Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Estimation of state of charge for lithium-ion batteries - A Review

Department of Electrical Engineering, University of Moratuwa, Moratuwa, Sri Lanka

The State Of Charge (SOC) is the most important index in a Battery ManagementSystem (BMS) to regulate charge/discharge decisions and to ensure the battery’s safety,efficiency, and longevity. There are many methods to estimate SOC of a battery and the model based-methods exhibit higher accuracy compared to other methods. Among them the EquivalentCircuit Model (ECM)-based methods are employed in power system applications due to theirflexible nature. These models consist of a voltage source to represent Open Circuit Voltage(OCV) which depends on the SOC of the battery. The accuracy of the SOC estimation highlydepends on the adopted Equivalent Circuit Model. To accomplish accurate battery model,battery SOC should be precisely estimated. This paper investigates various types of SOCestimation methods for lithium-ion batteries in-depth in view point of Battery Energy StorageSystems (BESS). Different SOC estimation methods are compared and evaluated to assess theirsuitability under both static response and dynamic conditions.
  Article Metrics


1. Chen B, Ma H, Fang H, et al. (2014) An approach for state of charge estimation of li- ion battery based on thevenin equivalent circuit model. Prognostics and System Health Management Conference (PHM-2014 Hunan) 647–652.

2. Ng KS, Moo CS, Chen YP, et al. (2008) State-of-charge estimation for lead-acid batteries based on dynamic open-circuit voltage. Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International 972–976.

3. Chang WY (2013) The state of charge estimating methods for battery: A review. ISRN Appl Math 2013.

4. Xiong R, Cao J, Yu Q, et al.(2018) Critical review on the battery state of charge estimation methods for electric vehicles. Ieee Access 6: 1832–1843.

5. Ren H, Zhao Y, Chen S, et al. (2019) Design and implementation of a battery management system with active charge balance based on the soc and soh online estimation. Energy 166: 908–917.    

6. Gundogdu B, Gladwin D, Foster M, et al. (2018) A forecasting battery state of charge management strategy for frequency response in the uk system. 2018 IEEE International Conference on Industrial Technology (ICIT) 1726–1731.

7. Coleman M, Lee CK, Zhu C, et al. (2007) State-of-charge determination from emf voltage estimation: Using impedance, terminal voltage, and current for lead-acid and lithium-ion batteries. IEEE Trans Ind Electron 54: 2550–2557.    

8. Awadallah MA, Venkatesh B (2016) Accuracy improvement of soc estimation in lithium-ion batteries. J Energy Storage 6: 95–104.    

9. Chun CY, Baek J, Seo GS, et al. (2015) Current sensor-less state-of-charge estimation algorithm for lithium-ion batteries utilizing filtered terminal voltage. J Power Sources 273: 255–263.    

10. Larsson F, Andersson P, Blomqvist P, et al. (2014) Characteristics of lithium-ion batteries during fire tests. J Power Sources 271: 414–420.    

11. Zubi G, Dufo-López R, Carvalho M, et al. (2018) The lithium-ion battery: State of the art and future perspectives Renew Sust Energy Rev 89: 292–308.

12. He H, Xiong R, Zhang X, et al. (2011) State-of-charge estimation of the lithium-ion battery using an adaptive extended kalman filter based on an improved thevenin model.IEEE Trans Veh Technol 60: 1461–1469.    

13. Sarasua AE, Molina MG, Mercado PE (2013) Dynamic modelling of advanced battery energy storage system for grid-tied ac microgrid applications. Energy Storage: Technol Appl.

14. Moura SJ, Argomedo FB, Klein R, et al. (2017) Battery state estimation for a single particle model with electrolyte dynamics. IEEE Trans Control Syst Technol 25: 453–468.    

15. Moura SJ, Krstic M, Chaturvedi NA (2012) Adaptive pde observer for battery soc/soh estimation. ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference 101–110.

16. Tsang K, Chan W, Wong Y, et al. (2010) Lithium-ion battery models for computer simulation. 2010 IEEE International Conference on Automation and Logistics 98–102.

17. Chen M, Rincon-Mora GA (2006) Accurate electrical battery model capable of predicting runtime and iv performance IEEE Trans Energy Convers 21: 504–511.

18. Dey S, Ayalew B, Pisu B (2014) Adaptive observer design for a li-ion cell based on coupled electrochemical-thermal model. ASME 2014 Dynamic Systems and Control Conference V002T23A001–V002T23A001.

