An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles

Control Engineering Practice

Volumn 54, Issue , 2016, Pages 81-90

  Du, Jiani ^a   Liu, Zhitao ^b   Wang, Youyi ^a   Wen, Changyun ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

Adaptive sliding mode observer; State of charge (SOC) estimation; State of health (SOH) estimation

Indexed keywords

CHARGING (BATTERIES); ELECTRIC BATTERIES; ELECTRIC POWER SYSTEMS; ELECTRIC VEHICLES; EQUIVALENT CIRCUITS; HEALTH; IONS; LITHIUM; LITHIUM ALLOYS; LITHIUM-ION BATTERIES; SECONDARY BATTERIES; SLIDING MODE CONTROL; UNCERTAINTY ANALYSIS;

ADAPTIVE SLIDING MODE OBSERVER; BATTERY STATE OF CHARGE; CHATTERING EFFECTS; EQUIVALENT CIRCUIT MODEL; ESTIMATION PERFORMANCE; ESTIMATION RESULTS; STATE OF CHARGE; STATE OF HEALTH;

BATTERY MANAGEMENT SYSTEMS;

EID: 84971595202     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2016.05.014     Document Type: Article

References (41)

1. Ahmed R., El Sayed M., Arasaratnam I., Jimi T., Habibi S. Reduced-order electrochemical model parameters identification and SOC estimation for healthy and aged Li-ion batteries Part I: parameterization model development for healthy batteries. Emerging and Selected Topics in Power Electronics, IEEE Journal of 2014, 2:659-677.
2. Andre D., Appel C., Soczka-Guth T., Sauer D.U. Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries. Journal of Power Sources 2013, 224:20-27.
3. Chen M., Rincon-Mora G.A. Accurate electrical battery model capable of predicting runtime and IV performance. Energy Conversion, IEEE Transactions on 2006, 21:504-511.
4. Chen X., Kano H. A new state observer for perspective systems. Automatic Control, IEEE Transactions on 2002, 47:658-663.
5. Chen X., Kano H. State observer for a class of nonlinear systems and its application to machine vision. Automatic Control, IEEE Transactions on 2004, 49:2085-2091.
6. Chen X., Shen W., Cao Z., Kapoor A. A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles. Journal of Power Sources 2014, 246:667-678.
7. Chiang Y.-H., Sean W.-Y., Ke J.-C. Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles. Journal of Power Sources 2011, 196:3921-3932.
8. Di Domenico D., Prada E., Creff Y. An adaptive strategy for Li-ion battery internal state estimation. Control Engineering Practice 2013, 21:1851-1859.
9. Drakunov, S.V. (1992). Sliding-mode observers based on equivalent control method. In: Proceedings of the 31st IEEE Conference on Decision and Control (pp. 2368-2369).
10. Edwards C., Spurgeon S. Sliding mode control: theory and applications 1998, CRC Press, Gunpowder Square, London.
11. Fang H., Wang Y., Sahinoglu Z., Wada T., Hara S. State of charge estimation for lithium-ion batteries: an adaptive approach. Control Engineering Practice 2014, 25:45-54.
12. Gao, M., Liu, Y., and He, Z. (2011). Battery state of charge online estimation based on particle filter. In: Proceedings of the 4th International Congress on Image and Signal Processing (CISP) (pp. 2233-2236).
13. Gholizadeh M., Salmasi F.R. Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model. Industrial Electronics, IEEE Transactions on 2014, 61:1335-1344.
14. Gomez J., Nelson R., Kalu E.E., Weatherspoon M.H., Zheng J.P. Equivalent circuit model parameters of a high-power Li-ion battery: thermal and state of charge effects. Journal of Power Sources 2011, 196:4826-4831.
15. He H., Xiong R., Zhang X., Sun F., Fan J. State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model. Vehicular Technology, IEEE Transactions on 2011, 60:1461-1469.
16. Hu C., Youn B.D., Chung J. A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation. Applied Energy 2012, 92:694-704.
17. Hu X., Li S., Peng H. A comparative study of equivalent circuit models for Li-ion batteries. Journal of Power Sources 2012, 198:359-367.
