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On-board Capacity Estimation of Lithium-ion Batteries Based on Charge Phase

  • Zhou, Yapeng (School of Automotive Engineering, Wuhan University of Technology) ;
  • Huang, Miaohua (School of Automotive Engineering, Wuhan University of Technology)
  • Received : 2017.07.16
  • Accepted : 2017.12.07
  • Published : 2018.03.01

Abstract

Capacity estimation is indispensable to ensure the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Therefore it's quite necessary to develop an effective on-board capacity estimation technique. Based on experiment, it's found constant current charge time (CCCT) and the capacity have a strong linear correlation when the capacity is more than 80% of its rated value, during which the battery is considered healthy. Thus this paper employs CCCT as the health indicator for on-board capacity estimation by means of relevance vector machine (RVM). As the ambient temperature (AT) dramatically influences the capacity fading, it is added to RVM input to improve the estimation accuracy. The estimations are compared with that via back-propagation neural network (BPNN). The experiments demonstrate that CCCT with AT is highly qualified for on-board capacity estimation of lithium-ion batteries via RVM as the results are more precise and reliable than that calculated by BPNN.

References

  1. E. Uherek, T. Halenka, J. Borken-Kleefeld, Y. Balkanski, T. Berntsen, C. Borrego, et al., "Transport Impacts on Atmosphere and Climate: Land Transport," Atmospheric Environment, vol. 44, no. 37, pp. 4772-4816, Dec. 2010. https://doi.org/10.1016/j.atmosenv.2010.01.002
  2. H. Cai and M. Xu, "Greenhouse Gas Implications of Fleet Electrification Based on Big Data-Informed Individual Travel Patterns," Environ Sci Technol, vol. 47, no. 16, pp. 9035-43, Jul. 2013. https://doi.org/10.1021/es401008f
  3. A. P. Schmidt, M. Bitzer, A. W. Imre, and L. Guzzella, "Model-Based Distinction and Quantification of Capacity Loss and Rate Capability Fade in Li-Ion Batteries," Journal of Power Sources, vol. 195, no. 22, pp. 7634-7638, 2010. https://doi.org/10.1016/j.jpowsour.2010.06.011
  4. M. V. Micea, L. Ungurean, G. N. Carstoiu, and V. Groza, "Online State-of-Health Assessment for Battery Management Systems," IEEE Trans. on Instrumentation and Measurement, vol. 60, no. 6, pp. 1997-2006, Mar. 2011. https://doi.org/10.1109/TIM.2011.2115630
  5. W. He, N. Williard, M. Osterman, and M. Pecht, "Prognostics of Lithium-ion Batteries based on Dempster-Shafer Theory and The Bayesian Monte Carlo Method," Journal of Power Sources, vol. 196, no. 23, pp. 10314-10321, Dec. 2011. https://doi.org/10.1016/j.jpowsour.2011.08.040
  6. F. Yang, D. Wang, Y. Xing, and K.-L. Tsui, "Prognostics of Li(NiMnCo)O2-based Lithium-ion Batteries Using A Novel Battery Degradation Model," Microelectronics Reliability, vol. 70, pp. 70-78, Mar. 2017. https://doi.org/10.1016/j.microrel.2017.02.002
  7. J. Li, L. Wang, C. Lyu, L. Zhang, and H. Wang, "Discharge Capacity Estimation for Li-Ion Batteries based on Particle Filter under Multi-Operating Conditions," Energy, vol. 86, pp. 638-648, Jun. 2015. https://doi.org/10.1016/j.energy.2015.04.021
  8. C. Hu, G. Jain, P. Tamirisa, and T. Gorka, "Method for Estimating Capacity and Predicting Remaining Useful Life of Lithium-ion Battery," Applied Energy, vol. 126, pp. 182-189, Aug. 2014. https://doi.org/10.1016/j.apenergy.2014.03.086
  9. Y. Hua, A. Cordoba-Arenas, N. Warner, and G. Rizzoni, "A Multi Time-Scale State-of-Charge and State-of-Health Estimation Framework Using Nonlinear Predictive Filter for Lithium-ion Battery Pack with Passive Balance Control," Journal of Power Sources, vol. 280, pp. 293-312, Apr. 2015. https://doi.org/10.1016/j.jpowsour.2015.01.112
  10. Chenghui Zhang, Yun Zhang, and Y. Li, "A Novel Battery State-of-Health Estimation Method for Hybrid Electric Vehicles," IEEE/ASME Trans. on Mechatronics, vol. 20, no. 5, pp. 2604-2612, Jun. 2015. https://doi.org/10.1109/TMECH.2014.2371919
  11. J. Remmlinger, M. Buchholz, T. Soczka-Guth, and K. Dietmayer, "On-Board State-of-Health Monitoring of Lithium-ion Batteries Using Linear Parameter-Varying Models," Journal of Power Sources, vol. 239, pp. 689-695, Oct. 2013. https://doi.org/10.1016/j.jpowsour.2012.11.102
  12. Z. Chen, C. C. Mi, Y. Fu, J. Xu, and X. Gong, "Online Battery State of Health Estimation based on Genetic Algorithm for Electric and Hybrid Vehicle Applications," Journal of Power Sources, vol. 240, pp. 184-192, Oct. 2013. https://doi.org/10.1016/j.jpowsour.2013.03.158
  13. S. Tong, M. P. Klein, and J. W. Park, "On-line Optimization of Battery Open Circuit Voltage for Improved State-of-Charge and State-of-Health Estimation," Journal of Power Sources, vol. 293, pp. 416-428, Oct. 2015. https://doi.org/10.1016/j.jpowsour.2015.03.157
  14. Z. Guo, X. Qiu, G. Hou, B. Y. Liaw, and C. Zhang, "State of Health Estimation for Lithium-ion Batteries Based on Charging Curves," Journal of Power Sources, vol. 249, pp. 457-462, Mar. 2014. https://doi.org/10.1016/j.jpowsour.2013.10.114
  15. J. Remmlinger, M. Buchholz, M. Meiler, P. Bernreuter, and K. Dietmayer, "State-of-Health Monitoring of Lithium-ion Batteries in Electric Vehicles by on-board Internal Resistance Estimation," Journal of Power Sources, vol. 196, no.12, pp. 5357-5363, Jun. 2011. https://doi.org/10.1016/j.jpowsour.2010.08.035
  16. I.-S. Kim, "A Technique for Estimating the State of Health of Lithium Batteries through a Dual-Sliding-Mode Observer," IEEE Trans. on Power Electronics, vol. 25, no. 4, pp. 1013-1022, Oct. 2009. https://doi.org/10.1109/TPEL.2009.2034966
  17. K. S. Ng, C.-S. Moo, Y.-P. Chen, and Y.-C. Hsieh, "Enhanced Coulomb Counting Method for Estimating State-of-Charge and State-of-Health of Lithium-ion Batteries," Applied Energy, vol. 86, no. 9, pp. 1506-1511, Sep. 2009. https://doi.org/10.1016/j.apenergy.2008.11.021
  18. K. Goebel, B. Saha, A. Saxena, J. Celaya, and J. Christophersen, "Prognostics in Battery Health Management," IEEE Instrumentation & Measurement Magazine, vol. 11, no. 4, pp. 33-40, Jul. 2008. https://doi.org/10.1109/MIM.2008.4579269
  19. C. Hu, G. Jain, P. Zhang, C. Schmidt, P. Gomadam, and T. Gorka, "Data-driven Method based on Particle Swarm Optimization and k-Nearest Neighbor Regression for Estimating Capacity of Lithium-ion Battery," Applied Energy, vol. 129, pp. 49-55, Sep. 2014. https://doi.org/10.1016/j.apenergy.2014.04.077
  20. X. Han, M. Ouyang, L. Lu, J. Li, Y. Zheng, and Z. Li, "A Comparative Study of Commercial Lithium-ion Battery Cycle Life in Electrical Vehicle: Aging Mechanism Identification," Journal of Power Sources, vol. 251, pp. 38-54, Apr. 2014. https://doi.org/10.1016/j.jpowsour.2013.11.029
  21. H. T. Lin, T. J. Liang, and S. M. Chen, "Estimation of Battery State of Health Using Probabilistic Neural Network," IEEE Trans. on Industrial Informatics, vol. 9, no. 2, pp. 679-685, May 2013. https://doi.org/10.1109/TII.2012.2222650
  22. D. T. Liu, J. B. Zhou, H. T. Liao, Y. Peng, and X. Y. Peng, "A Health Indicator Extraction and Optimization Framework for Lithium-ion Battery Degradation Modeling and Prognostics," IEEE Trans. on Systems Man Cybernetics-Systems, vol. 45, no. 6, pp. 915-928, Jan. 2015. https://doi.org/10.1109/TSMC.2015.2389757
  23. Y. Li, P. Chattopadhyay, A. Ray, and C. D. Rahn, "Identification of the Battery State-of-Health Parameter from Input-Output Pairs of Time Series Data," Journal of Power Sources, vol. 285, pp. 235-246, Jul. 2015. https://doi.org/10.1016/j.jpowsour.2015.03.068
  24. A. Widodo, M. C. Shim, W. Caesarendra, and B. S. Yang, "Intelligent Prognostics for Battery Health Monitoring based on Sample Entropy," Expert Systems with Applications, vol. 38, no. 9, pp. 11763-11769, Sep. 2011. https://doi.org/10.1016/j.eswa.2011.03.063
  25. L. Wang, C. Pan, L. Liu, Y. Cheng, and X. Zhao, "On-board State of Health Estimation of LiFePO4 Battery Pack through Differential Voltage Analysis," Applied Energy, vol. 168, pp. 465-472, Apr. 2016. https://doi.org/10.1016/j.apenergy.2016.01.125
  26. C. Weng, Y. Cui, J. Sun, and H. Peng, "On-board State of Health Monitoring of Lithium-ion Batteries Using Incremental Capacity Analysis With Support Vector Regression," Journal of Power Sources, vol. 235, pp. 36-44, Aug. 2013. https://doi.org/10.1016/j.jpowsour.2013.02.012
  27. 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, Nov. 2015. https://doi.org/10.3390/en81112320
  28. B. Sun, J. Jiang, F. Zheng, W. Zhao, B. Y. Liaw, H. Ruan, et al., "Practical State of Health Estimation of Power Batteries based on Delphi Method and Grey Relational Grade Analysis," Journal of Power Sources, vol. 282, pp. 146-157, May 2015. https://doi.org/10.1016/j.jpowsour.2015.01.106
  29. D. T. Liu, H. Wang, Y. Peng, W. Xie, and H. T. Liao, "Satellite Lithium-ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction," Energies, vol. 6, no. 8, pp. 3654-3668, Jul. 2013. https://doi.org/10.3390/en6083654
  30. S. Torai, M. Nakagomi, S. Yoshitake, S. Yamaguchi, and N. Oyama, "State-of-health Estimation of LiFePO4/Graphite Batteries based on a Model Using Differential Capacity," Journal of Power Sources, vol. 306, pp. 62-69, Feb. 2016. https://doi.org/10.1016/j.jpowsour.2015.11.070
  31. V. Klass, M. Behm, and G. Lindbergh, "A Support Vector Machine-based State-of-Health Estimation Method for Lithium-ion Batteries under Electric Vehicle Operation," Journal of Power Sources, vol. 270, pp. 262-272, Dec. 2014. https://doi.org/10.1016/j.jpowsour.2014.07.116
  32. C. Zhang, J. Jiang, W. Zhang, Y. Wang, S. Sharkh, and R. Xiong, "A Novel Data-Driven Fast Capacity Estimation of Spent Electric Vehicle Lithium-ion Batteries," Energies, vol. 7, no. 12, pp. 8076-8094, Dec. 2014. https://doi.org/10.3390/en7128076
  33. A. A. Hussein, "Capacity Fade Estimation in Electric Vehicle Li-ion Batteries Using Artificial Neural Networks," IEEE Trans. on Industry Applications, vol. 51, no. 3, pp. 2321-2330, May 2015. https://doi.org/10.1109/TIA.2014.2365152
  34. C. Lu, L. Tao, and H. Fan, "Li-ion Battery Capacity Estimation: A Geometrical Approach," Journal of Power Sources, vol. 261, pp. 141-147, Sep. 2014. https://doi.org/10.1016/j.jpowsour.2014.03.058
  35. J. Li, C. Lyu, L. Wang, L. Zhang, and C. Li, "Remaining Capacity Estimation of Li-Ion Batteries based on Temperature Sample Entropy and Particle Filter," Journal of Power Sources, vol. 268, pp. 895-903, Dec. 2014. https://doi.org/10.1016/j.jpowsour.2014.06.133
  36. A. Eddahech, O. Briat, and J.-M. Vinassa, "Determination of Lithium-ion Battery State-of-Health based on Constant-Voltage Charge Phase," Journal of Power Sources, vol. 258, pp. 218-227, Jul. 2014. https://doi.org/10.1016/j.jpowsour.2014.02.020
  37. C. Hu, G. Jain, C. Schmidt, C. Strief, and M. Sullivan, "Online Estimation of Lithium-ion Battery Capacity Using Sparse Bayesian Learning," Journal of Power Sources, vol. 289, pp. 105-113, Sep. 2015. https://doi.org/10.1016/j.jpowsour.2015.04.166
  38. Y. Zheng, Y.-B. He, K. Qian, B. Li, X. Wang, J. Li, et al., "Deterioration of Lithium-ion Phosphate/Graphite Power Batteries under High-rate Discharge Cycling," Electrochimica Acta, vol. 176, pp. 270-279, Sep. 2015. https://doi.org/10.1016/j.electacta.2015.06.096
  39. Y. Zhang, C.-Y. Wang, and X. Tang, "Cycling Degradation of an Automotive LiFePO4 Lithium-ion Battery," Journal of Power Sources, vol. 196, no. 3, pp. 1513-1520, Feb. 2011. https://doi.org/10.1016/j.jpowsour.2010.08.070
  40. X. Zheng and H. Fang, "An Integrated Unscented Kalman Filter and Relevance Vector Regression Approach for Lithium-ion Battery Remaining Useful Life and Short-Term Capacity Prediction," Reliability Engineering & System Safety, vol. 144, pp. 74-82, Dec. 2015. https://doi.org/10.1016/j.ress.2015.07.013
  41. D. T. Liu, J. B. Zhou, D. W. Pan, Y. Peng, and X. Y. Peng, "Lithium-ion Battery Remaining Useful Life Estimation with an Optimized Relevance Vector Machine Algorithm with Incremental Learning," Measurement, vol. 63, pp. 143-151, Mar. 2015. https://doi.org/10.1016/j.measurement.2014.11.031
  42. M. E. Tipping and A. C. Faul, "Fast Marginal Likelihood Maximisation for Sparse Bayesian Models," in Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003.
  43. B. Saha and K. Goebel, "Battery Data Set," NASA Ames Prognostics Data Repository (https://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/), 2007.