Predictive control of commercial e-vehicle using a priori route information

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F18%3A00321010" target="_blank" >RIV/68407700:21220/18:00321010 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.inderscience.com/dev/search/index.php?mainAction=search&action=record&rec_id=90362&prevQuery=&ps=10&m=or" target="_blank" >http://www.inderscience.com/dev/search/index.php?mainAction=search&action=record&rec_id=90362&prevQuery=&ps=10&m=or</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1504/IJPT.2018.10011446" target="_blank" >10.1504/IJPT.2018.10011446</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predictive control of commercial e-vehicle using a priori route information

Popis výsledku v původním jazyce

The driving range of the vehicle is usually an issue due to the limited energy storage capacity of the acu-pack. Thus, the e-vehicle control towards energy consumption decrease is of extreme importance. The known information about route properties can be used to plan torque/braking profile in an optimal way. Several approaches are compared. The first is design approach based on model predictive control (MPC) in combination with prior (before the trip starts) dynamic optimisation, the other is model-predictive control using hard limits based on route shape analyses and legal limits. The classical, optimised PID control is used as reference driver. A detailed driving range estimation model of a Fiat Doblo e-vehicle is the basis, including the main e-vehicle subsystem 1D model, e-motor, battery pack, air-conditioning/heating and EVCU. The model calibration is based on real vehicle measurements.

Název v anglickém jazyce

Predictive control of commercial e-vehicle using a priori route information

Popis výsledku anglicky

The driving range of the vehicle is usually an issue due to the limited energy storage capacity of the acu-pack. Thus, the e-vehicle control towards energy consumption decrease is of extreme importance. The known information about route properties can be used to plan torque/braking profile in an optimal way. Several approaches are compared. The first is design approach based on model predictive control (MPC) in combination with prior (before the trip starts) dynamic optimisation, the other is model-predictive control using hard limits based on route shape analyses and legal limits. The classical, optimised PID control is used as reference driver. A detailed driving range estimation model of a Fiat Doblo e-vehicle is the basis, including the main e-vehicle subsystem 1D model, e-motor, battery pack, air-conditioning/heating and EVCU. The model calibration is based on real vehicle measurements.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

<a href="/cs/project/TE01020020" target="_blank" >TE01020020: Centrum kompetence automobilového průmyslu Josefa Božka</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

International Journal of Powertrains

ISSN

1742-4275

e-ISSN

—

Svazek periodika

2018

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

53-71

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85043768016