18. Jiani, D. Youyi, W. and Changyun, W. (2013). Li-ion battery SOC estimation using particle filter based on an equivalent circuit model. In: Proceedings of the 10th IEEE International Conference on Control and Automation (ICCA) (pp. 580-585).
19. Jossen A., Späth V., Döring H., Garche J. Reliable battery operation-a challenge for the battery management system. Journal of Power Sources 1999, 84:283-286.
20. Kim I.-S. Nonlinear state of charge estimator for hybrid electric vehicle battery. Power Electronics, IEEE Transactions on 2008, 23:2027-2034.
21. Kim I.-S. A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer. Power Electronics, IEEE Transactions on 2010, 25:1013-1022.
22. Kim J., Lee S., Cho B. Complementary cooperation algorithm based on DEKF combined with pattern recognition for SOC/capacity estimation and SOH prediction. Power Electronics, IEEE Transactions on 2012, 27:436-451.
23. Kim, J. Lee, S. and Cho, B. (2009). The state of charge estimation employing empirical parameters measurements for various temperatures. In: Proceedings of the 6th IEEE International Power Electronics and Motion Control Conference, 2009. IPEMC'09 (pp. 939-944).
24. Knauff, M., Dafis, C.J., Niebur, D., Kwatny, H. G., and Nwankpa, C. (2007). Simulink model for hybrid power system test-bed. In: Proceedings of the IEEE Electric Ship Technologies Symposium, ESTS'07 (pp. 421-427).
25. Landi M., Gross G. Measurement techniques for online battery state of health estimation in vehicle-to-grid applications. IEEE Transactions on Instrumentation and Measurement 2014, 63:1224-1234.
26. Lee S., Kim J., Lee J., Cho B. State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge. Journal of Power Sources 2008, 185:1367-1373.
27. Lin H.-T., Liang T.-J., Chen S.-M. Estimation of battery state of health using probabilistic neural network. Industrial Informatics, IEEE Transactions on 2013, 9:679-685.
28. Ng K.S., Moo C.-S., Chen Y.-P., Hsieh Y.-C. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Applied Energy 2009, 86:1506-1511.
29. Petzl M., Danzer M.A. Advancements in OCV measurement and analysis for Lithium-ion batteries. Energy Conversion, IEEE Transactions on 2013, 28:675-681.
30. Plett G.L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation. Journal of Power Sources 2004, 134:277-292.
31. Plett G.L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification. Journal of Power Sources 2004, 134:262-276.
32. Plett G.L. Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2: Simultaneous state and parameter estimation. Journal of Power Sources 2006, 161:1369-1384.
33. Prasad G.K., Rahn C.D. Model based identification of aging parameters in lithium ion batteries. Journal of Power Sources 2013, 232:79-85.
34. Seaman A., Dao T.-S., McPhee J. A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation. Journal of Power Sources 2014, 256:410-423.
35. Serrao, L., Onori, S., Sciarretta, A., Guezennec, Y., and Rizzoni, G. (2011). Optimal energy management of hybrid electric vehicles including battery aging. In: Proceedings of American Control Conference (ACC) (pp. 2125-2130).
36. Sheng H., Xiao J. Electric vehicle state of charge estimation: nonlinear correlation and fuzzy support vector machine. Journal of Power Sources 2015, 281:131-137.
37. Sitterly M., Wang L.Y., Yin G.G., Wang C. Enhanced identification of battery models for real-time battery management. Sustainable Energy, IEEE Transactions on 2011, 2:300-308.
38. Utkin V.I. Sliding modes in control and optimization 1992, vol. 116. Springer, Berlin, Heidelberg.
39. 4 batteries. Applied Energy 2014, 135:81-87.
40. Xi J., Li M., Xu M. Optimal energy management strategy for battery powered electric vehicles. Applied Energy 2014, 134:332-341.
41. Zhang F., Liu G., Fang L., Wang H. Estimation of battery state of charge with observer: applied to a robot for inspecting power transmission lines. Industrial Electronics, IEEE Transactions on 2012, 59:1086-1095.