IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v189y2019ics0360544219319693.html
   My bibliography  Save this article

Recovering waste vibration energy of an automobile using shock absorbers included magnet moving-coil mechanism and adding to overall efficiency using wind turbine

Author

Listed:
  • Fathabadi, Hassan

Abstract

The vibration energy of an automobile caused by driving on bumpy roads and the automobile acceleration as well as its deceleration is dissipated by the shock absorbers of the automobile, so the vibration energy is wasted in the suspension system of the automobile. This paper deals with increasing the energy efficiency of an automobile by applying two modifications to the automobile. First, to recover the mentioned waste vibration energy, magnet moving-coil mechanism is added to the four shock absorbers of the automobile by embedding four proposed electric coils inside the four shock absorbers. The total electric voltage of the four electric coils is converted into a constant dc voltage technically named “dc-link voltage” using a converter. Second, to recover a part of the kinetic energy of wind hitting the automobile when the automobile is moving or stopping, a wind turbine is attached to the back of the automobile’s condenser. Correspondingly, the output voltage of the wind turbine is converted into the dc-link voltage using another converter. By constructing a novel system including the two mentioned converters, the power productions of the four electric coils and wind turbine are converted into appropriate levels of voltage and current to be used to charge the battery of the automobile as well as to drive the other parts of the automobile. The two modifications can be applied to both internal combustion engine vehicles and electric vehicles (EVs). Experimental measurements obtained from applying the two mentioned modifications and the constructed system to an EV with the weight of 1855 kg are presented that demonstrate the four electric coils and wind turbine have added respectively 0.36 kWh and 2.64 kWh to the energy production in the EV during two days, and as a result, the traveling range of the EV has increased by 7.27 km. This point proves the advantage of utilizing the proposed electric coils and wind turbine as well as the novelty and contribution of this study in recapturing the waste vibration energy of an automobile and the energy of wind hitting the automobile.

