Multi-Criterial Assessment of Electric Vehicle Integration into the Commercial Sector-A Case Study

被引:3
|
作者
Pietracho, Robert [1 ]
Wenge, Christoph [2 ]
Komarnicki, Przemyslaw [1 ]
Kasprzyk, Leszek [3 ]
机构
[1] Univ Appl Sci Magdeburg, Inst Elect Engn, D-39114 Magdeburg, Germany
[2] Fraunhofer Inst Factory Operat & Automat, D-39106 Magdeburg, Germany
[3] Poznan Univ Tech, Inst Elect Engn & Elect, PL-60965 Poznan, Poland
关键词
AHP algorithm; electric vehicles; electric vehicle fleet; electric vehicle logistic utilization; well-to-wheels analyses; TCO analyses; electric commercial light vehicles; E-LCV; FREQUENCY CONTROL; RENEWABLE ENERGY; EMISSIONS; TECHNOLOGIES; CONSUMPTION; AGGREGATOR; MANAGEMENT; IMPACTS; SYSTEMS; PROFILE;
D O I
10.3390/en16010462
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Transforming the transport sector to zero emission is an integral part of changes to the energy sector worldwide. This effects not only the electrification of the private sector but also the commercial sector. The aim of this study is to develop methodologies, algorithms and associated requirements for the integration of electric vehicles into a logistics application with a possible reduction in operating costs. The most favorable solution for a company was evaluated using the analytic hierarchy process algorithm considering three main aspects: economic, environmental and technical. An analysis of the environmental impact of the vehicle fleet in terms of atmospheric emissions was also conducted, based on the data available for combustion and electric vehicles, considering the well-to-tank approach. The costs associated with operating an electric vehicle were identified and compared to the current costs associated with operating a standard diesel-based fleet. Incorporating the identified costs of electrifying the vehicle fleet, an algorithm was implemented to reduce the number of vehicles in the company and, thereby, significantly reducing the costs associated with fleet maintenance.
引用
收藏
页数:29
相关论文
共 50 条
  • [1] Multi-Criterial Carbon Assessment of the City
    Sobierajewicz, Piotr
    Adamczyk, Janusz
    Dylewski, Robert
    ENERGIES, 2024, 17 (18)
  • [2] Multi-criterial vulnerability assessment for Timisoara city, Romania
    Onescu, I
    Onescu, E.
    Mosoarca, M.
    STRUCTURES AND ARCHITECTURE: BRIDGING THE GAP AND CROSSING BORDERS, 2019, 1 : 923 - 930
  • [3] Virtual Multi-Criterial Calibration of Operating Strategies for Hybrid-Electric Powertrains
    Duezguen, Marc Timur
    Dorscheidt, Frank
    Krysmon, Sascha
    Bailly, Peter
    Lee, Sung-Yong
    Doenitz, Christian
    Pischinger, Stefan
    VEHICLES, 2023, 5 (04): : 1367 - 1383
  • [4] Integrated multi-criterial decision model: A case study for the allocation of facilities in Chinese agriculture
    Guo, LS
    He, YS
    JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH, 1999, 73 (01): : 87 - 94
  • [5] A Multi-Criterial Hardware Assessment of the Psychophysical Capacity of Workers in the Investigation of Fatigue
    Dahlke, Grzegorz
    Drzewiecka, Milena
    Butlewski, Marcin
    Pacholski, Leszek
    ADVANCES IN SOCIAL & OCCUPATIONAL ERGONOMICS, 2017, 487 : 25 - 34
  • [6] Multi-Criterial Assessment of the Uniformity of the Electrical Potential of Micro-Films
    Teodorescu, Horia-Nicolai
    Cojocaru, Victor
    Katashev, Alexei
    2019 24TH INTERNATIONAL CONFERENCE ON APPLIED ELECTRONICS (AE), 2019, : 173 - 176
  • [7] STRATEGIC ENVIRONMENTAL ASSESSMENT FOR PLANS, PROGRAMS, POLICIES IN ROMANIA: MULTI-CRITERIAL METHOD
    Robu, Brindusa
    Macoveanu, Matei
    ENVIRONMENTAL ENGINEERING AND MANAGEMENT JOURNAL, 2009, 8 (06): : 1451 - 1456
  • [8] Impact Assessment of Electric Vehicle Integration: A case study of Kathmandu Valley
    Shrestha, Ravi Raj
    Paudyal, Binay
    Basnet, Prasant
    Niraula, Dayasagar
    Mali, Bijen
    Shakya, Hitendra Dev
    2022 IEEE KANSAS POWER AND ENERGY CONFERENCE (KPEC 2022), 2022,
  • [9] Towards Fully Automated Multi-Criterial Plan Generation: A Prospective Clinical Study
    Voet, P.
    Dirkx, M.
    Breedveld, S.
    Fransen, D.
    Levendag, P.
    Heijmen, B.
    MEDICAL PHYSICS, 2012, 39 (06) : 3848 - 3848
  • [10] Predicting Worst-Case Execution Times During Multi-criterial Function Inlining
    Muts, Kateryna
    Falk, Heiko
    MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE (LOD 2021), PT I, 2022, 13163 : 281 - 295