Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (1/4)

Technical report about sustainable urban freight solutions, part 1 of

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (3/4)

Technical report about sustainable urban freight solutions, part 3 of

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (2/4)

Technical report about sustainable urban freight solutions, part 2 of

A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times

Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe

Integracija električnih vozila u energetske i transportne sustave

There is a strong tendency of development and application of different types of electric vehicles (EV). This can clearly be beneficial for transport systems in terms of making it more efficient, cleaner, and quieter, as well as for energy systems due to the grid load leveling and renewable energy sources exploitation opportunities. The latter can be achieved only through application of smart EV charging technologies that strongly rely on application of optimization methods. For the development of both EV architectures and controls and charging optimization methods, it is important to gain the knowledge about driving cycle features of a particular EV fleet. To this end, the paper presents an overview of (i) electric vehicle architectures, modeling, and control system optimization and design; (ii) experimental characterization of vehicle fleet behaviors and synthesis of representative driving cycles; and (iii) aggregate-level modeling and charging optimization for EV fleets, with emphasis on freight transport.U novije vrijeme postoji izražena težnja za razvojem i korištenjem različitih tipova električnih vozila. Ovo može biti korisno sa stanovišta transportnih sustava u smislu omogućavanja efikasnijeg, čišćeg, i tišeg transporta, kao i iz perspektive energetskih sustava zbog dodatnih potencijala za poravnanje opterećenja mreže i iskorištenje obnovljivih izvora energije. Potonje može biti ostvareno samo kroz korištenje tehnologija naprednog punjenja električnih vozila, koje se često temelje na primjeni optimizacijskih postupaka. Za razvoj prikladnih konfiguracija, upravljačkih sustava te metoda pametnog punjenja električnih vozila, potrebno je steći uvid u značajke voznih ciklusa razmatrane flote električnih vozila. Imajući u vidu navedeno, članak predstavlja pregled (i) konfiguracija i modeliranja električnih vozila, te optimiranja i sinteze njihova upravljačkog sustava; (ii) eksperimentalne karakterizacije ponašanja flote vozila i sinteze reprezentativnih voznih ciklusa; te (iii) modeliranja i optimiranja punjenja flote električnih vozila na agregatnom nivou, s naglaskom na teretni transport

Electric Vehicles Charging Control based on Future Internet Generic Enablers

In this paper a rationale for the deployment of Future Internet based applications in the field of Electric Vehicles (EVs) smart charging is presented. The focus is on the Connected Device Interface (CDI) Generic Enabler (GE) and the Network Information and Controller (NetIC) GE, which are recognized to have a potential impact on the charging control problem and the configuration of communications networks within reconfigurable clusters of charging points. The CDI GE can be used for capturing the driver feedback in terms of Quality of Experience (QoE) in those situations where the charging power is abruptly limited as a consequence of short term grid needs, like the shedding action asked by the Transmission System Operator to the Distribution System Operator aimed at clearing networks contingencies due to the loss of a transmission line or large wind power fluctuations. The NetIC GE can be used when a master Electric Vehicle Supply Equipment (EVSE) hosts the Load Area Controller, responsible for managing simultaneous charging sessions within a given Load Area (LA); the reconfiguration of distribution grid topology results in shift of EVSEs among LAs, then reallocation of slave EVSEs is needed. Involved actors, equipment, communications and processes are identified through the standardized framework provided by the Smart Grid Architecture Model (SGAM).Comment: To appear in IEEE International Electric Vehicle Conference (IEEE IEVC 2014

Transportation-mission-based Optimization of Heterogeneous Heavy-vehicle Fleet Including Electrified Propulsion

Commercial-vehicle manufacturers design vehicles to operate over a wide range of transportation tasks and driving cycles. However, certain possibilities of reducing emissions, manufacturing and operational costs from end vehicles are neglected if the target range of transportation tasks is narrow and known in advance, especially in case of electrified propulsion. Apart from real-time energy optimization, vehicle hardware can be meticulously tailored to best fit a known transportation task. As proposed in this study, a heterogeneous fleet of heavy-vehicles can be designed in a more cost- and energy-efficient manner, if the coupling between vehicle hardware, transportation mission, and infrastructure is considered during initial conceptual-design stages. To this end, a rather large optimization problem was defined and solved to minimize the total cost of fleet ownership in an integrated manner for a real-world case study. In the said case-study, design variables of optimization problem included mission, recharging infrastructure, loading--unloading scheme, number of vehicles of each type, number of trips, vehicle-loading capacity, selection between conventional, fully electric, and hybrid powertrains, size of internal-combustion engines and electric motors, number of axles being powered, and type and size of battery packs. This study demonstrated that by means of integrated fleet customization, battery-electric heavy-vehicles could strongly compete against their conventional combustion-powered counterparts. Primary focus has been put on optimizing vehicle propulsion, transport mission, infrastructure and fleet size rather than routing