DOI QR코드

DOI QR Code

Review of state-of-the-art battery state estimation technologies for battery management systems of stationary energy storage systems

  • Park, Seongyun (Department of Electrical Engineering, Chungnam National University) ;
  • Ahn, Jeongho (Department of Electrical Engineering, Chungnam National University) ;
  • Kang, Taewoo (Department of Electrical Engineering, Chungnam National University) ;
  • Park, Sungbeak (Department of Nuclear Safety Research, Korea Institute of Nuclear Safety) ;
  • Kim, Youngmi (Department of Nuclear Safety Research, Korea Institute of Nuclear Safety) ;
  • Cho, Inho (Propulsion System Research Team, Smart Electrical and Signaling Division, Korea Railroad Research Institute) ;
  • Kim, Jonghoon (Department of Electrical Engineering, Chungnam National University)
  • Received : 2020.03.24
  • Accepted : 2020.07.13
  • Published : 2020.11.20

Abstract

Lithium-ion batteries have recently been in the spotlight as the main energy source for the energy storage devices used in the renewable energy industry. The main issues in the use of lithium-ion batteries are satisfaction with the design life and safe operation. Therefore, battery management has been required in practice. In accordance with this demand, battery state indicators such as the state-of-charge (SOC), state-of-health (SOH), state-of-function (SOF), and state-of-temperature (SOT) have been widely applied. The use of these indicators ensures safe operation without overcharging and over-discharging. In addition, it can also help satisfy the design life. This paper presents a literature review of battery state indicators over the last three years and proposes the requirement of state-of-the-art battery state indicators. It also suggests future developments for battery management system (BMS) in stationary energy storage systems (ESSs).

Keywords

Acknowledgement

This work was supported by the Nuclear Safety Research Program through the Korea Foundation Of Nuclear Safety (KoFONS), granted financial resource from the Nuclear Safety and Security Commission (NSSC), Republic of Korea. (No. 1805006) and a Grant (20TLRP-C135446-01, Development of Hybrid Electric Vehicle Conversion Kit for Diesel Delivery Trucks and its Commercialization for Parcel Services) from the Transportation & Logistics Research Program (TLRP) funded by the Ministry of Land, Infrastructure, and Transportation of the Korean government.

References

  1. Intergovernmental Panel on Climate Change: Climate Change 2014: mitigation of climate change: working group III contribution to the IPCC fifth assessment report. Cambridge University Press, Cambridge (2015)
  2. Hao, H., Liu, Z., Zhao, F., Geng, Y., Sarkis, J.: Material flow analysis of lithium in China. Resour. Policy 51, 100-106 (2017) https://doi.org/10.1016/j.resourpol.2016.12.005
  3. Datta, U., Kalam, A., Shi, J.: Battery energy storage system control for mitigating PV penetration impact on primary frequency control and state-of-charge recovery. IEEE Trans. Sustain. Energy 11, 746-757 (2019) https://doi.org/10.1109/tste.2019.2904722
  4. Barre, A., Deguilhem, B.: A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. J. Power Sources 241, 680-689 (2013) https://doi.org/10.1016/j.jpowsour.2013.05.040
  5. Belmonte, N., Luetoo, C., Staulo, S., Rizzi, P., Baricco, M.: Case Studies of Energy Storage with Fuel Cells and Batteries for Stationary and Mobile Application. Challenges 8(1), 9 (2017) https://doi.org/10.3390/challe8010009
  6. BENF, Energy Storage Outlook 2019 (2019)
  7. Hong, J.H., Kim, J.T., Son, W.I.: Long-term energy strategy scenarios for south Korea: transition to a sustainable energy system. Energy Policy 127, 425-437 (2019) https://doi.org/10.1016/j.enpol.2018.11.055
