Electric vehicles (EVs) present an opportunity to develop integrated and intelligent transport and energy networks. The employment of smart management technology to plan and control activity in both these sectors could see significant improvements in efficiency and emissions (1). Motivated by a need to reduce carbon emissions and backed by commitments to embrace renewable energy, European authorities are committed to adopting new and efficient forms of transport. The first step in the adoption process is the in depth research of the requirements and potential benefits of new technologies. This includes developing an understanding of the technological needs, such as infrastructure and ICT architecture, combined with knowledge of vehicle performance and the attitudes of consumers to changes in mobility patterns. In addition to the gaining an understanding of the performance of new vehicles and associated support infrastructure, it is also necessary to understand the effect that electric vehicles will have on energy supply and generation systems. Governments and utilities across Europe are introducing renewable energy both as a means of building more sustainable systems and reducing carbon emissions (2). These clean energy sources can bring with them new problems of demand management and predictability. Electromobility holds great potential for increasing efficiency in the energy production and management process through regulation and control of what is traditionally a major energy usage sector (3, 4). The nature of energy delivery and usage in the transport sector is changing and electricvehicle research can play a large part in managing the transition by providing insights into user behaviour and the resulting influences on wider energy systems.
and multisite project clinical teams utilize comprehensive age- and disease-specific measures that are implemented as widely as possible and checked regularly for reliability. PBE data are merged into a central study database for analysis and hypothesis testing. Study findings are then implemented into clinical practice for validation testing with the ultimate goal of integration into standard care. PBE studies are designed to improve on traditional observational studies by 1) examining large, diverse patient populations; 2) involving clinicians in the research design and datacollection; 3) using carefully selected patient characteristics for analysis to avoid bias; and 4) standardizing datacollection and treatment documentation at all research sites. PBE methodology is ideal for conducting “pragmatic” trials that are designed to measure the overall benefit produced by a treatment in a naturalistic clinical setting. 9 Reviews in the pain medicine literature indicate
Methods/Design: A pan-Canadian observational study was conducted in a convenience sample of public health clinics and healthcare institutions during the H1N1 vaccination campaign in the fall of 2009. The study design consisted of three stages: Stage 1 involved passive observation of the site's layout, processes and client flow; Stage 2 entailed timing site staff on 20 clients through five core immunization tasks: i) client registration, ii) medical history collection, iii) medical history review, iv) vaccine administration record keeping and v) preparation of proof of vaccine administration for the client; in Stage 3, site staff completed a questionnaire regarding perceived usability of the site's datacollection approach. Before the national study began, a pilot study was conducted in three seasonal influenza vaccination sites in Ontario, to both test that the proposed methodology was logistically feasible and to determine inter-rater reliability in the measurements of the research staff. Comparative analyses will be conducted across the range of datacollection methods with respect to time required to collect immunization data, number and type of individual-level data elements collected, and clinic staff perceptions of the usability of the method employed at their site, using analysis of variance (ANOVA).
EVs. This datacollection and analysis formed one of a number of user behaviour trials of EVs across Europe, part funded by the European Union on the Green eMotion project. Green eMotion (Green eMotion, 2015) was a pan-European project aimed at developing the European framework for an interoperable electromobility system, beginning in March 2011 and concluding in February 2015. Once the charging profiles of the users had been observed in the Irish trial, the next step was to estimate how the electrical demand by EVs would scale up, as EV penetration increases in the Irish car market. To answer this research question, estimates of likely EV penetration were needed. To inform their selection, the 2011 Irish census data were analysed to profile the existing household population in Ireland in terms of car ownership levels and work car trip duration. The focus on these two characteristics was driven by the assumption that households having more than one car may be more likely to replace an existing vehicle in the household with an EV, that people may be more likely to purchase an EV if their roundtrip to work is well within battery range and finally the availability of this information in the census. (Very few questions with a transport focus are included in the Irish census form and this was a limiting factor in the analysis completed here). The next step in the process was to characterise the potential EV market penetration projections under a number of economic growth level scenarios. The final step in the work was to estimate the impact of the different EV penetration scenarios on the electrical grid in terms of charging demands and to assess the ability of the existing grid to cope with those demands.
