Optimization of Hybrid Vehicle Driving

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F17%3A00320917" target="_blank" >RIV/68407700:21220/17:00320917 - isvavai.cz</a>

Výsledek na webu

<a href="http://fs12120.fsid.cvut.cz/softlib/2017/HybridOptimization.zip" target="_blank" >http://fs12120.fsid.cvut.cz/softlib/2017/HybridOptimization.zip</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Optimization of Hybrid Vehicle Driving

Popis výsledku v původním jazyce

The predictive optimization of minimum energy consumption and low carbon dioxide emissions at known route is becoming the standard tool for the extended horizon driving control of hybrid vehicles. The problem occurs if the capacity of accumulators is sufficient for distance much longer than the extended horizon is. The driving strategy should assess the possibilities of re-charging an accumulator for the optimum combination of prime mover power (today, an internal combustion engine) and an electric motor power booster, taking the rest of trip into account. Adaption for changed traffic density or weather is the next step of future control systems, finding the best compromise between trip time and total energy consumption. The “brute force” optimization of many thousands independent variables for the model with detailed description of vehicle components is possible, if performed out of real time only, using dynamic programming techniques and cloud computing devices. The paper describes fast model and optimization algorithm for the best carbon dioxide emissions, which finds the local optimum distribution of power between an engine and a motor/generator, applying the numerical solution for finding the optimum of energy consumption, described by a regression powertrain model. It takes the efficiency of stored electric energy use in a motor during the relevant rest of trip into account by iterative way. The model limits number of optimization variables by semi-heuristic algorithm of speed schedule in a route section. Then, the eco-driving optimization can be done by genetic algorithm. Currently, the model describes single-accumulator parallel or serial hybrids including differential power splitter, but it can be extended to all hybrid layouts.

Název v anglickém jazyce

Optimization of Hybrid Vehicle Driving

Popis výsledku anglicky

The predictive optimization of minimum energy consumption and low carbon dioxide emissions at known route is becoming the standard tool for the extended horizon driving control of hybrid vehicles. The problem occurs if the capacity of accumulators is sufficient for distance much longer than the extended horizon is. The driving strategy should assess the possibilities of re-charging an accumulator for the optimum combination of prime mover power (today, an internal combustion engine) and an electric motor power booster, taking the rest of trip into account. Adaption for changed traffic density or weather is the next step of future control systems, finding the best compromise between trip time and total energy consumption. The “brute force” optimization of many thousands independent variables for the model with detailed description of vehicle components is possible, if performed out of real time only, using dynamic programming techniques and cloud computing devices. The paper describes fast model and optimization algorithm for the best carbon dioxide emissions, which finds the local optimum distribution of power between an engine and a motor/generator, applying the numerical solution for finding the optimum of energy consumption, described by a regression powertrain model. It takes the efficiency of stored electric energy use in a motor during the relevant rest of trip into account by iterative way. The model limits number of optimization variables by semi-heuristic algorithm of speed schedule in a route section. Then, the eco-driving optimization can be done by genetic algorithm. Currently, the model describes single-accumulator parallel or serial hybrids including differential power splitter, but it can be extended to all hybrid layouts.

Druh

R - Software

CEP obor

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OECD FORD obor

20302 - Applied mechanics

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)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Interní identifikační kód produktu

HybridOptimization

Technické parametry

Smlouva o využití výsledků uzavřena s Honeywell spol. s r. o., Ricardo Prague s.r.o., TÜV SÜD CZECH s.r.o., TU v Liberci, VŠB TU Ostrava, VUT v Brně v rámci využívání progrmaové náplně DASY a OntoDASY. MS EXCEL, 150 MB. Rozsáhlý program pro optimalizaci průjedu zadanou trasou. Velikost podle rozsahu trasy.

Ekonomické parametry

Náklady na vývoj programu 2,2 MKč. Licence zdarma, pokud provozován na serveru ČVUT. Program poskytnut zahraničním partnerům v projektech H2020.

IČO vlastníka výsledku

68407700

Název vlastníka

FS - rezerva