  8. Kim, J.H.: ESS Fire accident and investigate cause, Publishing Ministry of Trade, Industry and Energy. https://www.motie.go.kr/motie/ne/presse/press2/bbs/bbsView.do?bbs_cd_n=81&bbs_seq_n=161771 (2019). Accessed June 2019
  9. Li, Z., Huang, J., Liwa, B., Zhang, J.: On state-of-charge determination for lithium-ion batteries. J. Power Sources 348, 281-301 (2017) https://doi.org/10.1016/j.jpowsour.2017.03.001
  10. Xiong, R., Li, L., Tian, J.: Towards a smarter battery management system: a critical review on battery state of health monitoring methods. J. Power Sources 405, 18-29 (2018) https://doi.org/10.1016/j.jpowsour.2018.10.019
  11. Du, J., Liu, Z., Wang, Y., Wen, C.: An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles. Control Eng. Pract. 54, 81-90 (2016) https://doi.org/10.1016/j.conengprac.2016.05.014
  12. Hossain Lipu, M.S., Hannan, M.A., Hussain, A., Hoque, M.M.: A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: challenges and recommendations. J. Clean. Prod. 205, 115-133 (2018) https://doi.org/10.1016/j.jclepro.2018.09.065
  13. Perez, H.E., Hu, X., Dey, S., Moura, S.J.: Optimal charging of liion batteries with coupled electro-thermal-aging dynamics. IEEE Trans. Veh. Technol. 66(9), 7761-7770 (2017) https://doi.org/10.1109/TVT.2017.2676044
  14. Beelen, H.P.G.J., Raijmakers, L.H.J., Donkers, M.C.F., Notten, P.H.L., Bergveld, H.J.: A comparison and accuracy analysis of impedance-based temperature estimation methods for Li-ion batteries. Appl. Energy 175, 128-140 (2016) https://doi.org/10.1016/j.apenergy.2016.04.103
  15. Xia, G., Cao, L., Bi, G.: A review on battery thermal management in electric vehicle application. J. Power Sources 367, 90-105 (2017) https://doi.org/10.1016/j.jpowsour.2017.09.046
  16. Gao, Y., Jiang, J., Zhang, C., Zhaing, W., Ma, Z., Jiang, Y.: Lithium-ion battery aging mechanisms and life model under different charging stresses. J. Power Sources 365, 103-114 (2017)
  17. Klett, M., Eriksson, R., Groot, J., Svens, P., Hogstrom, K.C., Lindstrom, R.W., Berg, H., Gustafson, T., Lindberg, G., Edstrom, K.: Non-uniform aging of cycled commercial LiFePO4//graphite cylindrical cells revealed by post-mortem analysis. J. Power Sources 257, 126-137 (2014) https://doi.org/10.1016/j.jpowsour.2014.01.105
  18. Feng, X., Sun, J., Ouyang, M., He, X., Lu, L., Han, X., Fang, M., Peng, H.: Characterization of large format lithium ion battery exposed to extremely high temperature. J. Power Sources 272, 457-467 (2014) https://doi.org/10.1016/j.jpowsour.2014.08.094
  19. Petzl, M., Kasper, M., Danzer, M.A.: Lithium plaiting in commercial lithium-ion battery-a low temperature aging study. J. Power Sources 275, 799-807 (2015) https://doi.org/10.1016/j.jpowsour.2014.11.065
  20. Momma, T., Matsunaga, M., Mukoyama, D., Osaka, T.: Ac impedance analysis of lithium ion battery under temperature control. J. Power Sources 216, 304-307 (2012) https://doi.org/10.1016/j.jpowsour.2012.05.095
  21. Anton, J.C.A., Nieto, P.J.G., Juez, F.J.C., Lasheras, F.S., Vega, M.G., Gutierrez, M.N.R.: Battery state-of-charge estimator using the SVM technique. Appl. Math. Model. 37, 6244-6253 (2013) https://doi.org/10.1016/j.apm.2013.01.024
  22. Meng, J., Ricco, M., Luo, G., Swierczynski, M.: An overview and comparison of online implementable SOC estimation methods for lithium-ion battery. IEEE Trans. Ind. Appl. 54(2), 1583-1591 (2018) https://doi.org/10.1109/tia.2017.2775179
  23. Chaoui, H.: Lyapunov-based adaptive state of charge and state of health estimation for lithium-ion batteries. IEEE Trans. Ind. Electron 62(3), 1010-1018 (2015) https://doi.org/10.1109/TIE.2014.2341576
  24. Kim, J.H., Lee, S.J., Cho, B.H.: Complementary cooperation algorithm based on DEKF combined with pattern recognition for SOC/capacity estimation and SOH prediction. IEEE Trans. Power Electron. 27(1), 436-451 (2012) https://doi.org/10.1109/TPEL.2011.2158554
