Pregled bibliografske jedinice broj: 1202044
Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology
Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology // Energies, 15 (2022), 11; 4108, 21 doi:10.3390/en15114108 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1202044 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology
Autori
Dabčević, Zvonimir ; Škugor, Branimir ; Topić, Jakov ; Deur, Joško
Izvornik
Energies (1996-1073) 15
(2022), 11;
4108, 21
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
driving cycle ; synthesis ; boundary conditions ; city bus ; vehicle-tracking data ; Markov chain method ; validation
Sažetak
The authors of this paper propose a Markov-chain-based method for the synthesis of naturalistic, high- sampling-rate driving cycles based on the route segment statistics extracted from low-sampling-rate vehicle-tracking data. In the considered case of a city bus transport system, the route segments correspond to sections between two consecutive bus stations. The route segment statistics include segment lengths and maps of average velocity, station stop time, and station-stopping probability, all given along the day on an hourly basis. In the process of driving cycle synthesis, the transition probability matrix is built up based on the high-sampling-rate driving cycles purposely recorded in a separate reference city. The particular emphasis of the synthesis process is on satisfying the route segment velocity and acceleration boundary conditions, which may be equal to or greater than zero depending on whether a bus stops or passes a station. This enables concatenating the synthesized consecutive micro-cycles into the full-trip driving cycle. The synthesis method was validated through an extensive statistical analysis of generated driving cycles, including computational efficiency aspects.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Strojarstvo, Tehnologija prometa i transport
POVEZANOST RADA
Projekti:
IP-2018-01-8323 - Adaptivno i prediktivno upravljanje utičnim hibridnim električnim vozilima (ACHIEVE) (Deur, Joško, HRZZ - 2018-01) ( CroRIS)
Citiraj ovu publikaciju:
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