ABSCEV: An agent-based simulation framework about smart transportation for reducing waiting times in charging electric vehicles

[EN] Fuel has been the main source of energy for cars for many years, but the non-renewable resources are limited in the planet. In this context, electric vehicles (EVs) are increasingly replacing the previous kind of cars. However, as the number of EVs increases, some challenges arise such as the reduction of waiting times in the queues of fast charging stations. The current work addresses this challenge by means of social coordination mechanisms. In particular, this work presents an agent-based simulation framework for simulating the effects of different coordination policies in the route planning of EV drivers for charging their vehicles on their trips. In this manner, researchers and professionals can test different coordination mechanisms for this purpose. This framework has been experienced by simulating an adaptive strategy based on the implicit communication through booking systems in the charging stations. This strategy was compared with another common strategy, which was used as the control mechanism. This comparison was done by simulating several scenarios in two Spanish cities (i.e. Madrid and Zaragoza). The experimental results show that the current approach was useful to propose a route planning strategy that had statistically significant improvements in the reduction of waiting times in charging stations and also in the global trip times. In addition, the evolutions of pathfinding execution times and the numbers of interchanged messages did not show any overloading pattern over the time. (C) 2018 Elsevier B.V. All rights reservedWe acknowledge the research project "Construccion de un framework para agilizar el desarrollo de aplicaciones mviles en el ambito de la salud" funded by University of Zaragoza and Foundation Ibercaja with grant reference JIUZ-2017-TEC-03. This work has been supported by the program "Estancias de movilidad en el extranjero Jose Castillejo para jovenes doctores" funded by the Spanish Ministry of Education, Culture and Sport with reference CAS17/00005. We also acknowledge support from "Universidad de Zaragoza", "Fundacion Bancaria Ibercaja" and "Fundacion CAI" in the "Programa Ibercaja-CAI de Estancias de Investigacion" with reference IT1/18. This work acknowledges the research project "Desarrollo Colaborativo de Soluciones AAL" with reference TIN2014-57028-R funded by the Spanish Ministry of Economy and Competitiveness. It has also been supported by "Organismo Autonomo Programas Educativos Europeos" with reference 2013-1-CZ1-GRU06-14277. We also acknowledge support from project "Sensores vestibles y tecnologa movil como apoyo en la formacin y practica de mindfulness: prototipo previo aplicado a bienestar" funded by University of Zaragoza with grant number UZ2017-TEC-02.García-Magariño, I.; Palacios-Navarro, G.; Lacuesta Gilaberte, R.; Lloret, J. (2018). ABSCEV: An agent-based simulation framework about smart transportation for reducing waiting times in charging electric vehicles. Computer Networks. 138:119-135. https://doi.org/10.1016/j.comnet.2018.03.01411913513

Exploring the Role of Cities in Electrifying Passenger Transportation

Key Takeaways1. The electrification of passenger vehicles should be one part of a city’s transportation plan. Shifting from internal combustion engine vehicles to plug-in electric vehicles (PEVs) can improve urban air quality, reduce greenhouse gas emissions, and reduce energy consumption.2. Recent studies show that electric vehicle awareness is low even in mature markets; cities should promote electric vehicles to residents by leveraging existing promotional campaigns.3. Various financial and non-financial incentives can effectively encourage electric vehicle uptake, including: free, discounted, or preferential-location parking; free or reduced road and bridge tolls; and allowing electric vehicles to drive in bus or carpool lanes.4. Several cities are restricting or planning to restrict the access that internal combustion engine vehicles (ICEVs) have to certain areas. If these restrictions apply to most (or all) passenger ICEVs, they can promote PEV purchase and use in cities.5. Infrastructure development in cities should follow the same fundamental approach as that used outside of cities. The priority should be ensuring that PEV owners and prospective PEV buyers have access to charging at or near home. Workplace and public charging should be developed for those who cannot access charging at or near home.6. Cities should be strategic in their approach, first identifying the goals they want to achieve, and then exploring what steps they can take to meet these goals. The steps available will likely differ between cities due to the different ways in which roads, parking, and any other vehicle infrastructure is governed