  25. Pilatowicz, G., Marongiu, A., Drillkens, J., Sinhuber, P., Sauer, D.U.: A critical overview of definitions and determination techniques of the internal resistance using lithium-ion, lead-acid, nickel metal-hydride batteries and electrochemical double-layer capacitors as example. J. Power Sources 296, 365-376 (2015) https://doi.org/10.1016/j.jpowsour.2015.07.073
  26. Han, X., Lu, L., Zheng, Y., Feng, X.: A review on the key issue of the lithium ion battery degradation among the whole life cycle. eTransportation 1, 100005 (2010) https://doi.org/10.1016/j.etran.2019.100005
  27. Li, Y., Liu, K., Aoife, M.F., Zulke, A.: Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review. Renew. Sustain. Energy Rev. 113, 109254 (2019) https://doi.org/10.1016/j.rser.2019.109254
  28. Lu, L., Han, X., Li, J., Hua, J., Ouyang, M.: A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sources 226, 272-288 (2013) https://doi.org/10.1016/j.jpowsour.2012.10.060
  29. Meissner, E., Richter, G.: Battery monitoring and electrical energy management precondition for future vehicles electric power systems. J. Power Sources 116, 79-98 (2003) https://doi.org/10.1016/S0378-7753(02)00713-9
  30. Plett, G.L.: Battery management systems, Volume II: Equivalent-circuit methods, 1st edn. Artech House, Norwood (2015)
  31. Fleischer, C., Waag, W., Heyn, H., Sauer, D.: On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit. J. Power Sources 262, 457-482 (2014) https://doi.org/10.1016/j.jpowsour.2014.03.046
  32. Wang, D., Yang, F., Gan, L., Li, Y.: Fuzzy prediction of power lithium ion battery state of function based on the fuzzy c-means clustering algorithm. World Electr. Veh. J. 10(1), 1 (2019) https://doi.org/10.3390/wevj10010001
  33. Abada, S., Marlair, G., Lecocq, A., Petit, M., Sauvant-Moynot, V., Huet, F.: Safety focused modeling of lithium-ion batteries: a review. J. Power Sources 306, 178-192 (2016) https://doi.org/10.1016/j.jpowsour.2015.11.100
  34. Kang, D.H., Lee, P.Y., Yoo, K.S., Kim, J.H.: Internal thermal network model-based inner temperature distribution of high-power lithium-ion battery packs with different shapes for thermal management. J. Energy Storage 27, 101017 (2020) https://doi.org/10.1016/j.est.2019.101017
  35. Yoo, K.S., Kim, J.H.: Thermal behavior of full-scale battery pack based on comprehensive heat-generation model. J. Power Sources 433, 226715 (2019) https://doi.org/10.1016/j.jpowsour.2019.226715
  36. Liao, Z., Zhang, S., Li, K., Zhang, G., Habetler, T.G.: A survey of methods for monitoring and detecting thermal runaway of lithium-ion batteries. J. Power Sources 436, 226879 (2019) https://doi.org/10.1016/j.jpowsour.2019.226879
  37. Bernardi, D., Powlikowski, E., Newman, J.: A general energy balance for battery systems. J. Electrochem. Soc. 132, 5-12 (1985) https://doi.org/10.1149/1.2113792
  38. Xie, Y., Shi, S., Tang, J., Wu, H., Yu, J.: Experimental and analytical study on heat generation characteristics of a lithium-ion power battery. Int. J. Heat Mass Transf. 122, 884-894 (2018) https://doi.org/10.1016/j.ijheatmasstransfer.2018.02.038
  39. Wang, Q., Jiang, B., Li, B., Yan, Y.: A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles. Renew. Sustain. Energy Rev. 64, 106-128 (2016) https://doi.org/10.1016/j.rser.2016.05.033
  40. Feng, X., Ouyang, M., Liu, X., Lu, L., Xia, Y., He, X.: Thermal runaway mechanism of lithium ion battery for electric vehicles: a review. Energy Storage Mater. 10, 246-267 (2018) https://doi.org/10.1016/j.ensm.2017.05.013
  41. Arora, S.: Selection of thermal management system for modular battery packs of electric vehicles: a review of existing and emerging technologies. J. Power Sources 400, 621-640 (2018) https://doi.org/10.1016/j.jpowsour.2018.08.020
