IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v7y2014i12p8317-8337d43489.html
   My bibliography  Save this article

Energy Consumption of a Battery Electric Vehicle with Infinitely Variable Transmission

Author

Listed:
  • Francesco Bottiglione

    (Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, Bari 70176, Italy
    These authors contributed equally to this work.)

  • Stefano De Pinto

    (Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, Bari 70176, Italy
    Department of Mechanical Engineering Sciences, University of Surrey, Guildford GU2 7XH, UK
    These authors contributed equally to this work.)

  • Giacomo Mantriota

    (Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, Bari 70176, Italy)

  • Aldo Sorniotti

    (Department of Mechanical Engineering Sciences, University of Surrey, Guildford GU2 7XH, UK
    These authors contributed equally to this work.)

Abstract

Battery electric vehicles (BEVs) represent a possible sustainable solution for personal urban transportation. Presently, the most limiting characteristic of BEVs is their short range, mainly because of battery technology limitations. A proper design and control of the drivetrain, aimed at reducing the power losses and thus increasing BEV range, can contribute to make the electrification of urban transportation a convenient choice. This paper presents a simulation-based comparison of the energy efficiency performance of six drivetrain architectures for BEVs. Although many different drivetrain and transmission architectures have been proposed for BEVs, no literature was found regarding BEVs equipped with infinitely variable transmissions (IVTs). The analyzed drivetrain configurations are: single- (1G) and two-speed (2G) gear drives, half toroidal (HT) and full toroidal (FT) continuously variable transmissions (CVTs), and infinitely variable transmissions (IVTs) with two different types of internal power flow (IVT-I and IVT-II). An off-line procedure for determining the most efficient control action for each drivetrain configuration is proposed, which allows selecting the optimal speed ratio for each operating condition. The energy consumption of the BEVs is simulated along the UDC (Urban Driving Cycle) and Japanese 10-15 driving cycle, with a backward facing approach. In order to achieve the lowest energy consumption, a trade-off between high transmission efficiency and flexibility in terms of allowed speed ratios is required.

