Username   Password       Forgot your password?  Forgot your username? 

 

Health Status Comparisons of Lithium-Ion Batteries When Fusing Various Features

Volume 15, Number 1, January 2019, pp. 138-145
DOI: 10.23940/ijpe.19.01.p14.138145

Xueling Haoa, Yongquan Suna,b, Zimei Sua, and Bo Liua

aInstitute of Sensor and Reliability Engineering (ISRE), Harbin University of Science and Technology, Harbin, 150080, China
bCenter for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, 20742, USA

(Submitted on October 6, 2018; Revised on November 16, 2018; Accepted on December 18, 2018)

Abstract:

In order to solve the one-sidedness problem based on a single indicator for evaluating the status of health (SOH) and predicting the remaining useful life (RUL) of lithium-ion batteries, a new algorithm is developed where the different features are integrated on the basis of the beta function distribution. The data of the capacity, internal resistance, and constant current charging time (CCCT) of lithium-ion batteries are analyzed, and then the fused features are presented. The simulation includes the data fusion of different types of batteries and the comparison between the SOH of a single indicator and the SOH of two or three fused indicators. From the simulation results, the end-of-life of the three features after fusion is shorter than the capacity, which indicates that multi-indicators are closer to the real situation than a single indicator for SOH and RUL.

 

References: 21

      1. K. Chatzizacharia, V. Benekis, and D. Hatziavramidis, “A Blueprint for an Energy Policy in Greece with Considerations of Climate Change,” Applied Energy, Vol. 162, pp. 382-389, 2016
      2. T. Fujimi, Y. Kajitani, and S. E. Chang, “Effective and Persistent Changes in Household Energy-Saving Behaviors: Evidence from Post-Tsunami Japan,” Applied Energy, Vol. 167, pp. 93-106, 2016
      3. I. M. Kong, J. W. Choi, S. I. Kim, E. S. Lee, and M. S. Kim, “Experimental Study on the Self Humidification Effect in Proton Exchange Membrane Fuel Cells Containing Double Gas Diffusion Backing Layer,” Applied Energy, Vol. 145, pp. 345-353, 2015
      4. T. R. Ashwin, Y. M. Chung, and J. H. Wang, “Capacity Fade Modelling of Lithium-Ion Battery under Cyclic Loading Conditions,” Journal of Power Sources, Vol. 328, pp. 586-598, 2016
      5. J. B. Goodenough and Y. Kim, “Challenges for Rechargeable Li Batteries,” Chemistry of Materials, Vol. 22, No. 3, pp. 587-603, 2010
      6. L. Lu, X. Han, J. Li, J. Hua, and M. Ouyang, “A Review on the Key Issues for Lithium-Ion Battery Management in Electric Vehicles,” Power Sources, Vol. 226, pp. 272-288, 2013
      7. X. Zhang, Y. J. Wang, and C. Liu, “A Novel Approach of Remaining Discharge Energy Prediction for Large Format Lithium-Ion Battery Pack,” Journal of Power Sources, Vol. 343, pp. 216-225, 2017
      8. M. Berecibar, I. Gandiaga, I. Villarreal, N. Omar, J. Van Mierlo, and P. Van den Bossche, “Critical Review of State of Health Estimation Methods of Li-Ion Batteries for Real Applications,” Renewable and Sustainable Energy Review, Vol. 56, pp. 572-587, 2016
      9. E. Sarasketa-Zabala, E. Martinez-Laserna, M. Berecibar, I. Gandiaga, L. R. Martinez, and I. Villarreal, “Realistic Lifetime Prediction Approach for Li-Ion Batteries,” Applied Energy, Vol. 162, pp. 839-852, 2016
      10. J. Wu, C. Zhang, and Z. Chen, “An Online Method for Lithium-Ion Battery Remaining Useful Life Estimation using Importance Sampling and Neural Networks,” Applied Energy, Vol. 173, pp. 134-140, 2016
      11. T. Fen, L. Yang, X. Zhao, H. Zhang, and J. Qiang, “Online Identification of Lithium-Ion Battery Parameters based on an Improved Equivalent Circuit Model and its Implementation on Battery State-of-Power Prediction,” Power Sources, pp. 192-203, 2015
      12. A. Eddahech, O. Briat, and J. Vinassa, “Determination of Lithium-Ion Battery State-of-Health based on Constant-Voltage Charge Phase,” Power Sources, Vol. 258, pp. 218-227, 2014
      13. Y. Zhang and B. Guo, “Online Capacity Estimation of Lithium-Ion Batteries based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine,” Energies, Vol. 8, No. 11, pp. 12439-12457, November 2015
      14. N. Williard, W. He, M. Osterman, and M. Pecht, “Comparative Analysis of Features for Determining State of Health in Lithium-Ion Batteries,” International Journal of Prognostics and Health Management, pp. 2153-2648, 2013
      15. Y. P. Zhou and M. H. Huang, “On-Board Capacity Estimation of Lithium-Ion Batteries based on Charge Phase,” Journal of Electrical Engineering and Technology, Vol. 13, pp. 1921-718, 2018
      16. A. E. Mejdoubi, A. Oukaour, and H. Chaoui, “State-of-Charge and State-of-Health Lithium-ion Batteries Diagnosis According to Surface Temperature Variation,” IEEE Transactions on Software Engineering, Vol. 63, No. 4, April 2016
      17. M. B. Pinson, and M. Z. Bazant, “Theory of SEI Formation in Rechargeable Batteries: Capacity Fade, Accelerated Aging and Lifetime Prediction,” Electrochem, Vol. 160, pp. A243-A250, 2012
      18. A. Barre, B. Deguilhem, S. Grolleau, M. Gerard, and D. J. Riu, “Failure Analysis of Lithium-Ion Batteries,” Power Sources, Vol. 241, pp. 680-689, 2013
      19. Q. Y. Wang, S. Wang, and J. N. Zhang, “Overview of the Failure Analysis of Lithium-Ion Batteries,” Energy Storage Science and Technology, No. 43, pp. 2095-4239, 2017
      20. W. S. Li and S. Z. Qiu, “Causes for Capacity Decrease of Li-Ion Batteries,” Chinese Battery Industry, Vol. 6, No. 1, pp. 21-24. 2001
      21. Y. Xing, N. Williard, and K. L. Tsui, “A Comparative Review of Prognostics based Reliability Methods for Lithium Batteries,” in Proceedings of Prognostics and System Health Management Conference, pp. 1-6, IEEE, 2011

           

          Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

           
          This site uses encryption for transmitting your passwords. ratmilwebsolutions.com