  42. Sun, J., Wei, G., Pei, L., Lu, R., Song, K., Wu, C., Zhu, C.: Online internal temperature estimation for lithium-ion batteries based on Kalman filter. Energies 8, 4400-4415 (2015) https://doi.org/10.3390/en8054400
  43. Cuma, M.U., Koroglu, T.: A comprehensive review on estimation strategies used in hybrid and battery electric vehicles. Renew. Sustain. Energy Rev. 42, 517-531 (2015) https://doi.org/10.1016/j.rser.2014.10.047
  44. Lee, S.J., Kim, J.H., Lee, J.M., Cho, B.H.: 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 (2008) https://doi.org/10.1016/j.jpowsour.2008.08.103
  45. Kao, C., Chen, C., Tso, T.: Method of predicting remaining capacity and run-time of a battery device, US Patent 20110234167 (2011)
  46. Seaman, A., Dao, T., McPhee, J.: A survey of mathematics-based equivalent-circuit and electro-chemical battery models for hybrid and electric vehicles simulation. J. Power Sources 256, 410-423 (2014) https://doi.org/10.1016/j.jpowsour.2014.01.057
  47. Khan, K., Jafari, M., Gauchia, L.: Comparison of Li-ion battery equivalent circuit modelling using impedance analyzer and Bayesian network. IET Electr. Syst. Transp. 8(3), 197-204 (2018) https://doi.org/10.1049/iet-est.2017.0087
  48. Cho, S.W., Jeong, H.S., Han, C.H., Jin, S.S., Lim, J.H., Oh, J.K.: State-of-charge estimation for lithium-ion batteries under various operating conditions using and equivalent circuit model. Comput. Chem. Eng. 41, 1-9 (2012) https://doi.org/10.1016/j.compchemeng.2012.02.003
  49. Hu, X., Li, S., Peng, H.: A comparative study of equivalent circuit models for Li-ion batteries. J. Power Sources 198, 359-367 (2012) https://doi.org/10.1016/j.jpowsour.2011.10.013
  50. He, H., Xiong, R., Guo, H., Li, S.: Comparison study on the battery models used for the energy management of batteries in electric vehicles. Energy Convers. Manag. 64, 113-121 (2012) https://doi.org/10.1016/j.enconman.2012.04.014
  51. Hannan, M.A., Lipu, M.S.H., Hussain, A., Mohamed, A.: A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations. Renew. Sustain. Energy Rev. 78, 834-854 (2017) https://doi.org/10.1016/j.rser.2017.05.001
  52. Pattipati, B., Sankavaram, C., Pattipati, K.: System identification and estimation framework for pivotal automotive battery management system characteristics. IEEE Trans. Syst. Man Cybern. Part C 41, 869-884 (2011) https://doi.org/10.1109/TSMCC.2010.2089979
  53. Wu, J., Zhang, C., Chen, Z.: An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural network. Appl. Energy 173, 134-140 (2016) https://doi.org/10.1016/j.apenergy.2016.04.057
  54. Wu, J., Wang, Y., Zhang, X., Chen, Z.: A novel state of health estimation method of Li-ion battery using group method of data handling. J. Power Sources 327, 457-464 (2016) https://doi.org/10.1016/j.jpowsour.2016.07.065
  55. Chen, J., Ouyang, Q., Xu, C., Su, H.: Neural network-based sate of charge observer design for Lithium-Ion batteries. IEEE Trans. Control Syst. Technol. 26, 313-320 (2017) https://doi.org/10.1109/TCST.2017.2664726
  56. Ma, Y., Duan, P., Sun, Y., Chen, H.: Equalization of lithium-ion battery pack based on fuzzy logic control in electric vehicle. IEEE Trans. Ind. Electron. 65, 6762-6771 (2018) https://doi.org/10.1109/tie.2018.2795578
  57. Sheng, H., Xiao, J.: Electric vehicle state of charge estimation: nonlinear correlation and fuzzy support vector machine. J. Power Sources 281, 131-137 (2015) https://doi.org/10.1016/j.jpowsour.2015.01.145
  58. Alvarez Anton, J.C., Garacia Nieto, P.J., Blanco Viejo, C., Vilan, J.A.: Support vector machines used to estimate the battery sate of charge. IEEE Trans. Power Electron. 28, 5919-5926 (2013) https://doi.org/10.1109/TPEL.2013.2243918
