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Dynamic Optimization of the E-Vehicle Route Profile

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F16%3A00308068" target="_blank" >RIV/68407700:21220/16:00308068 - isvavai.cz</a>

  • Výsledek na webu

    <a href="http://papers.sae.org" target="_blank" >http://papers.sae.org</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.4271/2016-01-0156" target="_blank" >10.4271/2016-01-0156</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Dynamic Optimization of the E-Vehicle Route Profile

  • Popis výsledku v původním jazyce

    Current vehicles, especially the electric ones, are complex mechatronic devices. The pickup vehicles of small sizes are currently used in transport considerably. They often operate within a repeating scheme of a limited variety of tracks and larger fleets. Thanks to mechatronic design of vehicles and their components and availability of high capacity data connection with computational centers (clouds), there are many means to optimize their performance, both by planning prior the trip and recalculations during the route. Although many aspects of this opportunity were already addressed, the paper shows an approach developed to further increase the range of e-vehicle operation. It is based on prior information about the route profile, traffic density, road conditions, past behaviour, mathematical models of the route, vehicle and dynamic optimization. The most important part of the procedure is performed in the cloud, using both computational power and rich information resources. Suitable route discretization into sections is most important part of the algorithm. The various information resources are used. Accumulated experience coming from fleet operation is very important as well. Methods for automation of this procedure are presented. Subsequently, feasible initial values of section parameters are found using heuristic rules devised from good driver’s practice and backward calculation based on dynamic programming principals. Designed velocity profile is further optimized based on simplified, but very fast energy consumption models, verified and fine-tuned on detailed simulation model of the vehicle. The velocity profile is updated when requested and finally loaded into on-board control unit. Model based predictive controller is used to keep the vehicle with its driver efficiently on defined track. The proposed strategy is verified in simulation environment and prepared to be implemented on test vehicle and cloud system.

  • Název v anglickém jazyce

    Dynamic Optimization of the E-Vehicle Route Profile

  • Popis výsledku anglicky

    Current vehicles, especially the electric ones, are complex mechatronic devices. The pickup vehicles of small sizes are currently used in transport considerably. They often operate within a repeating scheme of a limited variety of tracks and larger fleets. Thanks to mechatronic design of vehicles and their components and availability of high capacity data connection with computational centers (clouds), there are many means to optimize their performance, both by planning prior the trip and recalculations during the route. Although many aspects of this opportunity were already addressed, the paper shows an approach developed to further increase the range of e-vehicle operation. It is based on prior information about the route profile, traffic density, road conditions, past behaviour, mathematical models of the route, vehicle and dynamic optimization. The most important part of the procedure is performed in the cloud, using both computational power and rich information resources. Suitable route discretization into sections is most important part of the algorithm. The various information resources are used. Accumulated experience coming from fleet operation is very important as well. Methods for automation of this procedure are presented. Subsequently, feasible initial values of section parameters are found using heuristic rules devised from good driver’s practice and backward calculation based on dynamic programming principals. Designed velocity profile is further optimized based on simplified, but very fast energy consumption models, verified and fine-tuned on detailed simulation model of the vehicle. The velocity profile is updated when requested and finally loaded into on-board control unit. Model based predictive controller is used to keep the vehicle with its driver efficiently on defined track. The proposed strategy is verified in simulation environment and prepared to be implemented on test vehicle and cloud system.

Klasifikace

  • Druh

    J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

  • CEP obor

    JO - Pozemní dopravní systémy a zařízení

  • OECD FORD obor

Návaznosti výsledku

  • Projekt

    Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

  • Návaznosti

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Ostatní

  • Rok uplatnění

    2016

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název periodika

    Society of Automotive Engineers Technical Paper Series

  • ISSN

    0148-7191

  • e-ISSN

  • Svazek periodika

    2016

  • Číslo periodika v rámci svazku

    01

  • Stát vydavatele periodika

    US - Spojené státy americké

  • Počet stran výsledku

    10

  • Strana od-do

  • Kód UT WoS článku

  • EID výsledku v databázi Scopus