19. Satadru D, Beshah A, Pierluigi P(2015) Nonlinear robust observers for state-of-charge estimation of lithium-ion cells based on a reduced electrochemical model. IEEE Trans Control Syst Technol 23: 1935–1942.

20. Plett GL (2004) Extended kalman filtering for battery management systems of lipb-based hev battery packs: Part 3. state and parameter estimation. J Power sources 134: 277–292.    

21. Zhang L, Peng H, Ning Z, et al. (2017)Comparative research on rc equivalent circuit models for lithium-ion batteries of electric vehicles. Appl Sci 7: 1002.

22. Van den Bossche P, Omar N, Al Sakka M, et al. (2014) The challenge of phev battery design and the opportunities of electrothermal modeling. Lithium-Ion Batteries 2014: 249–271.

23. Rivera-Barrera JP, Muñoz-Galeano N, Sarmiento-Maldonado HO (2017) Soc estimation for lithium-ion batteries: review and future challenges. Electronics 6: 102.    

24. Xie B, Liu Y, Ji Y, et al. (2018) Two-stage battery energy storage system (bess) in ac microgrids with balanced state-of-charge and guaranteed small-signal stability. Energies 11: 322.    

25. Tang X, Liu B, Gao F (2017) State of charge estimation of lifepo4 battery based on a gain-classifier observer. Energy Procedia 105: 2071–2076.    

26. Hussein AA (2014) Kalman filters versus neural networks in battery state-of-charge estimation: A comparative study. Int J Mod Nonlinear Theory Appl 3: 199.    

27. Barai A, Widanage WD, Marco J, et al. (2015) A study of the open circuit voltage characterization technique and hysteresis assessment of lithium-ion cells. J Power Sources 295: 99–107.    

28. Xiong R, Yu Q, Wang LY (2017) Open circuit voltage and state of charge online estimation for lithium ion batteries. Energy Procedia 142: 1902–1907.    

29. Lee S, Kim J, Lee J, et al. (2008) State-of-charge and capacity estimation of lithium- ion battery using a new open-circuit voltage versus state-of-charge. J power sources 185: 1367–1373.    

30. Meng J, Luo G, Ricco M, et al. (2018) Overview of lithium-ion battery modeling methods for state-of-charge estimation in electrical vehicles. Appl Sci 8: 659.    

31. Tan Y, Mao J, Tseng K (2011) Modelling of battery temperature effect on electrical characteristics of li-ion battery in hybrid electric vehicle. 2011 IEEE Ninth International Conference on Power Electronics and Drive Systems 637–642.

32. Cheng Z, Wang L, Liu J, et al. (2016) Estimation of state of charge of lithium-ion battery based on photovoltaic generation energy storage system. Tehnički vjesnik 23: 695–700.

33. Weng C, Sun J, Peng F (2013) An open-circuit-voltage model of lithium-ion batteries for effective incremental capacity analysis. ASME 2013 dynamic systems and controlconference. 2013: V001T05A002–V001T05A002.

34. Charkhgard M, Farrokhi M (2010) State-of-charge estimation for lithium-ion batteries using neural networks and ekf. IEEE Trans Ind electron 57: 4178–4187.    

35. Ma Y, Zhou X, Li B, et al. (2016) Fractional modeling and soc estimation of lithium-ion battery. IEEE/CAA Journal of Auto Sin 3: 281–287.    

36. Ng KS, Moo CS, Chen YP, et al. (2009) Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl energy 86: 1506–1511.    

37. Lipu MH, Hannan MA, Hussain A, et al. (2018) State of charge estimation for lithium-ion battery using recurrent narx neural network model based lighting search algorithm. IEEE Access 2018.

38. Hansen T, Wang CJ (2005) Support vector based battery state of charge estimator. J Power Sources 141: 351–358.    

39. Chang MH, Huang HP, Chang SW (2013) A new state of charge estimation method for lifepo4 battery packs used in robots. Energies 6: 2007–2030.    

40. Tudoroiu RE, Zaheeruddin M, Radu SM, et al. (2018) Real-time implementation of an extended kalman filter and a pi observer for state estimation of rechargeable li-ion batteries in hybrid electric vehicle applications-a case study. Batteries 4: 19.    

41. Linghu J, Kang L, Liu M, et al. (2018) State of charge estimation for ternary battery in electric vehicles using spherical simplex-radial cubature kalman filter. 2018 International Conference on Power System Technology 1586–1592.

42. Wu TH, Moo CS (2017) State-of-charge estimation with state-of-health calibration for lithium-ion batteries. Energies 10: 987.    