  59. Zhang, S., Yang, L., Zhao, X., Qiang, J.: A GA optimization for lithium-ion battery equalization based on SOC estimation by NN and FLC. Electr. Power Energy Syst. 73, 318-328 (2015) https://doi.org/10.1016/j.ijepes.2015.05.018
  60. Moura, S. J., Chaturvedi, N.A., Krstic, M.: PDE estimation techniques for advanced battery management systems-Part I: SOC estimation. In: 2012 American Control Conference (2012)
  61. Shrivastava, P., Soon, T.K., Idris, M.: Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries. Renew. Sustain. Energy Rev. 113, 109233 (2019) https://doi.org/10.1016/j.rser.2019.06.040
  62. Anton, A., Nieto, G., Gonzlo, G., Perez, V., Vega, G., Viejo, B.: A new predictive model for the state-of-charge of a high-power lithium-ion cell based on a PSO-optimized multivariated adaptive regression spline approach. IEEE Trans. Veh. Technol. 65, 4197-4208 (2016) https://doi.org/10.1109/TVT.2015.2504933
  63. Lai, X., Wang, S., He, L., Zhou, L., Zheng, Y.: A hybrid state-of-charge estimation method based on credible increment for electric vehicle applications with large sensor and model errors. J. Energy Storage 27, 101106 (2020) https://doi.org/10.1016/j.est.2019.101106
  64. Xiong, R., Cao, J., Yu, Q., He, H.: Critical review on the battery state of charge estimation methods for electric vehicles. In: IEEE Access Special Section on Battery Energy Storage and Management System (2018)
  65. Peng, S., Chen, C., Shi, H., Yao, Z.: State of charge estimation of battery energy storage systems based on adaptive unscented Kalman filter with a noise statistics estimator. IEEE Acces 5, 13202-13212 (2017) https://doi.org/10.1109/ACCESS.2017.2725301
  66. Zhang, C., Allaf, W., Dinh, Q., Ascencio, P., Marco, J.: Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique. Energy 142, 678-688 (2018) https://doi.org/10.1016/j.energy.2017.10.043
  67. Xuan, D., Shi, X., Chen, J., Zhang, C., Wang, Y.: Real-time estimation of state-of-charge in lithium-ion batteries using improved central diference transform method. J. Clean. Prod. 252, 119787 (2020) https://doi.org/10.1016/j.jclepro.2019.119787
  68. Duong, V., Bastawrous, H.A., See, K.W.: Accurate approach to the temperature efect on state of charge estimation in the LiFePO4 battery under dynamic load operation. Appl. Energy 204, 560-571 (2017) https://doi.org/10.1016/j.apenergy.2017.07.056
  69. Xu, Y., Hu, M., Zhou, A., Li, Y., Li, S., Fu, C., Gong, C.: State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter. Appl. Math. Model. 77, 1255-1272 (2020) https://doi.org/10.1016/j.apm.2019.09.011
  70. Bi, Y., Choe, S.Y.: An adaptive sigma-point Kalman filter with state equality constraints for online state-of-charge estimation of Li(NiMnCo)O2/carbon battery using a reduced-order electrochemical model. Appl. Energy 258, 113925 (2020) https://doi.org/10.1016/j.apenergy.2019.113925
  71. Bian, C., He, H., Yang, S.: Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries. Energy 191, 116538 (2020) https://doi.org/10.1016/j.energy.2019.116538
  72. Huang, D., Chen, Z., Zheng, C., Li, H.: A model-based state-of-charging estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature. Energy 185, 847-861 (2019) https://doi.org/10.1016/j.energy.2019.07.063
  73. Abbas, G., Nawaz, M., Kamran, F.: Performance comparison of NARX & RNN-LSTM neural networks for LiFePO4 battery state of charge estimation, IBCAST (2019)
  74. Chemali, E., Kollmeyer, P.J., Preindl, M., Emadi, A.: State-of-charge estimation of Li-ion batteries using deep neural networks: a machine learning approach. J. Power Sources 400, 242-255 (2018) https://doi.org/10.1016/j.jpowsour.2018.06.104
  75. Peng, J., Luo, J., He, H., Lu, B.: An improved state of charge estimation method based on cubature Kalman filter for lithium-ion batteries. Appl. Energy 253, 113520 (2019) https://doi.org/10.1016/j.apenergy.2019.113520
