新型太阳能汽车路线优化外文翻译中英文.docx

1、新型太阳能汽车路线优化外文翻译中英文新型太阳能汽车路线优化外文翻译中英文英文Criteria for Solar Car Optimized Route EstimationMehrija Hasicic，Damir Bilic，Harun SiljakAbstractThis paper gives a thorough overview of Solar Car Optimized Route Estimation (SCORE), novel route optimization scheme for solar vehicles based on solar irradiance an

2、d target distance. In order to conduct the optimization, both data collection and the optimization algorithm itself have to be performed using appropriate hardware. Here we give an insight to both stages, hardware and software used and present some results of the SCORE system together with certain i

3、mprovements of its fusion and optimization criteria. Results and the limited applicability of SCORE are discussed together with an overview of future research plans and comparison with state-of-the-art solar vehicle optimization solutions.Keywords：Vehicle routing，Electric vehicle，Solar vehicle，Navig

4、ation，Route optimizationIntroductionDevelopment of navigation systems has been an important topic in optimization once portable electronic devices were feasible and route selection and optimization have been the vital part of it, aiming at fuel consumption reduction and driver satisfaction, which is

5、 in general a multi-objective optimization problem. With the advent of electric and autonomous cars, attention in development of optimization algorithms turned to them, utilizing properties of these new vehicles. Navigation for autonomous vehicles also allows use of algorithms previously developed f

6、or mobile robotics.Geographic Information System (GIS) integrates various data types, many of which are instrumental in navigation, leading to extensive use of GIS in route optimization. From our perspective, it is also important to note utilization of GIS (solar radiation maps) in solar energy mana

7、gementas it allows us to use GIS for our optimization as well as an input provider.In the spirit of optimization with respect to fuel consumption, power management optimization in electric and solar cars has been investigated in theory and practice, and for solar hybrid cars the power management sch

8、emes focus on the question of switching energy sources and optimization of resources. Sunshine forecast as an optimization input has been recently introducedand used mainly for parking planning.This paper presents Solar Car Optimized Route Estimation (SCORE), a novel route optimization system based

9、on proposing sunniest routes and sunniest parking spots, therefore utilizing the options of charging while driving and charging on parking lots. The data on solar irradiance for routes and parking spots is a fusion of previously collected, real time and forecasted data.Describing the whole process f

10、rom data collection to route selection, this paper provides both theoretical and practical treatise of SCORE, giving a general structure and the real world implementation of it. The paper itself is an extension of the work under same title presented in MECO 2016 conference, presenting the implementa

11、tion challenges and solutions. In addition to previous work, this paper also extends the analysis of cost functions used in SCORE for selection of routes and parking places, as well the mathematical model of data fusion, together with more details on the results.State of the artRoute planning and se

12、lection for road vehicles has been a subject of interest for decades, with the first commercial digital map navigation system appearing more than 30 years ago. Since then, various route planning systems have been proposed, based on different algorithms and inputs.Dijkstras algorithm has been the sim

13、plest algorithm for implementation and serves as a benchmark for storage and time consumption in route planning for carswhen compared with other algorithms, from bidirectional and A* search to special goal-driven, hierarchical and bounded-hop algorithms. Although Dijkstras algorithm is the slowest o

14、ption in big networks, it can still be used as a proof of concept.While in the beginning the route planning and selection systems had a single objective: namely, fuel consumption or journey time minimization, soon after their inception multi-objective optimization models were developed, often using

15、artificial intelligence techniques to combine different goals These goals often include personalized choices of driversand their personal attitude towards possible routes.Parking selection has been studied extensively as well. It has been modeled as a multi-input problem measuring the utility of a p

16、arking space by accounting for availability, driving duration, walking distance to the destination, parking cost, traffic congestion, etc.In terms of solar car optimization, the power management techniques mentioned in the introduction have been extended to minimize total energy consumption by plann

