Performance Study of a Developed Rule-Based Control Strategy with Use of an ECMS Optimization Control Algorithm on a Plug-In Hybrid Electric Vehicle

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU146315" target="_blank" >RIV/00216305:26210/22:PU146315 - isvavai.cz</a>

Výsledek na webu

<a href="https://sciendo.com/article/10.2478/scjme-2022-0041#" target="_blank" >https://sciendo.com/article/10.2478/scjme-2022-0041#</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/scjme-2022-0041" target="_blank" >10.2478/scjme-2022-0041</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Performance Study of a Developed Rule-Based Control Strategy with Use of an ECMS Optimization Control Algorithm on a Plug-In Hybrid Electric Vehicle

Popis výsledku v původním jazyce

Greenhouse gases jeopardize world’s climate. A significant amount of these pollutants is produced by road vehicles, so their producers are forced to reduce their emissions significantly. This means that every car manufacturer is expanding their electrified vehicle range. Fully electric vehicles are the best way for long-term elimination of greenhouse gases production in road transport. However, in the short term it is not possible to switch all vehicles to EVs. Temporary solutions are hybrid electric vehicles, which offer a compromise between conventional and electric vehicles. In addition to the right choice of hybrid powertrain and correct scaling of its components, it is also important to develop a suitable control strategy for its energy management. The main goal of this work is to compare the performance of the rule-based control strategy with the built-in local optimization algorithm ECMS in GT-SUITETM software. ECMS means Equivalent Consumption Minimization Strategy and is based on an optimization of selected control parameters in each time step of the driving cycle simulation. A fuel efficiency improvement is assessed on a selected plug-in hybrid vehicle. Results of WLTC driving cycle simulations in charge sustaining mode (state of charge of the battery at the beginning and at the end of the simulation is the same) shows fuel consumption of 5 l/100km for rule based control strategy and 4.2 l/100km for ECMS algorithm. This means that ECMS can achieve more than 16% improvement for this particular vehicle.

Název v anglickém jazyce

Performance Study of a Developed Rule-Based Control Strategy with Use of an ECMS Optimization Control Algorithm on a Plug-In Hybrid Electric Vehicle

Popis výsledku anglicky

Greenhouse gases jeopardize world’s climate. A significant amount of these pollutants is produced by road vehicles, so their producers are forced to reduce their emissions significantly. This means that every car manufacturer is expanding their electrified vehicle range. Fully electric vehicles are the best way for long-term elimination of greenhouse gases production in road transport. However, in the short term it is not possible to switch all vehicles to EVs. Temporary solutions are hybrid electric vehicles, which offer a compromise between conventional and electric vehicles. In addition to the right choice of hybrid powertrain and correct scaling of its components, it is also important to develop a suitable control strategy for its energy management. The main goal of this work is to compare the performance of the rule-based control strategy with the built-in local optimization algorithm ECMS in GT-SUITETM software. ECMS means Equivalent Consumption Minimization Strategy and is based on an optimization of selected control parameters in each time step of the driving cycle simulation. A fuel efficiency improvement is assessed on a selected plug-in hybrid vehicle. Results of WLTC driving cycle simulations in charge sustaining mode (state of charge of the battery at the beginning and at the end of the simulation is the same) shows fuel consumption of 5 l/100km for rule based control strategy and 4.2 l/100km for ECMS algorithm. This means that ECMS can achieve more than 16% improvement for this particular vehicle.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Journal of Mechanical Engineering - Strojnícky časopis

ISSN

2450-5471

e-ISSN

—

Svazek periodika

72

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

10

Strana od-do

61-70

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85143048190