43. Li Y, Zou C, Berecibar M, et al. (2018) Random forest regression for online capacity estimation of lithium-ion batteries. Appl Energy 23: 197–210.

44. Yan Q, Wang Y (2017) Predicting for power battery soc based on neural network. Control Conference (CCC), 2017 36th Chinese 4140–4143.

45. He W, Williard N, Chen C, et al. (2014) State of charge estimation for li-ion batteries using neural network modeling and unscented kalman filter-based error cancellation. Int J Electr Power Energy Syst 62: 783–791.    

46. Yu Z, Huai R, Xiao L (2015) State-of-charge estimation for lithium-ion batteries using a kalman filter based on local linearization. Energies 8: 7854–7873.    

47. Lipu MH, Hussain A, Saad M, et al. (2018) Improved recurrent narx neural network model for state of charge estimation of lithium-ion battery using pso algorithm. 2018 IEEE Symposium on Computer Applications & Industrial Electronics 354–359.

48. Lv J, Yuan H, Lv Y (2012) Battery state-of-charge estimation based on fuzzy neural network and improved particle swarm optimization algorithm. 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control 22–27.

49. Yu DX, Gao YX(2013) Soc estimation of lithium-ion battery based on kalman filter algorithm. Appl Mech Mater 347: 1852–1855.

50. Tingting D, Jun L, Fuquan Z, et al. (2011) Analysis on the influence of measurement error on state of charge estimation of lifepo4 power battery. 2011 International Conference on Materials for Renewable Energy Environment 1: 644–649.

51. He Z, Gao M, Wang C, et al. (2013) Adaptive state of charge estimation for li-ion batteries based on an unscented kalman filter with an enhanced battery model. Energies 6: 4134– 4151.    

52. Di¸ s¸ ci FN, El-Kahlout Y, Balık¸ cı A (2017) Li-ion battery modeling and soc estimation using extended kalman filter. 2017 10th International Conference on Electrical and Electronics Engineering 166–169.

53. Huria T, Ceraolo M, Gazzarri J, et al. (2012) High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells. 2012 IEEE International Electric Vehicle Conference 1–8.

54. Huria T, Ceraolo M, Gazzarri J, et al. (2013) Simplified extended kalman filter observer for soc estimation of commercial power-oriented lfp lithium battery cells. SAE Technical Paper, Tech. Rep., 2013.

55. Plett GL (2003) Advances in ekf soc estimation for lipb hev battery packs. Consultant to Compact Power, Inc.

56. Lu J, Chen Z, Yang Y, et al. (2018) Online estimation of state of power for lithium-ion batteries in electric vehicles using genetic algorithm. IEEE Access 6: 868–880.

57. He W, Williard N, Chen C, et al. (2013) State of charge estimation for electric vehicle batteries using unscented kalman filtering. Microelectron Reliab 53: 840–847.    

58. Ma Y, Duan P, Sun Y, et al. (2018) Equalization of lithium-ion battery pack based on fuzzy logic control in electric vehicle. IEEE Trans Ind Electron 65: 6762–6771.    

59. Gan L, Yang F, Shi Y, et al. (2017) Lithium-ion battery state of function estimation based on fuzzy logic algorithm with associated variables. IOP Conference Series: Earth and Environmental Science 94: 012133.    

60. Wu T, Wang M, Xiao Q, et al. (2012) The soc estimation of power li-ion battery based on anfis model. Smart Grid Renewable Energy 3: 51.    

61. Singh P, Reisner D (2002) Fuzzy logic-based state-of-health determination of lead acid batteries. 24th Annual International Telecommunications Energy Conference 2002: 583– 590.

62. Singh P, Fennie C, Reisner D (2004) Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries. J Power Sources 136: 322–333.    

63. Salkind AJ, Fennie C, Singh P, et al. (1999) Determination of state-of-charge and state-of- health of batteries by fuzzy logic methodology. J Power sources 80: 293–300.    

64. Anton JA, Nieto PG, Viejo CB, et al. (2013) Support vector machines used to estimate the battery state of charge. IEEE Trans. Power Electron 28: 5919–5926.    

65. Guo GF, Shui L, Wu XL, et al. (2014) Soc estimation for li-ion battery using svm based on particle swarm optimization. Adv Mater Res 1051: 1004–1008.    

66. Pattipati B, Sankavaram C, Pattipati K (2011) System identification and estimation framework for pivotal automotive battery management system characteristics. IEEE Trans Syst Man Cybern Part C Appl Rev 41: 869–884.    