Suggested Citation

  • Francesco Bottiglione & Stefano De Pinto & Giacomo Mantriota & Aldo Sorniotti, 2014. "Energy Consumption of a Battery Electric Vehicle with Infinitely Variable Transmission," Energies, MDPI, vol. 7(12), pages 1-21, December.
  • Handle: RePEc:gam:jeners:v:7:y:2014:i:12:p:8317-8337:d:43489
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/7/12/8317/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/7/12/8317/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ribau, João P. & Silva, Carla M. & Sousa, João M.C., 2014. "Efficiency, cost and life cycle CO2 optimization of fuel cell hybrid and plug-in hybrid urban buses," Applied Energy, Elsevier, vol. 129(C), pages 320-335.
    2. Hutchinson, Tim & Burgess, Stuart & Herrmann, Guido, 2014. "Current hybrid-electric powertrain architectures: Applying empirical design data to life cycle assessment and whole-life cost analysis," Applied Energy, Elsevier, vol. 119(C), pages 314-329.
    3. Tie, Siang Fui & Tan, Chee Wei, 2013. "A review of energy sources and energy management system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 82-102.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Polychronis Spanoudakis & Gerasimos Moschopoulos & Theodoros Stefanoulis & Nikolaos Sarantinoudis & Eftichios Papadokokolakis & Ioannis Ioannou & Savvas Piperidis & Lefteris Doitsidis & Nikolaos C. Ts, 2020. "Efficient Gear Ratio Selection of a Single-Speed Drivetrain for Improved Electric Vehicle Energy Consumption," Sustainability, MDPI, vol. 12(21), pages 1-19, November.
    2. Ruan, Jiageng & Walker, Paul David & Zhang, Nong & Wu, Jinglai, 2017. "An investigation of hybrid energy storage system in multi-speed electric vehicle," Energy, Elsevier, vol. 140(P1), pages 291-306.
    3. Stefano De Pinto & Pablo Camocardi & Christoforos Chatzikomis & Aldo Sorniotti & Francesco Bottiglione & Giacomo Mantriota & Pietro Perlo, 2020. "On the Comparison of 2- and 4-Wheel-Drive Electric Vehicle Layouts with Central Motors and Single- and 2-Speed Transmission Systems," Energies, MDPI, vol. 13(13), pages 1-24, June.
    4. Wang, Zhenzhen & Zhou, Jun & Rizzoni, Giorgio, 2022. "A review of architectures and control strategies of dual-motor coupling powertrain systems for battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    5. Junhui Liu & Lei Feng & Zhiwu Li, 2017. "The Optimal Road Grade Design for Minimizing Ground Vehicle Energy Consumption," Energies, MDPI, vol. 10(5), pages 1-31, May.
    6. Antti Ritari & Jari Vepsäläinen & Klaus Kivekäs & Kari Tammi & Heikki Laitinen, 2020. "Energy Consumption and Lifecycle Cost Analysis of Electric City Buses with Multispeed Gearboxes," Energies, MDPI, vol. 13(8), pages 1-21, April.
    7. Ruan, Jiageng & Walker, Paul & Zhang, Nong, 2016. "A comparative study energy consumption and costs of battery electric vehicle transmissions," Applied Energy, Elsevier, vol. 165(C), pages 119-134.
    8. Graba, M. & Mamala, J. & Bieniek, A. & Sroka, Z., 2021. "Impact of the acceleration intensity of a passenger car in a road test on energy consumption," Energy, Elsevier, vol. 226(C).
    9. Shilei Zhou & Paul Walker & Yang Tian & Cong Thanh Nguyen & Nong Zhang, 2021. "Comparison on Energy Economy and Vibration Characteristics of Electric and Hydraulic in-Wheel Drive Vehicles," Energies, MDPI, vol. 14(8), pages 1-15, April.
    10. Ruan, Jiageng & Walker, Paul D. & Watterson, Peter A. & Zhang, Nong, 2016. "The dynamic performance and economic benefit of a blended braking system in a multi-speed battery electric vehicle," Applied Energy, Elsevier, vol. 183(C), pages 1240-1258.
    11. Alexander Koch & Olaf Teichert & Svenja Kalt & Aybike Ongel & Markus Lienkamp, 2020. "Powertrain Optimization for Electric Buses under Optimal Energy-Efficient Driving," Energies, MDPI, vol. 13(23), pages 1-19, December.
    12. Fabio Vacca & Stefano De Pinto & Ahu Ece Hartavi Karci & Patrick Gruber & Fabio Viotto & Carlo Cavallino & Jacopo Rossi & Aldo Sorniotti, 2017. "On the Energy Efficiency of Dual Clutch Transmissions and Automated Manual Transmissions," Energies, MDPI, vol. 10(10), pages 1-22, October.
    13. Cheng, Zhun, 2023. "High nonlinearity of BEV's stepped automatic transmission design objectives and its optimal solution by a novel ISA-RSA," Energy, Elsevier, vol. 282(C).
    14. Milan Perkušić & Damir Jelaska & Srdjan Podrug & Vjekoslav Tvrdić, 2017. "On the Feasibility of Independently Controllable Transmissions," Energies, MDPI, vol. 10(11), pages 1-13, November.
    15. Polychronis Spanoudakis & Nikolaos C. Tsourveloudis & Lefteris Doitsidis & Emmanuel S. Karapidakis, 2019. "Experimental Research of Transmissions on Electric Vehicles’ Energy Consumption," Energies, MDPI, vol. 12(3), pages 1-15, January.
    16. Jarosław Mamala & Michał Śmieja & Krzysztof Prażnowski, 2021. "Analysis of the Total Unit Energy Consumption of a Car with a Hybrid Drive System in Real Operating Conditions," Energies, MDPI, vol. 14(13), pages 1-16, July.