17、ing speed on parts of the path differently exposed to the sun. This work, published at the same time as the first SCORE resultsbuilds up on closely related work on solar powered robots.Inauthors propose a solar race car optimization based on weather forecast and velocity profile, determining the nee

18、d for acceleration and deceleration throughout the race course in order to maximize the average velocity. Similar task is done inas well, but the latter also includes somewhat more complex weather model and solar position algorithm. The weather model is a random walk around expected irradiance curve

19、, while solar position is determined through the NREL (National Renewable Energy Laboratory) model.In comparison to these solutions, SCORE has somewhat different purpose. Aiming at offering a framework for optimized city and intercity travel, SCOREs model relies on data which can be collected in a m

20、ore regular fashion than the data for a race track. Moreover, SCOREs output is the route, unlike the outputs of power management optimization systems having the velocity profile or engine-generator power trajectory (in case of hybrid cars) as the output. This does mean that SCORE should be extended

21、so it covers the velocity control and/or hybrid vehicle power switching, but at this point the focus is on route selection. This is along the lines of the idea in, where the path selection is followed by speed profile selection.SCORE system descriptionSCORE system consists of three separable parts,

22、indicated in namely:1. Mobile sensor data transmitter, transmitting solar irradiance data through wireless channel in real time from the roads. Although we will use the term mobile sensors throughout this paper, they can be stationary as well, placed at selected places by the road. When mobile, thes

23、e transmitters are not necessarily placed on solar cars using SCORE as their navigation system. They can be placed on fossil fuel and electric cars as well. Preferably, cars carrying the sensors would be often in motion, covering a large area (e.g. taxis, public transportation).2. Server for data fu

24、sion, collecting readings transmitters send from the field and third party sources, processing them and combining with offline data (which can include weather forecast and historic readings, as the next section will show) and allowing the car computer clients to fetch the processed data in appropria

25、te matrix format.3. Embedded car computer client in the solar car, taking the processed data from servers cloud service and customize it on its own by using readings from its own sensors. Built-in light sensor can be used for normalization of data, and electric measurements from the car can be utili

26、zed for state estimation. Finally, the user can enter the destination and obtain the proposed route, which should dynamically change based on weather updates.Data collectionTheoretical considerations of irradiation data analysisIn order to select routes with highest solar energy gain, SCORE system h

27、as to have relevant solar irradiation data. Since it is not possible to always have up to date data in real time for every road segment considered by the algorithm, it is important to use different sources of information. In this work, we have divided the irradiation data into two categories:1.Onlin

28、e data, gathered by the mobile sensor data transmitters and updated in regular fashion. The data for each location is represented by real numbers between 0 (no irradiance) and 1 (maximum irradiance) with a timestamp for data sample collection. In this paper, timestamps are integers denoting hours st

29、arting from a reference time (beginning of the year).2.Offline data, generated using numerical sunshine forecast, CAD (computer aided design) and GIS models for prediction of solar irradiance for a particular location. CAD data is generated from CAD street models and simulating sun movement, while G

30、IS data is taken from the GIS services doing solar irradiance measurements for areas of interest. This data is provided in an aggregated form by Google as well through their Project Sunroof for housing solar panel planning.These two numerical values, denotedron(normalized value of online data irradi

31、ance) androff(normalized value of irradiance inferred from offline data) are combined for each geographical location (in the optimization part, we will refer to these as graph nodes) on the server. Details of this fusion will be discussed in a separate section.Implementation of sensor data collectio

32、n and the serverMobile device developed within this project is compact and autonomous, which enables its placement on a vehicle moving through the city to collect irradiation data without customization of the car itself or its routes. Once again it is emphasized that the vehicles carrying these mobi

33、le devices do not have to be vehicles using SCORE for navigation, i.e. traditional fossil fuel cars may serve the purpose of data collecting “crawlers”.While any wireless protocol could be used for transmission from these mobile devices, we propose the use of packet radio. Its easiest implementation is APRS (Auto