations while considering degradation and charging plan of EV batteries. Peer reviewed