    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. Du, Jiuyu & Ouyang, Danhua, 2017. "Progress of Chinese electric vehicles industrialization in 2015: A review," Applied Energy, Elsevier, vol. 188(C), pages 529-546.
    2. Babu, Ajay & Ashok, S., 2015. "Improved parallel mild hybrids for urban roads," Applied Energy, Elsevier, vol. 144(C), pages 276-283.
    3. Mustafa Hamurcu & Tamer Eren, 2020. "Electric Bus Selection with Multicriteria Decision Analysis for Green Transportation," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    4. Harris, Andrew & Soban, Danielle & Smyth, Beatrice M. & Best, Robert, 2018. "Assessing life cycle impacts and the risk and uncertainty of alternative bus technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 569-579.
    5. Huang, Yanjun & Wang, Hong & Khajepour, Amir & Li, Bin & Ji, Jie & Zhao, Kegang & Hu, Chuan, 2018. "A review of power management strategies and component sizing methods for hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 132-144.
    6. Robert J.R. Elliott & Viet Nguyen-Tien & Eric Strobl & Chengyu Zhang, 2024. "Estimating the longevity of electric vehicles: What do 300 million MOT test results tell us?," CEP Discussion Papers dp1972, Centre for Economic Performance, LSE.
    7. Sandoval, Cinda & Alvarado, Victor M. & Carmona, Jean-Claude & Lopez Lopez, Guadalupe & Gomez-Aguilar, J.F., 2017. "Energy management control strategy to improve the FC/SC dynamic behavior on hybrid electric vehicles: A frequency based distribution," Renewable Energy, Elsevier, vol. 105(C), pages 407-418.
    8. Baresch, Martin & Moser, Simon, 2019. "Allocation of e-car charging: Assessing the utilization of charging infrastructures by location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 388-395.
    9. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    10. Peng, Fei & Zhao, Yuanzhe & Li, Xiaopeng & Liu, Zhixiang & Chen, Weirong & Liu, Yang & Zhou, Donghua, 2017. "Development of master-slave energy management strategy based on fuzzy logic hysteresis state machine and differential power processing compensation for a PEMFC-LIB-SC hybrid tramway," Applied Energy, Elsevier, vol. 206(C), pages 346-363.
    11. Tharsis Teoh & Oliver Kunze & Chee-Chong Teo & Yiik Diew Wong, 2018. "Decarbonisation of Urban Freight Transport Using Electric Vehicles and Opportunity Charging," Sustainability, MDPI, vol. 10(9), pages 1-20, September.
    12. Ru-Jen Lin & Rong-Huei Chen & Thao-Minh Ho, 2013. "Market Demand, Green Innovation, and Firm Performance: Evidence from Hybrid Vehicle Industry," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    13. Chen, Xu & Li, Mince & Chen, Zonghai, 2023. "Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    14. Lane, Blake & Kinnon, Michael Mac & Shaffer, Brendan & Samuelsen, Scott, 2022. "Deployment planning tool for environmentally sensitive heavy-duty vehicles and fueling infrastructure," Energy Policy, Elsevier, vol. 171(C).
    15. Das, Kaushik & Kumar, Roushan & Krishna, Anurup, 2024. "Analyzing electric vehicle battery health performance using supervised machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    16. Shi, Xiao & Pan, Jian & Wang, Hewu & Cai, Hua, 2019. "Battery electric vehicles: What is the minimum range required?," Energy, Elsevier, vol. 166(C), pages 352-358.
    17. Ming Cai & Weijie Chen & Xiaojun Tan, 2017. "Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model," Energies, MDPI, vol. 10(10), pages 1-16, October.
    18. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    19. Dennis Dreier & Mark Howells, 2019. "OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling," Energies, MDPI, vol. 12(7), pages 1-26, April.
    20. Menon, Ramanunni P. & Paolone, Mario & Maréchal, François, 2013. "Study of optimal design of polygeneration systems in optimal control strategies," Energy, Elsevier, vol. 55(C), pages 134-141.

    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:gam:jeners:v:7:y:2014:i:12:p:8317-8337:d:43489. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.