Suggested Citation

  • Fathabadi, Hassan, 2019. "Recovering waste vibration energy of an automobile using shock absorbers included magnet moving-coil mechanism and adding to overall efficiency using wind turbine," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319693
    DOI: 10.1016/j.energy.2019.116274
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219319693
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.116274?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yip, Chak Man Andrew & Gunturu, Udaya Bhaskar & Stenchikov, Georgiy L., 2016. "Wind resource characterization in the Arabian Peninsula," Applied Energy, Elsevier, vol. 164(C), pages 826-836.
    2. Petković, Dalibor & Ćojbašič, Žarko & Nikolić, Vlastimir, 2013. "Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 191-195.
    3. Dalibor Petković & Siti Ab Hamid & Žarko Ćojbašić & Nenad Pavlović, 2014. "Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 463-475, November.
    4. Fathabadi, Hassan, 2016. "Novel high-efficient unified maximum power point tracking controller for hybrid fuel cell/wind systems," Applied Energy, Elsevier, vol. 183(C), pages 1498-1510.
    5. Fathabadi, Hassan, 2018. "Novel fuel cell/battery/supercapacitor hybrid power source for fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 143(C), pages 467-477.
    6. Fathabadi, Hassan, 2020. "Novel solar-powered photovoltaic/thermoelectric hybrid power source," Renewable Energy, Elsevier, vol. 146(C), pages 426-434.
    7. Fathabadi, Hassan, 2017. "Novel standalone hybrid solar/wind/fuel cell/battery power generation system," Energy, Elsevier, vol. 140(P1), pages 454-465.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yadegaridehkordi, Elaheh & Hourmand, Mehdi & Nilashi, Mehrbakhsh & Shuib, Liyana & Ahani, Ali & Ibrahim, Othman, 2018. "Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 199-210.
    2. Kim, Dongwoo & Song, Kang Sub & Lim, Junyub & Kim, Yongchan, 2018. "Analysis of two-phase injection heat pump using artificial neural network considering APF and LCCP under various weather conditions," Energy, Elsevier, vol. 155(C), pages 117-127.
    3. Neshat, Mehdi & Nezhad, Meysam Majidi & Abbasnejad, Ehsan & Mirjalili, Seyedali & Groppi, Daniele & Heydari, Azim & Tjernberg, Lina Bertling & Astiaso Garcia, Davide & Alexander, Bradley & Shi, Qinfen, 2021. "Wind turbine power output prediction using a new hybrid neuro-evolutionary method," Energy, Elsevier, vol. 229(C).
    4. Ahuja, Anjali & Jain, Anamika & Jain, Madhu, 2022. "Transient analysis and ANFIS computing of unreliable single server queueing model with multiple stage service and functioning vacation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 464-490.
    5. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Wang, Chenfang & Li, Qingshan & Wang, Chunmei & Zhang, Yangjun & Zhuge, Weilin, 2021. "Thermodynamic analysis of a hydrogen fuel cell waste heat recovery system based on a zeotropic organic Rankine cycle," Energy, Elsevier, vol. 232(C).
    7. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Characterization of wind resource in China from a new perspective," Energy, Elsevier, vol. 167(C), pages 994-1010.
    8. Hou, Hui & Xu, Tao & Wu, Xixiu & Wang, Huan & Tang, Aihong & Chen, Yangyang, 2020. "Optimal capacity configuration of the wind-photovoltaic-storage hybrid power system based on gravity energy storage system," Applied Energy, Elsevier, vol. 271(C).
    9. Giani, Paolo & Tagle, Felipe & Genton, Marc G. & Castruccio, Stefano & Crippa, Paola, 2020. "Closing the gap between wind energy targets and implementation for emerging countries," Applied Energy, Elsevier, vol. 269(C).
    10. M’hammed Sahnoun & David Baudry & Navonil Mustafee & Anne Louis & Philip Andi Smart & Phil Godsiff & Belahcene Mazari, 2019. "Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2981-2997, December.
    11. Li, Yanju & Li, Dongxu & Ma, Zheshu & Zheng, Meng & Lu, Zhanghao & Song, Hanlin & Guo, Xinjia & Shao, Wei, 2022. "Performance analysis and optimization of a novel vehicular power system based on HT-PEMFC integrated methanol steam reforming and ORC," Energy, Elsevier, vol. 257(C).
    12. Chen, Xi & Yang, Chen & Sun, Yun & Liu, Qinxiao & Wan, Zhongmin & Kong, Xiangzhong & Tu, Zhengkai & Wang, Xiaodong, 2022. "Water management and structure optimization study of nickel metal foam as flow distributors in proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 309(C).
    13. Arshdeep Singh & Shimi Sudha Letha, 2019. "Emerging energy sources for electric vehicle charging station," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(5), pages 2043-2082, October.
    14. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    15. Christina Ortega & Amin Younes & Mark Severy & Charles Chamberlin & Arne Jacobson, 2020. "Resource and Load Compatibility Assessment of Wind Energy Offshore of Humboldt County, California," Energies, MDPI, vol. 13(21), pages 1-27, October.
    16. Tri Cuong Do & Hoai Vu Anh Truong & Hoang Vu Dao & Cong Minh Ho & Xuan Dinh To & Tri Dung Dang & Kyoung Kwan Ahn, 2019. "Energy Management Strategy of a PEM Fuel Cell Excavator with a Supercapacitor/Battery Hybrid Power Source," Energies, MDPI, vol. 12(22), pages 1-24, November.
    17. Pan, Rui & Yang, Duo & Wang, Yujie & Chen, Zonghai, 2020. "Health degradation assessment of proton exchange membrane fuel cell based on an analytical equivalent circuit model," Energy, Elsevier, vol. 207(C).
    18. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    19. Anicic, Obrad & Jovic, Srdjan, 2016. "Adaptive neuro-fuzzy approach for ducted tidal turbine performance estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1111-1116.
    20. Asadi, Shahla & Nilashi, Mehrbakhsh & Iranmanesh, Mohammad & Hyun, Sunghyup Sean & Rezvani, Azadeh, 2022. "Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach," Technovation, Elsevier, vol. 118(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319693. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.