DOI QR코드

DOI QR Code

A Novel Battery State of Health Estimation Method Based on Outlier Detection Algorithm

  • Piao, Chang-hao (Institution of Pattern Recognition and Application, Chongqing University of Posts and Telecommunications) ;
  • Hu, Zi-hao (Institution of Pattern Recognition and Application, Chongqing University of Posts and Telecommunications) ;
  • Su, Ling (Chongqing Changan New Energy Automobile CO., LTD.) ;
  • Zhao, Jian-fei (Institution of Pattern Recognition and Application, Chongqing University of Posts and Telecommunications)
  • 투고 : 2015.04.28
  • 심사 : 2016.06.13
  • 발행 : 2016.11.01

초록

A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

키워드

참고문헌

  1. Hannan, M.A, et al, "Hybrid electric vehicles and their challenges: A review," Renewable & Sustainable Energy Reviews, vol. 29, no. 7, pp. 135-150, 2014. https://doi.org/10.1016/j.rser.2013.08.097
  2. Chen Ping, Piao Changhao, et al, "Modeling of battery management system software in virtual simulation environment," Journal of Automotive Safety and Energy, vol.4, no.1, pp.67-74, 2013.
  3. Ying Xie, Zhao-guang Wang, et al. "Development of Battery System Testing Machine," International Journal of Future Engineering, vol.9, no.5, pp. 1-7, 2015.
  4. Changhao Piao, Wenli Fu, Chongdu Cho, et al, "Online Parameter Estimation of the Ni-MH Batteries Based on Statistical Methods," Energies, vol.3, no.2, pp. 206-215, 2010. https://doi.org/10.3390/en3020206
  5. Huet F, "A review of impedance Measurements for Determination of the state-of-charge and state-ofhealth of Secondary Batteries," Journal of Power Sources, vol.70, no.1, pp. 59-69, 1998. https://doi.org/10.1016/S0378-7753(97)02665-7
  6. Calabek M, Micka K, et al, "Resistance Changes and Premature Capacity Loss in Lead Battery Plates," Journal of Power Sources, vol.62, no.2, pp. 161-166, 1996. https://doi.org/10.1016/S0378-7753(96)02432-9
  7. Fan J, Fedkiw P, "Electrochemical impedance spectra of full cells: relation to capacity and capacity-rate of rechargeable Li cells using LiCoO2, LiMn2O4, and LiNiO2 cathodes," Journal of Power Sources, vol.72, no.2, pp. 165-173, 1998. https://doi.org/10.1016/S0378-7753(97)02708-0
  8. Gregory L, Plett, "Extended kalman filtering for battery management systems of LiPB-based HEV battery packs, part 3. state and parameter estimation," Journal of Power Sources, vol. 134, pp. 277-292, 2004. https://doi.org/10.1016/j.jpowsour.2004.02.033
  9. Dai Haifeng, Sun Zechang, "Estimation of Internal States of Power Lithium-ion Batteries Used on Electric Vehicles by Dual Extended Kalman Filter," Journal of Mechanical Engineering, vol. 45, no. 6, pp. 95-101, 2009.
  10. H. Nakamura, D. Yumoto, "The application of adaptive digital filter for the internal state estimate on of batteries," SCIE Annual Conference in Fukui, Fukui, Japan, 2003.
  11. IL-Song Kim, "A technique for estimating the state of health of lithium batteries through a dual-slidingmode observer," IEEE Transactions on Power Electronics, vol. 25, no. 4, pp. 1013-1022, 2010. https://doi.org/10.1109/TPEL.2009.2034966
  12. Edwin M, Raymond T, "Algorithms for Mining Distance-Based Ouliers in Large Datasets," Very Large Data Bases Conference Proceedings, University of Vienna, Austria, 1998.
  13. Piao Changhao, Huang Zhi, "High-Dimensional Data Stream Outlier Detection Algorithm Based on Angle Distribution," Journal of Shanghai Jiao Tong University, vol. 48, no. 5, pp. 647-652, 2014.
  14. Kriegel H P, Schubert M, Zimek A, "Angle-based outlier detection in high dimensional data," Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, 2008.
  15. Pham N, Pagh R, "A near-linear time approximation algorithm for angle-based outlier detection in high dimensional data," Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, 2012.
  16. Changhao Piao, Huang Zhi, Ling Su and Sheng Lu, Research on Outlier Detection Algorithm for Evaluation of Battery System Safety, Advances in Mechanical Engineering, vol. 6, pp. 1-7, January 2014
  17. Lu L, Han X, Li J, et al. "A review on the key issues for lithium-ion battery management in electric vehicle," Journal of Power Sources, vol. 226, no. 6, pp. 272-288, 2013. https://doi.org/10.1016/j.jpowsour.2012.10.060
  18. Wang J, Liu P, Jocelyn H G, et al. "Cycle-Life Model for Graphite-LiFePO4 Cells," Journal of Power Sources, vol.196, pp. 3924-3948, 2011.
  19. Chang-hao Piao, Qi-fan Yu, et al. "Virtual Environment Modeling for Battery Management System," Journal of Electrical Engineering & Technology, vol. 9, no. 5, pp. 1729-1738, 2014. https://doi.org/10.5370/JEET.2014.9.5.1729