67. de Matos JG, e Silva FS, Ribeiro LAdS (2015) Power control in ac isolated microgrids with renewable energy sources and energy storage systems. IEEE Trans Ind Electron 62: 3490–3498.    

68. Jun B, Wang YX, Zhao XM (2017) State of charge estimation for electric vehicle batteries based on a particle filter algorithm. DEStech Trans Compu Sci Eng.

69. Li B, Peng K, Li G (2018) State-of-charge estimation for lithium-ion battery using the gauss-hermite particle filter technique. J Renewable Sustainable Energy 10: 014105.    

70. Bi J, Gao H, Wang Y, et al. (2017) Estimation of state-of-charge of li-ion batteries in ev using the genetic particle filter. IOP Conference Series: Earth and Environmental Science 81: 012183.    

71. Xia B, Sun Z, Zhang R, et al. (2017) A cubature particle filter algorithm to estimate the state of the charge of lithium-ion batteries based on a second-order equivalent circuit model. Energies 10: 457.    

72. Jiani D, Youyi W, Changyun W (2013) Li-ion battery soc estimation using particle filter based on an equivalent circuit model. 2013 10th IEEE International Conference on Control and Automation (ICCA) 2013: 580–585.

73. Xia B, Sun Z, Zhang R, et al. (2017) A comparative study of three improved algorithms based on particle filter algorithms in soc estimation of lithium ion batteries. Energies 10: 1149.    

74. Lipu MH, Hannan M, Hussain A, et al. (2018) A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations. J Cleaner Prod 2018.

75. Deng Y, Hu Y, Cao Y (2014) An improved algorithm of soc testing based on open- circuit voltage-ampere hour method. International Conference on Life System Modeling and Simulation and International Conference on Intelligent Computing for Sustainable Energy and Environment 258–267.

76. Murnane M, Ghazel A (2017) A closer look at state of charge (soc) and state of health (soh) estimation techniques for batteries. Available from: http://www. analog. com/media/en/technical-documentation/technicalarticles/A-Closer-Look-at-State- Of-Charge-and-State-Health-Estimation-Techniques-.... pdf.

77. Leksono E, Haq IN, Iqbal M, et al. (2013) State of charge (soc) estimation on lifepo4 battery module using coulomb counting methods with modified peukert. 2013 Joint International Conference on Rural Information Communication Technology and Electric- Vehicle Technology (rICT & ICeV-T) 2013.

78. Jeong YM, Cho YK, Ahn JH, et al. (2014) Enhanced coulomb counting method with adaptive soc reset time for estimating ocv. 2014 IEEE Energy Conversion Congress and Exposition 1313–1318.

79. Wu J, Zhang C, Chen Z (2016) An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Appl energy 173: 134–140.    

80. Liu F, Liu T, Fu Y (2015) An improved soc estimation algorithm based on artificial neural network. 2015 8th International Symposium on Computational Intelligence and Design(ISCID) 2: 152–155.

81. Wang L, Savvaris A, Tsourdos A (2018) Online battery pack state of charge estimation via ekf-fuzzy logic joint method. 2018 5th International Conference on Control, Decision and Information Technologies 899–904.

82. Zenati A, Desprez P, Razik H, et al. (2012) A methodology to assess the state of health of lithium-ion batteries based on the battery's parameters and a fuzzy logic system. 2012 IEEE International Electric Vehicle Conference (IEVC). 2012: 1–6.

83. Khayat N, Karami N (2016) Adaptive techniques used for lifetime estimation of lithium- ion batteries. 2016 Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA). 2016: 98–103.

84. Zhang N, Liu K (2011) The prediction of soc based on multiple dimensioned support vector machine. 2011 Second International Conference on Mechanic Automation and Control Engineering (MACE). 2011: 1786–1788.

85. Zhao Q, Qin X, Zhao H, et al. (2018) A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries. Microelectron Reliab 85: 99–108.    

86. Deng Z, Yang L, Cai Y, et al. (2017) Maximum available capacity and energy estimation based on support vector machine regression for lithium-ion battery. Energy Procedia 107: 68–75.    

87. Rahul K, Ramprabhakar J, Shankar S (2017) Comparative study on modeling and estimation of state of charge in battery. 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon). 2017: 1610–1615.

88. Xing Y, He W, Pecht M, et al. (2014) State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures. Appl Energy 113: 106–115.    

