Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 823331

A bi-level optimisation framework for electric vehicle fleet charging management


Škugor, Branimir; Deur, Joško
A bi-level optimisation framework for electric vehicle fleet charging management // Applied energy, 184 (2016), 1332-1342 doi:10.1016/j.apenergy.2016.03.091 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 823331 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
A bi-level optimisation framework for electric vehicle fleet charging management

Autori
Škugor, Branimir ; Deur, Joško

Izvornik
Applied energy (0306-2619) 184 (2016); 1332-1342

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Electric vehicle fleet ; Aggregate battery ; Modelling ; Charging optimisation ; Genetic algorithm ; Dynamic programming

Sažetak
The paper proposes a bi-level optimisation framework for Electric Vehicle (EV) fleet charging based on a realistic EV fleet model including a transport demand sub-model. The EV fleet is described by an aggregate battery model, which is parameterised by using recorded driving cycle data of a delivery vehicle fleet. The EV fleet model is used within the inner level of the bi-level optimisation framework, where the aggregate charging power is optimised by using the dynamic programming (DP) algorithm. At the superimposed optimisation level, the final State- of-Charge (SoC) values of individual EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of the bi-level optimisation algorithm, it is generally needed to recalculate the transport demand sub-model for the new set of final SoC values. In order to simplify this process, the transport demand is modelled by using a computationally efficient response surface method, which is based on naturalistic synthetic driving cycles and agent- based simulations of the EV model. When compared to the single-level charging optimisation approach, which assumes the final SoC values to be equal to 1 (full batteries on departure), the bi-level optimisation provides a degree of optimisation freedom more for more accurate techno-economic analyses of the integrated transport-energy system. The two approaches are compared through a simulation study of the particular delivery vehicle fleet transport- energy system.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo



POVEZANOST RADA


Ustanove:
Fakultet strojarstva i brodogradnje, Zagreb

Profili:

Avatar Url Joško Deur (autor)

Avatar Url Branimir Škugor (autor)

Poveznice na cjeloviti tekst rada:

doi www.sciencedirect.com dx.doi.org

Citiraj ovu publikaciju:

Škugor, Branimir; Deur, Joško
A bi-level optimisation framework for electric vehicle fleet charging management // Applied energy, 184 (2016), 1332-1342 doi:10.1016/j.apenergy.2016.03.091 (međunarodna recenzija, članak, znanstveni)
Škugor, B. & Deur, J. (2016) A bi-level optimisation framework for electric vehicle fleet charging management. Applied energy, 184, 1332-1342 doi:10.1016/j.apenergy.2016.03.091.
@article{article, author = {\v{S}kugor, Branimir and Deur, Jo\v{s}ko}, year = {2016}, pages = {1332-1342}, DOI = {10.1016/j.apenergy.2016.03.091}, keywords = {Electric vehicle fleet, Aggregate battery, Modelling, Charging optimisation, Genetic algorithm, Dynamic programming}, journal = {Applied energy}, doi = {10.1016/j.apenergy.2016.03.091}, volume = {184}, issn = {0306-2619}, title = {A bi-level optimisation framework for electric vehicle fleet charging management}, keyword = {Electric vehicle fleet, Aggregate battery, Modelling, Charging optimisation, Genetic algorithm, Dynamic programming} }
@article{article, author = {\v{S}kugor, Branimir and Deur, Jo\v{s}ko}, year = {2016}, pages = {1332-1342}, DOI = {10.1016/j.apenergy.2016.03.091}, keywords = {Electric vehicle fleet, Aggregate battery, Modelling, Charging optimisation, Genetic algorithm, Dynamic programming}, journal = {Applied energy}, doi = {10.1016/j.apenergy.2016.03.091}, volume = {184}, issn = {0306-2619}, title = {A bi-level optimisation framework for electric vehicle fleet charging management}, keyword = {Electric vehicle fleet, Aggregate battery, Modelling, Charging optimisation, Genetic algorithm, Dynamic programming} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font