The purpose of a public passenger transport mode is to carry flows of people large enough to generate economies of scale and scope on one or more line services (i.e. transit routes). In relation to fixed resources and a quality of service objective, transport capacity is the maximum volume of flow that can be handled in standard conditions for a limited period. Since the service requires the combination of an infrastructure, a vehicle and a traffic protocol, capacity depends on the arrangements for each of the components: for instance the size and passenger capacity of a vehicle, or the fleet of vehicles available to run over a period. However, every vehicle needs to complete its route, which limits its availability at each spot. Moreover, in each station the flows of passengers boarding and alighting influence the vehicle’s dwelling time, hence the time it takes to complete its route. In other words, flow rates and configuration in space influence local capacity. 1.2 The issue: identifying local capacity in order to plan the network
instance in which the EU has taken the leadership in an initiative to bring together all the European former Soviet states without Russia – either at all, or only on an ad hoc and discretionary invitation basis. Critics of this initiative could say that it is going in the wrong direction, compared to endeavours in which the EU would try to bring the European CIS states together in a new spirit of cooperation. But to this criticism there is a more subtle counter-argument, which might foresee a sequential game process with Russia. According to this argument Russia is a realistic and pragmatic foreign policy actor, and its behaviour will depend on the context. If Russia sees the opportunity to re-establish a hegemonic or dominant leadership role among the former Soviet states, it will readily exploit this. If on the other hand Russia observes that this is no longer possible, in part because its recent policies have antagonised most of the European former Soviet states, and that the EU is making headway in developing its own political and economic relationship with these states, then it may judge it better to join the process rather than exclude itself. The new EaP initiative, especially in its regional-multilateral aspect, may be seen in this light as a message to Russia that its recent policies has resulted in increasing unity among EU and EaP partner states, and one that creates a new framework that can function without Russia. If this initiative becomes sufficiently substantial and credible then it might be instrumental in changing Russian views of how to pursue its own interests.
Hybrid vehicle typically achieves greater fuel economy and lower emissions than conventional internal combustion Engines. By the report of environment protection agency Hybrid vehicles emissions are getting closer even lower than the recommended level. Even though hybrid cars have been on the market since the late 1990s, have never really caught on with consumers. It’s not because of consumers not enough knowledge about the hybrid vehicles benefits, but the higher price of hybrid vehicle and low service support. These are the
This mathematical model expresses the fact that one seeks to determine a set of tours while minimizing the total distance traveled (1). The constraint (2) expresses that the capacity of the vehicle must be respected. Constraints (3) and (4) express the fact that a node is visited by a single vehicle, the depot is visited by all vehicles. Constraints (5) and (6) establish that a vehicle visiting a collection node leaves it to visit another node. Constraint (7) states that all tours must be related and from the depot. The binary of the decision variables is given by the constraints (8) and (9). The constrain (10) makes it possible to verify that the nodes are collected in their respective time window for a given vehicle. The constraint (11) expresses the succession between the collection of two vertices : if is collected after by the same vehicle then the start of the collection can not be done until the collection of is complete and until the path between these two nodes has been completed. Taking into account a limit on the duration of each tour (that is to say a working time), noted T can be done by introducing the constraint (12).
This section will explore a variety of architectures mentioned in Section 2, under the NEDC driving cycle and simulating the depletion of its energy state, the study obtained the performance comparison. And therefore to simulate the differences between HEV and HHV, the basic specifications of the vehicle could be unified as shown in Table 1.
The EV is type of road vehicle that includes the electric propulsion , which can be categorised into three different types; Pure-EV, Fuel-Cell-EV and HEV. In present days, there are different development stages because of existing technologies, in that the oriented field control and variable voltage frequency are widely adapted, which is communal technique used in EV. The initial cost of battery and the management of battery create difficulty in Pure-EVs in focus of 'zero-emission'; these problems related to battery cannot be resolve in upcoming years, therefore, the temporary solution of the Pure-EVs is HEVs till the Pure-EVs becomes full commercialize. The long-term possibility of Fuel- Cell-EV is high for the futuristic vehicles , but the development technologies of its refueling and cost system is in initial development stage , therefore at present scenario HEV seems to be better choice.