89. Yang D, Liu J, Wang Y, et al. (2014) State-of-charge estimation using a self-adaptive noise extended kalman filter for lithium batteries. 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). 2014: 1–5.

90. Liu Z, Wang Y, Du J, et al. (2012) Rbf network-aided adaptive unscented kalman filter for lithium-ion battery soc estimation in electric vehicles. 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA). 2012: 1673–1677.

91. Yang Y, Cui N, Wang C,et al. (2017) Soc estimation of lithium-ion battery based on new adaptive fading extended kalman filter. Chinese Automation Congress (CAC). 2017: 5630– 5634.

92. Sangwan V, Kumar R, Rathore AK (2017) State-of-charge estimation for li-ion battery using extended kalman filter (ekf) and central difference kalman filter (cdkf). 2017 IEEE Industry Applications Society Annual Meeting 2017: 1–6.

93. Zhang S, Sun H, Lyu C (2018) A method of soc estimation for power li-ion batteries based on equivalent circuit model and extended kalman filter. 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) 2018: 2683–2687.

94. Topan PA, Ramadan MN, Fathoni G, et al. (2016) State of charge (soc) and state of health (soh) estimation on lithium polymer battery via kalman filter. International Conference on Science and Technology-Computer (ICST) 2016: 93–96.

95. Jiang C, Taylor A, Duan C, et al. (2013) Extended kalman filter based battery state of charge (soc) estimation for electric vehicles. 2013 IEEE Transportation Electrification Conference and Expo (ITEC) 2013: 1–5.

96. Farrokhabadi M, König S, Cañizares CA, et al. (2018) Battery energy storage system models for microgrid stability analysis and dynamic simulation. IEEE Trans Power Syst 33: 2301– 2312.    

97. Thale SS, Wandhare RG, Agarwal V (2015) A novel reconfigurable microgrid architecture with renewable energy sources and storage. IEEE Trans Ind Appl 51: 1805–1816.    

98. Weng C, Sun J, Peng H (2014) A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring. J power Sources 258: 228– 237.    

99. Zhao S, Duncan SR, Howey DA (2017) Observability analysis and state estimation of lithium-ion batteries in the presence of sensor biases. IEEE Trans Control Syst Technol 25: 326–333.    

100. Samba A (2015) Battery electrical vehicles-analysis of thermal modelling and thermal management. LUSAC (Laboratoire Universitaire des Sciences Appliquées de Cherbourg), Université de caen Basse Normandie; MOBI (the Mobility, Logistics and Automotive Technology Research Centre), Vrije Universiteit Brussel.

101. Tang X, Gao F, Zou C, et al. (2019) Load-responsive model switching estimation for state of charge of lithium-ion batteries. Appl Energy 238: 423–434.    

102. Zou C, Manzie C, Neˇ si´ c D, et al. (2016) Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery. J Power Sources 335: 121–130.    

103. Baghdadi I, Briat O, Eddahech A, et al. (2015) Electro-thermal model of lithium-ion batteries for electrified vehicles applications. 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE). 2015: 1248–1252.

104. Pesaran AA (2002) Battery thermal models for hybrid vehicle simulations. J power sources 110: 377–382.    

105. Forgez C, Do DV, Friedrich G, et al. (2010) Thermal modeling of a cylindrical lifepo4/graphite lithium-ion battery. J Power Sources 195: 2961–2968.    

106. Gao Z, Chin CS, Woo WL, et al. (2017) Integrated equivalent circuit and thermal model for simulation of temperature-dependent lifepo4 battery in actual embedded application. Energies 10: 85.    

107. Peng S, Chen C, Shi H, et al. (2017) State of charge estimation of battery energy storage systems based on adaptive unscented kalman filter with a noise statistics estimator. IEEE Access 5: 202–212.

108. Zhou Y, Li X (2015) Overview of lithium-ion battery soc estimation. 2015 IEEE International Conference on Information and Automation 2015: 2454–2459.

109. Fayazi A, Arabloo M, Shokrollahi A, et al. (2013) State-of-the-art least square support vector machine application for accurate determination of natural gas viscosity. Ind Eng Chem Res 53: 945–958.

110. Xu J, Li S, Cao B (2017) A novel current disturbance estimation method for battery management systems in electric vehicle. Energy Procedia 105: 2837–2842.    

111. Ciortea F, Rusu C, Nemes M, et al. (2017) Extended kalman filter for state-of-charge estimation in electric vehicles battery packs. Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP), 2017 International Conference on. 2017: 611–616.

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved