Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Source abstract
Abstract
Battery management systems in hybrid-electric-vehicle battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state-of-charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack. In a series of three papers, we propose methods, based on extended Kalman filtering (EKF), that are able to accomplish these goals for a lithium ion polymer battery pack. We expect that they will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results. This third paper concludes the series by presenting five additional applications where either an EKF or results from EKF may be used in typical BMS algorithms: initializing state estimates after the vehicle has been idle for some time; estimating state-of-charge with dynamic error bounds on the estimate; estimating pack available dis/charge power; tracking changing pack parameters (including power fade and capacity fade) as the pack ages, and therefore providing a quantitative estimate of state-of-health; and determining which cells must be equalized. Results from pack tests are presented.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Battery Technologies Research · IoT-based Smart Home Systems · Energy Efficient Wireless Sensor Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (34)
Show source-provided references
1. G. Plett, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs, Part 1, Background, J. Power Sour. 134 (2) (2004) 252–261.10.1016/j.jpowsour.2004.02.031 ↗
2. G. Plett, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs, Part 2, Modeling and identification, J. Power Sour. 134 (2) (2004) 262–276.10.1016/j.jpowsour.2004.02.032 ↗
3. Current state-of-health [battery monitoring]
4. D. Baert, A. Vervaet, Determination of the state-of-health of vrla batteries by means of noise measurements, in: Proceedings of the 23rd International Telecommunications Energy Conference, INTELEC 2001, IEE, London, UK, 2001, pp. 301–306.10.1049/cp:20010614 ↗
5. M. Ignatov, B. Monahov, Some problems of assessing the state-of-health of lead-acid batteries during operation, in: Proceedings of the First International Telecommunications Energy Conference, VDE, Frankfurt/Main, Germany, 1994, pp. 105–111.10.1109/TELESC.1994.4794321 ↗
6. A. Green, Investigations into methods of measuring the state-of-health of a nickel–cadmium industrial battery, in: Standby Batteries; Balancing Reliability and Monitoring with Costs. Conference Volume, ERA Technol., Leatherhead, UK, 1998, pp. 2.3.1–2.3.7.
7. C. Bose, F. Laman, Battery state-of-health estimation through coup de fouet, in: Proceedings of the International IEEE Telecommunications Energy Conference, IEEE Power Electronics Society, IEEE, Piscataway, NJ, 2000, pp. 597–601.10.1109/INTLEC.2000.884309 ↗
8. C. Bose, D. Wilkins, S. McCluer, M. Model, Lessons learned in using ohmic techniques for battery monitoring, in: Proceedings of the 16th Annual Battery Conference on Applications and Advances, IEEE, Piscataway, NJ, 2001, pp. 99–104.10.1109/BCAA.2001.905107 ↗
9. Conductance testing compared to traditional methods of evaluating the capacity of valve-regulated lead/acid batteries and predicting state-of-health10.1016/0378-7753(92)80056-H ↗
10. D. Feder, T. Croda, K. Champlin, M. Hlavac, Field and laboratory studies to assess the state-of-health of valve-regulated lead acid batteries, I, Conductance/capacity correlation studies, in: Proceedings of the 14th International Telecommunications Energy Conference, INTELEC’92, IEEE, New York, NY, 1992, pp. 218–233.10.1109/INTLEC.1992.268438 ↗
11. Evaluating the state-of-health of flooded and valve-regulated lead/acid batteries: a comparison of conductance testing with traditional methods10.1016/0378-7753(93)90035-Y ↗
12. D. Feder, M. Hlavac, Analysis and interpretation of conductance measurements used to assess the state-of-health of valve regulated lead acid batteries, in: Proceedings of the 16th International Telecommunications Energy Conference, INTELEC’94, IEEE, New York, NY, 1994, pp. 282–291.10.1109/INTLEC.1994.396621 ↗
13. Updated status of conductance/capacity correlation studies to determine the state-of-health of automotive and stand-by lead/acid batteries10.1016/0378-7753(94)80014-6 ↗
14. A. Waters, K. Bullock, C. Bose, Monitoring the state-of-health of vrla batteries through ohmic measurements, in: Proceedings of the 19th International Telecommunications Energy Conference, INTELEC’97, IEEE, New York, NY, 1997, pp. 675–680.10.1109/INTLEC.1997.646069 ↗
15. A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries10.1016/S0378-7753(97)02665-7 ↗
16. P. Singh, D. Reisner, Fuzzy logic-based state-of-health determination of lead acid batteries, in: Proceedings of the International Telecommunications Energy Conference, INTELEC 2002, IEEE, Piscataway, NJ, 2002, pp. 583–590.10.1109/INTLEC.2002.1048716 ↗
17. I. Damlund, Analysis and interpretation of ac-measurements on batteries used to assess state-of-health and capacity-condition, in: Proceedings of the 17th International Telecommunications Energy Conference, INTELEC’95, IEEE, New York, NY, 1995, pp. 828–833.10.1109/INTLEC.1995.499055 ↗
18. M. Hlavac, D. Feder, T. Croda, K. Champlin, Field and laboratory studies to assess the state-of-health of valve-regulated lead acid and other battery technologies using conductance testing, in: Proceedings of the 15th International Telecommunications Energy Conference, INTELEC’93, vol. 2, IEEE, New York, NY, 1993, pp. 375–383.10.1109/INTLEC.1993.388542 ↗
19. M. Hlavac, Field application of conductance measurements used to ascertain cell/battery and inter-cell connection state-of-health in electric power utility applications, in: Proceedings of the American Power Conference, Illinois Inst. Technol., vol. 1, Chicago, IL, 1993, pp. 44–57.
20. I. Buchmann, Artificial intelligence reads battery state-of-health in three minutes, in: Proceedings of the 16th Annual Battery Conference on Applications and Advances, IEEE, Piscataway, NJ, 2001, pp. 263–265.10.1109/BCAA.2001.905135 ↗
21. R. Robinson, On-line battery testing: a reliable method for determining battery health? in: Proceedings of the INTELEC’96 International Telecommunications Energy Conference, IEEE Power Electronics Society, IEEE, New York, NY, 1996, pp. 654–661.10.1109/INTLEC.1996.573405 ↗
22. D. Cox, R. Perez-Kite, Battery state-of-health monitoring, combining conductance technology with other measurement parameters for real-time battery performance analysis, in: Proceedings of the International IEEE Telecommunications Energy Conference, IEEE Power Electronics Society, IEEE, Piscataway, NJ, 2000, pp. 342–347.10.1109/INTLEC.2000.884272 ↗
23. Vehicle electric power systems are under change! Implications for design, monitoring and management of automotive batteries10.1016/S0378-7753(00)00607-8 ↗
24. T. Suntio, Imperfectness as a useful approach in battery monitoring, in: Proceedings of the 16th International Telecommunications Energy Conference, INTELEC’94, IEEE, New York, NY, 1994, pp. 481–485.10.1109/INTLEC.1994.396625 ↗
25. V. Spath, A. Jossen, H. Doring, J. Garche, The detection of the state-of-health of lead-acid batteries, in: Proceedings of the 19th International Telecommunications Energy Conference, INTELEC’97, IEEE, New York, NY, 1997, pp. 681–686.10.1109/INTLEC.1997.646070 ↗
26. A. Anbuky, P. Pascoe, P. Hunter, Knowledge based VRLA battery monitoring and health assessment, in: Proceedings of the International IEEE Telecommunications Energy Conference, IEEE Power Electronics Society, IEEE, Piscataway, NJ, 2000, pp. 687–694.10.1109/INTLEC.2000.884323 ↗
27. E. Wan, A. Nelson, Dual extended Kalman filter methods, in: S. Haykin (Ed.), Kalman Filtering and Neural Networks, Wiley/Inter-Science, New York, 2001, pp. 123–174.10.1002/0471221546.ch5 ↗
28. CTTS Vehicle Systems Analysis Homepage, accessed on May 2003. http://www.ctts.nrel.gov/analysis/.
29. G. Plett, High-performance battery-pack power estimation using a dynamic cell model, IEEE Trans. Veh. Technol., submitted for publication.
30. U.S. Dept of Energy, Idaho National Engineering and Environmental Laboratory, PNGV Battery Test Manual, Revision 3, DOE/ID–10597, 2001.
31. S. Haykin, Kalman Filtering and Neural Networks, Wiley/Inter-Science, New York, 2001.10.1002/0471221546 ↗
32. S. Moore, P. Schneider, A review of cell equalization methods for lithium ion and lithium polymer battery systems, in: Proceedings of the SAE 2001 World Congress, Advanced Hybrid Vehicle Powertrains, SAE, Warrendale, PA, Detroit, MI, 2001, 2001-01-0959.10.4271/2001-01-0959 ↗
33. A new approach to linear filtering and prediction problems10.1115/1.3662552 ↗
34. The Seminal Kalman Filter Paper, 1960, accessed on 20 May 2003. http://www.cs.unc.edu/∼welch/kalman/kalmanPaper.html.