IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v182y2019icp166-178.html
   My bibliography  Save this article

Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms

Author

Listed:
  • Sierra, G.
  • Orchard, M.
  • Goebel, K.
  • Kulkarni, C.

Abstract

This article presents a holistic framework for the design, implementation and experimental validation of Battery Management Systems (BMS) in rotatory-wing Unmanned Aerial Vehicles (UAVs) that allows to accurately (i) estimate the State of Charge (SOC), and (ii) predict the End of Discharge (EOD) time of lithium-polymer batteries in small-size multirotors by using a model-based prognosis architecture that is efficient and feasible to implement in low-cost hardware. The proposed framework includes a simplified battery model that incorporates the electric load dependence, temperature dependence and SOC dependence by using the concept of Artificial Evolution to estimate some of its parameters, along with a novel Outer Feedback Correction Loop (OFCL) during the estimation stage which adjusts the variance of the process noise to diminish bias in Bayesian state estimation and helps to compensate problems associated with incorrect initial conditions in a non-observable dynamic system. Also, it provides an aerodynamic-based characterization of future power consumption profiles. A quadrotor has been used as validation platform. The results of this work will allow making decisions about the flight plan and having enough confidence in those decisions so that the mission objectives can be optimally achieved.

Suggested Citation

  • Sierra, G. & Orchard, M. & Goebel, K. & Kulkarni, C., 2019. "Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 166-178.
    • Handle: RePEc:eee:reensy:v:182:y:2019:i:c:p:166-178
    DOI: 10.1016/j.ress.2018.04.030
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832018301406
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2018.04.030?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. Burgos-Mellado, Claudio & Orchard, Marcos E. & Kazerani, Mehrdad & Cárdenas, Roberto & Sáez, Doris, 2016. "Particle-filtering-based estimation of maximum available power state in Lithium-Ion batteries," Applied Energy, Elsevier, vol. 161(C), pages 349-363.
    2. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
    3. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
    4. Ming-Hui Chang & Han-Pang Huang & Shu-Wei Chang, 2013. "A New State of Charge Estimation Method for LiFePO 4 Battery Packs Used in Robots," Energies, MDPI, vol. 6(4), pages 1-24, April.
    5. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
    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. Wang, Shunli & Fan, Yongcun & Jin, Siyu & Takyi-Aninakwa, Paul & Fernandez, Carlos, 2023. "Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Shaojie Ai & Jia Song & Guobiao Cai, 2022. "Sequence-to-Sequence Remaining Useful Life Prediction of the Highly Maneuverable Unmanned Aerial Vehicle: A Multilevel Fusion Transformer Network Solution," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    3. Sierra, Gina & Robinson, Elinirina I. & Goebel, Kai, 2021. "Improving tail accuracy of the predicted cumulative distribution function of time of failure," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    4. Haitao Zhang & Ming Zhou & Xudong Lan, 2019. "State of Charge Estimation Algorithm for Unmanned Aerial Vehicle Power-Type Lithium Battery Packs Based on the Extended Kalman Filter," Energies, MDPI, vol. 12(20), pages 1-15, October.

    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. Cuma, Mehmet Ugras & Koroglu, Tahsin, 2015. "A comprehensive review on estimation strategies used in hybrid and battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 517-531.
    2. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.
    3. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    4. Yong Tian & Bizhong Xia & Mingwang Wang & Wei Sun & Zhihui Xu, 2014. "Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(12), pages 1-19, December.
    5. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    6. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    7. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & Lu, Dylan Dah-Chuan & He, Tingting, 2018. "Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter," Energy, Elsevier, vol. 158(C), pages 1028-1037.
    8. Wang, Limei & Pan, Chaofeng & Liu, Liang & Cheng, Yong & Zhao, Xiuliang, 2016. "On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis," Applied Energy, Elsevier, vol. 168(C), pages 465-472.
    9. Bizhong Xia & Haiqing Wang & Yong Tian & Mingwang Wang & Wei Sun & Zhihui Xu, 2015. "State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 8(6), pages 1-21, June.
    10. Zheng, Xiujuan & Fang, Huajing, 2015. "An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 74-82.
    11. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2014. "A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries," Applied Energy, Elsevier, vol. 135(C), pages 81-87.
    12. Benedikt Rzepka & Simon Bischof & Thomas Blank, 2021. "Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide," Energies, MDPI, vol. 14(13), pages 1-17, June.
    13. Lyons, P.F. & Wade, N.S. & Jiang, T. & Taylor, P.C. & Hashiesh, F. & Michel, M. & Miller, D., 2015. "Design and analysis of electrical energy storage demonstration projects on UK distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 677-691.
    14. Shyh-Chin Huang & Kuo-Hsin Tseng & Jin-Wei Liang & Chung-Liang Chang & Michael G. Pecht, 2017. "An Online SOC and SOH Estimation Model for Lithium-Ion Batteries," Energies, MDPI, vol. 10(4), pages 1-18, April.
    15. Ramesh Kumar Chidambaram & Dipankar Chatterjee & Barnali Barman & Partha Pratim Das & Dawid Taler & Jan Taler & Tomasz Sobota, 2023. "Effect of Regenerative Braking on Battery Life," Energies, MDPI, vol. 16(14), pages 1-24, July.
    16. Wenhui Zheng & Bizhong Xia & Wei Wang & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2019. "State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer," Energies, MDPI, vol. 12(13), pages 1-14, June.
    17. Fei Feng & Rengui Lu & Guo Wei & Chunbo Zhu, 2015. "Online Estimation of Model Parameters and State of Charge of LiFePO 4 Batteries Using a Novel Open-Circuit Voltage at Various Ambient Temperatures," Energies, MDPI, vol. 8(4), pages 1-27, April.
    18. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    19. Victor Pizarro-Carmona & Marcelo Cortés-Carmona & Rodrigo Palma-Behnke & Williams Calderón-Muñoz & Marcos E. Orchard & Pablo A. Estévez, 2019. "An Optimized Impedance Model for the Estimation of the State-of-Charge of a Li-Ion Cell: The Case of a LiFePO 4 (ANR26650)," Energies, MDPI, vol. 12(4), pages 1-16, February.
    20. Lin, Cheng & Yu, Quanqing & Xiong, Rui & Wang, Le Yi, 2017. "A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 205(C), pages 892-902.

    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:reensy:v:182:y:2019:i:c:p:166-178. 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: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.