Deployment of Autonomous Electric Taxis with Consideration for Charging Stations

Autonomous electric vehicles are set to replace most conventional vehicles in the near future. Extensive research is being done to improve efficiency at the individual and fleet level. There is much potential benefit in optimizing the deployment and rebalancing of Autonomous Electric Taxi Fleets (AETF) in cities with dynamic demand and limited charging infrastructure. We propose a Fleet Management System with an Online Optimization Model to assign idle taxis to either a region or a charging station considering the current demand and charging station availability. Our system uses real-time information such as demand in regions, taxi locations and state of charge (SoC), and charging station availability to make optimal decisions in satisfying the dynamic demand considering the range-based constraints of electric taxis. We integrate our Fleet Management System with MATSim, an agent-based transport simulator, to simulate taxis serving real on-demand requests extracted from the San Francisco taxi mobility dataset. We found our system to be effective in rebalancing and ensuring efficient taxi operation by assigning them to charging stations when depleted. We evaluate this system using different performance metrics such as passenger waiting time, fleet efficiency (taxi empty driving time) and charging station utilization by varying initial SoC of taxis, frequency of optimization and charging station capacity and power

Uncertain demand prediction for guaranteed automated vehicle fleet performance

Mobility-on-demand (MoD) services offer a convenient and efficient transportation option, using technology to replace traditional modes. However, the flexibility of MoD services also presents challenges in controlling the system. One of the major issues is supply-demand imbalance, caused by uneven stochastic travel demand. To address this, it is crucial to predict the network behavior and proactively adapt to future travel demand.In this thesis, we present a stochastic model predictive controller (SMPC) that accounts for uncertainties in travel demand predictions. Our method make use of Gaussian Process Regression (GPR) to estimate passenger travel demand and predict time patterns with uncertainty bounds. The SMPC integrates these demand predictions into a receding horizon MoD optimization and uses a probabilistic constraining method with a user-defined confidence interval to guarantee constraint satisfaction. This result in a Chance Constrained Model Predictive Control (CCMPC) solution. Our approach has two benefits: incorporating travel demand uncertainty into the MoD optimization and the ability to relax the solution into a simpler Mixed-Integer Linear Program (MILP). Our simulation results demonstrate that this method reduces median customer wait time by 4% compared to using only the mean prediction from GPR. By adjusting the confidence bound, near-optimal performance can be achieved

Invest in fast-charging infrastructure or in longer battery ranges? A cost-efficiency comparison for Germany

To reach ambitious CO₂ mitigation targets, the transport sector has to become nearly emission-free and the most promising option for passenger cars are battery electric vehicles (BEV) powered using renewable energy. Despite their important benefits, BEV still face technological barriers, mainly their limited battery range and the limited availability of public fast-charging infrastructure. These factors are hindering the diffusion of electric vehicles (EV). The question of how to address these technical barriers has been widely analyzed in the literature, but so far there has been no cost-efficiency comparison of longer battery ranges and more widespread fast-charging infrastructure that evaluates them both technically and economically. This paper aims to find cost-efficient ways to address limited battery ranges and availability of public fast-charging infrastructure. We focus on German passenger cars that are licensed to commercial owners, since these are an important first market for EV. Our results indicate that fast-charging infrastructure is very cost-efficient as it enables significant proportions of BEV in the fleet at low infrastructure density. The technically feasible maximum BEV shares in the commercial sector can only be achieved with longer battery ranges. However, longer battery ranges are currently associated with comparatively high additional costs

Exploring Carsharing Diffusion Challenges through Systems Thinking and Causal Loop Diagrams

The diffusion of carsharing in cities can potentially support the transition towards a sustainable mobility system and help build a circular economy. Since urban transportation is a complex system due to the involvement of various stakeholders, including travelers, suppliers, manufacturers, and the government, a holistic approach based on systems thinking is essential to capture this complexity and its causalities. In this regard, the current research aims at identifying cause-and-effect relationships in the diffusion of carsharing services within the urban transport systems. To do so, a causal loop diagram (CLD) is developed to identify and capture the causalities of carsharing adoption. On this basis, the main four players within the carsharing domain in urban transportation were scrutinized and their causes and effects were visualized, including (i) the characteristics, behavior, and dynamics of the society population; (ii) transportation system and urban planning; (iii) the car manufacturing industry; and (iv) environmental pollution. The developed CLD can support decision-makers in the field of urban transport to gain a holistic and systemic approach to analyzing the issues within the transport sector due to their complexity. Moreover, they can help regulators and policymakers in intensifying the diffusion of more sustainable modes of transport by highlighting the role of population, car manufacturing, the transportation system, and environmental pollution

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019