depot VRPs are much closer to real world problems, because companies have often more than one depot [Wang et al. 2016]. In multi depot vehicle routing problem (MDVRP), the critical decision is assigning customers to more than one depot. In addition to assigning customers to depots, optimal routes should be found in order to delivering customer’s demands [Azadeh and Farrokhi-Asl, 2017]. Kim, Sun, and Lee (2013) have focused on single depot problem. Rabbani, M., Farrokhi-asl, H., and Rafiei, H. (2016), Cordeau and Maischberger (2012), Escobar, Linfati, Baldoquin (2014), Shimizu and Sakaguchi (2014), Subramanian, Uchoa, and Ochi (2013), and Vidal et al. (2014) are researchers that had addressed multiple depots in their studies so far. For solving this type of problem, many heuristic methods have been used by mentioned researchers. For instance, Rabbani, M., Farrokhi-asl, H., and Rafiei, H. (2016) used a hybrid genetic algorithm. Cordeau and Maischberger (2012) and Escobar, Linfati, Baldoquin (2014) implemented tabu search (TS) algorithm for solving their problem; Shimizu and Sakaguchi (2014) solved MDVRP problem with the hierarchical hybrid meta-heuristic; Subramanian, Uchoa, and Ochi (2013) used local search for solving the problem; Vidal et al. (2014) used genetic algorithm. Regarding different types of vehicle’s possession, VRP is divided into three categories including close, open and mixed close open. In close VRP all of the vehicles belong to a company and after servicing to the customers should come back to the same depot. In open VRP, the vehicles do not belong to the company (e.g. the vehicles are hired) and after servicing to the last customer in their routes they are free [Yakıcı, 2017]. Li, Golden, and Wasil (2007) used record-to-record travel algorithm for solving an OVRP problem. Repoussis et al. (2010) developed a hybrid evolution strategy for OVRP. Most complicated type in this class of problems
Because of increasing fuel efficiency restrictions over recent years, the Electric-Vehicle market is growing. Also, global vehicle makers are following this trend. But when the vehicle parts are changed, anticipating the performance is very difficult. So this research’s goal is to develop an environment that reflects the vehicle’s powertrain for design purposes. For this reason, we use MATLAB/Simulink to make a major components library, and build an electricvehicle model by using the made library. This model is verified from real car data. Also we use the 3D rendering tool Vega-prime to make a result that approximates the facts with a real driver model and various traffic conditions. So we can expect to utilize this developed simulation tool to make electricvehicle parts.
By the turn of the century, America was prosperous and the motor vehicle, now available in steam, electric, or gasoline versions, was becoming more popular. The years 1899 and 1900 were the high point of electric vehicles in America, as they outsold all other types of cars. Electric vehicles had many advantages over their competitors in the early 1900s. They did not have the vibration, smell, and noise associated with gasoline cars. Changing gears on gasoline cars was the most difficult part of driving, while electric vehicles did not require gear changes. While steam-powered cars also had no gear shifting, they suffered from long start-up times of up to 45 minutes on cold mornings. The steam cars had less range before needing water than an electric's range on a single charge. The only good roads of the period were in town, causing most travel to be local commuting, a perfect situation for electric vehicles, since their range was limited. The electricvehicle was the preferred choice of many because it did not require the manual effort to start, as with the hand crank on gasoline vehicles, and there was no wrestling with a gear shifter (Mike Chancey, 2009).
The era is, however, special in another optional area, namely the vehicle’s type of fuel. The classical, conventional gasoline and diesel-powered cars are no longer the only consumer’s options, while picking up a car on the market and the electricity is more in the game than ever before. It is certainly a nice gesture from an environmental perspective to decide for an eco-friendly driving machine, however, how expensive does it become to drive sustainably is another question. The consumers have the option of choosing fully electric zero tail-pipe emission vehicles, hybrid or even plug-in hybrid cars. Each mentioned one has then its own bright side, but there are dark sides as well.
There is a great diversity of electricvehicle promotion activity across Europe. Common to the European Union member states, vehicles are all promoted by the increasingly stringent carbon dioxide emission standards that aim to achieve a 95 g CO 2 /km new vehiclefleet in 2021, and these regulations provide further promotion for electric vehicles with “super credits” and the omission of upstream emissions (Mock, 2014). European countries have installed various levels of electricvehicle charging equipment in order to improve the value proposition, electric range, and range confidence of electricvehicle users. The EV wide Clean Power for Transport directive provides targets for each member state regarding the increased deployment of plug-in charging and hydrogen refuelling infrastructure (European Commission, 2014). Some European countries have also established bold targets, offered large fiscal incentives to consumers, installed vehicle charging networks, and implemented other support policies to promote electricvehicle deployment. Also, each of the European countries has had higher gasoline and diesel prices of about 1.50-1.80 euros per litter in 2013-2014 that inherently have provided greater fuel savings and a stronger relative motivation for alternative fuel vehicles (Mock & Yang, 2014).
