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Intelligent battery energy management and control for vehicle-to-grid via cloud computing network

Author

Listed:
  • Khayyam, Hamid
  • Abawajy, Jemal
  • Javadi, Bahman
  • Goscinski, Andrzej
  • Stojcevski, Alex
  • Bab-Hadiashar, Alireza

Abstract

Plug-in Electric Vehicles (PEVs) provide new opportunities to reduce fuel consumption and exhaust emission. PEVs need to draw and store energy from an electrical grid to supply propulsive energy for the vehicle. As a result, it is important to know when PEVs batteries are available for charging and discharging. Furthermore, battery energy management and control is imperative for PEVs as the vehicle operation and even the safety of passengers depend on the battery system. Thus, scheduling the grid power electricity with parking lots would be needed for efficient charging and discharging of PEV batteries. This paper aims to propose a new intelligent battery energy management and control scheduling service charging that utilize Cloud computing networks. The proposed intelligent vehicle-to-grid scheduling service offers the computational scalability required to make decisions necessary to allow PEVs battery energy management systems to operate efficiently when the number of PEVs and charging devices are large. Experimental analyses of the proposed scheduling service as compared to a traditional scheduling service are conducted through simulations. The results show that the proposed intelligent battery energy management scheduling service substantially reduces the required number of interactions of PEV with parking lots and grid as well as predicting the load demand calculated in advance with regards to their limitations. Also it shows that the intelligent scheduling service charging using Cloud computing network is more efficient than the traditional scheduling service network for battery energy management and control.

Suggested Citation

  • Khayyam, Hamid & Abawajy, Jemal & Javadi, Bahman & Goscinski, Andrzej & Stojcevski, Alex & Bab-Hadiashar, Alireza, 2013. "Intelligent battery energy management and control for vehicle-to-grid via cloud computing network," Applied Energy, Elsevier, vol. 111(C), pages 971-981.
    • Handle: RePEc:eee:appene:v:111:y:2013:i:c:p:971-981
    DOI: 10.1016/j.apenergy.2013.06.021
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    Cited by:

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    3. Kong, Xiangdong & Zheng, Yuejiu & Ouyang, Minggao & Li, Xiangjun & Lu, Languang & Li, Jianqiu & Zhang, Zhendong, 2017. "Signal synchronization for massive data storage in modular battery management system with controller area network," Applied Energy, Elsevier, vol. 197(C), pages 52-62.
    4. Jones Luís Schaefer & Julio Cezar Mairesse Siluk & Patrícia Stefan de Carvalho & José Renes Pinheiro & Paulo Smith Schneider, 2020. "Management Challenges and Opportunities for Energy Cloud Development and Diffusion," Energies, MDPI, vol. 13(16), pages 1-27, August.
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    6. Moghaddas Tafreshi, Seyed Masoud & Ranjbarzadeh, Hassan & Jafari, Mehdi & Khayyam, Hamid, 2016. "A probabilistic unit commitment model for optimal operation of plug-in electric vehicles in microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 934-947.
    7. Hassan Ranjbarzadeh & Seyed Masoud Moghaddas Tafreshi & Mohd Hasan Ali & Abbas Z. Kouzani & Suiyang Khoo, 2022. "A Probabilistic Model for Minimization of Solar Energy Operation Costs as Well as CO 2 Emissions in a Multi-Carrier Microgrid (MCMG)," Energies, MDPI, vol. 15(9), pages 1-24, April.
    8. Shafie-khah, M. & Neyestani, N. & Damavandi, M.Y. & Gil, F.A.S. & Catalão, J.P.S., 2016. "Economic and technical aspects of plug-in electric vehicles in electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1168-1177.
    9. De Gennaro, Michele & Paffumi, Elena & Scholz, Harald & Martini, Giorgio, 2014. "GIS-driven analysis of e-mobility in urban areas: An evaluation of the impact on the electric energy grid," Applied Energy, Elsevier, vol. 124(C), pages 94-116.
    10. Marongiu, Andrea & Roscher, Marco & Sauer, Dirk Uwe, 2015. "Influence of the vehicle-to-grid strategy on the aging behavior of lithium battery electric vehicles," Applied Energy, Elsevier, vol. 137(C), pages 899-912.
    11. Jian, Linni & Zheng, Yanchong & Xiao, Xinping & Chan, C.C., 2015. "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, Elsevier, vol. 146(C), pages 150-161.
    12. Han, Jie & Jiang, Cailou & Liu, Rong, 2023. "Does intelligent transformation trigger technology innovation in China's NEV enterprises?," Energy, Elsevier, vol. 270(C).
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    14. Sarabi, Siyamak & Davigny, Arnaud & Courtecuisse, Vincent & Riffonneau, Yann & Robyns, Benoît, 2016. "Potential of vehicle-to-grid ancillary services considering the uncertainties in plug-in electric vehicle availability and service/localization limitations in distribution grids," Applied Energy, Elsevier, vol. 171(C), pages 523-540.
    15. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    16. Liu, Guangming & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Hua, Jianfeng, 2015. "A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications," Applied Energy, Elsevier, vol. 149(C), pages 297-314.
    17. Tanaka, T. & Ito, S. & Muramatsu, M. & Yamada, T. & Kamiko, H. & Kakimoto, N. & Inui, Y., 2015. "Accurate and versatile simulation of transient voltage profile of lithium-ion secondary battery employing internal equivalent electric circuit," Applied Energy, Elsevier, vol. 143(C), pages 200-210.
    18. Sorrentino, Marco & Rizzo, Gianfranco & Sorrentino, Luca, 2014. "A study aimed at assessing the potential impact of vehicle electrification on grid infrastructure and road-traffic green house emissions," Applied Energy, Elsevier, vol. 120(C), pages 31-40.
    19. Zheng, Yuejiu & Ouyang, Minggao & Li, Xiangjun & Lu, Languang & Li, Jianqiu & Zhou, Long & Zhang, Zhendong, 2016. "Recording frequency optimization for massive battery data storage in battery management systems," Applied Energy, Elsevier, vol. 183(C), pages 380-389.

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