Vehicle tracking system is designed to monitor driver behaviour and android application is developed for fleet management. Fig. 1 shows the solution architecture. The system consists of different modules which are interfaced to the ARM Cortex M3 (32 bit) controller. Temperature sensor, alcohol sensor, accelerometer acts as input to controller. LCD display and buzzer are outputs. The input power is step down to 5v DC from 230v AC power line by the power supply unit. The main module is the Cortex M3 processor having greater performance efficiency allowing more work to be done without increasing the frequency or power requirements and low power consumption enabling longer battery life. The system continuously monitors temperature exceed condition of vehicle container, alcohol intoxication of driver, and accident occurrence of vehicle.
The objective of this paper is to propose a superior algorithm for CapacitatedVehicle Routing Problem (CVRP). The Vehicle Routing Problem (VRP)was first confronted and described by Dantzig and Ramser (1959) in their article on truck dispatching problem. This problem further has been enhanced as CapacitatedVehicle Routing Problem wherein a set of customer demand is served with a homogeneous fleet of vehicles (single capacity vehicles) from depot or a central node. Clarke and Wright (CW) was the pioneerwho proposed a well-structured heuristic savings algorithm for solving Capacitated Vehicle Routing Problem in 1964.Since then several enhancement of the classical CW formulation of CVRP were proposed by researchers by introducing additional parametric terms which accounts for variations in ‘distribution’, ‘distance’, ‘pick up time’ etc. However, it was found that the real time problems were still more complex and are dependent on nonlinear constraints like ‘work risks’, ‘geographical restriction’, ‘balance workload among routes’, ‘solution attractiveness’ etc. This resulted in a need for more flexible methods to be able to provide a large set of alternative near optimal solution for a Capacitated Vehicle routing problem. It is in this context we propose here a more flexible option for CVRP with introduction of heterogeneous fleet or mixed fleet (variable capacity vehicles, sub-routes within a specific route etc.), which hitherto was nonexistent in a structured manner in earlier researches. Moreover, we have adopted two phase selection procedure which involves sorting the savings values randomly with probability to yield improvement in the savings algorithm proposed in the literature. The above measures have dramatically improved the quality of our solution which we found are among the best.
The drive cycles means the average speed data which varies with time. There are some drive cycles developed for some common international cities like, New York. Some drive cycles are developed specifically for particular kinds of driving patterns like, driving on high ways, or in traffic etc. and some drive cycles are virtually developed for specific uses. The use of drive cycles is that, without actually driving the real car on that drive cycle, we can simulate our developed model of vehicle on these drive cycles in order to analyze vehicle’s behavior on that driving condition. The drive cycles are modeled by using look-up tables indexed by the simulation time. The look- tables consists of speed points for every second. The drive cycles used to simulate the model are EPA- NYCC (New York City drive cycle), West Virginia University 5 peak Drive Cycle (WVU5).
In the specialized literature, many papers address the EVCSs planning along the power distribution system but few works include the transportation network. To the best of the authors knowledge, this subject started to be studied in 2010, where a two steps model was presented (Ip, Fong, (IMS), 6th, & 2010, n.d.). This model identified clusters of data points that represent the traffic concentrations on urbanized areas, and then applied optimization techniques over the clusters for meeting the supplies and demands. Subsequently, in (Luo et al., 2011) the Grey prediction model to forecast the electric vehicles ownership and the total count of the charging infrastructure was presented, considering the service radius and planning area. Similar to (Luo et al., 2011), in (Xie et al., 2011) a daily load forecasting model for EV charging station load was introduced, using Back-propagation and Radial Basis Function neural network and Grey prediction model. Taking into account the charging and trip characteristics of the EVs, in (Cui et al., 2014) a model of charging station planning for EVs was proposed along with the power distribution system, combining particle swarm optimization and weighted Voronoi diagram to find a solution. A more structured model was presented in (Hu & Song, 2012), which relates the distribution expansion planning with the siting and sizing of EVs charging stations, meeting charging demands with the lowest investment costs and best user’s convenience. Similarly, in (Moradijoz & Moghaddam, 2012) the optimal allocation of parking lots providing Vehicle to Grid (V2G) power for loss reduction was studied, and (Feng et al., 2012a,b) presented a method for charging station location based on sensitivity analysis.
On-Road Charging ElectricVehicle is a new electricity powered transportation system to overcome the limitation of the existing electricvehicle technology. It can charge its battery while stationary or driving and eliminates the need of stopping at charging stations for a long time to charge the bulky battery. The OLEV operates with an electric motor and a battery installed in the vehicle. The input supply frequency is converted to a very high frequency using inverter and these high frequency current flows through the power line which is buried under the road. A pickup device installed under the vehicle collect the magnetic field from under-ground power cables and this is then rectified, regulated and stored in a battery  .