  76. Hu, L., Hu, X., Che, Y., Feng, F., Lin, X., Zhang, Z.: Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering. Appl. Energy 262, 114569 (2020) https://doi.org/10.1016/j.apenergy.2020.114569
  77. Singh, K.V., Bansal, H.O., Singh, D.: Hardware-in-the-loop implementation of ANFIS based adaptive SoC estimation of lithium-ion battery for hybrid vehicle applications. J. Energy Storage 27, 101124 (2020) https://doi.org/10.1016/j.est.2019.101124
  78. Chemali, E., Kollmeyer, P.J., Preindl, M., Ahmend, R., Emadi, A.: Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries. IEEE Trans. Ind. Electron. 65, 6730-6739 (2018) https://doi.org/10.1109/tie.2017.2787586
  79. Fotouhi, A., Propp, K., Samaranayake, L., Auger, D., Longo, S.: A hardware-in-the-loop test rig for development of electric vehicle battery identification and state estimation algorithms. Int. J. Powertrains 7, 227-278 (2018) https://doi.org/10.1504/IJPT.2018.090391
  80. 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. Appl. Energy 86, 1506-1511 (2009) https://doi.org/10.1016/j.apenergy.2008.11.021
  81. Schuster, S.F., Bach, T., Fleder, E., Muller, J., Brand, M., Sextl, G., Jossen, A.: Nonlinear aging characteristics of lithium-ion cells under different operational conditions. J. Energy Storage 1, 44-53 (2015) https://doi.org/10.1016/j.est.2015.05.003
  82. Weng, C., Cui, Y., Sun, J., Peng, H.: On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression. J. Power Sources 235, 36-44 (2013) https://doi.org/10.1016/j.jpowsour.2013.02.012
  83. Wang, L., Pan, C., Liu, L., Cheng, Y., Zhao, X.: On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis. Appl. Energy 168, 465-472 (2016) https://doi.org/10.1016/j.apenergy.2016.01.125
  84. Baghadadi, I., Briat, O., Hyan, P., Vinassa, J.M.: State of health assessment for lithium batteries based on voltage-time relaxation measure. Electrochim. Acta 194, 461-472 (2016) https://doi.org/10.1016/j.electacta.2016.02.109
  85. Zhou, D., Xue, L., Song, Y., Chen, J.: On-line remaining useful life prediction of lithium-ion batteries based on the optimized gray model GM(1,1). Batteries 3(3), 21 (2017) https://doi.org/10.3390/batteries3030021
  86. Hu, X., Feng, F., Liu, K., Zhang, L.: State estimation for advanced battery management: key challenges and future trends. Renew. Sustain. Energy Rev. 114, 109334 (2019) https://doi.org/10.1016/j.rser.2019.109334
  87. Wei, Z., Tseng, K.J., Wai, N., Lim, T.M., Skyllas-Kazacos, M.: Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery. J. Power Sources 332, 389-398 (2016) https://doi.org/10.1016/j.jpowsour.2016.09.123
  88. Waag, W., Kabitz, S., Sauer, D.: Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application. Appl. Energy 102, 885-897 (2013) https://doi.org/10.1016/j.apenergy.2012.09.030
  89. Li, X., Wang, Z., Yan, J.: Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression. J. Power Source 421, 56-67 (2019) https://doi.org/10.1016/j.jpowsour.2019.03.008
  90. Guo, J., Li, Z., Pecht, M.: A Bayesian approach for Li-Ion battery capacity fade modeling and cycles to failure prognostics. J. Power Sources 281, 173-184 (2015) https://doi.org/10.1016/j.jpowsour.2015.01.164
  91. Wang, Z., Ma, J., Zhang, L.: State-of-health estimation for lithium-ion batteries based on the multi-island genetic algorithm and the Gaussian process regression. IEEE Access 5, 1286-21295 (2017)
  92. Lievre, A., Sari, A., Venet, P., Hijazi, A., Ouattara-Brigaudet, M., Pelissier, S.: Practical online estimation of lithium-ion battery apparent series resistance for mild hybrid vehicles. IEEE Trans. Veh. Technol. 65(6), 4505-4511 (2016) https://doi.org/10.1109/TVT.2015.2446333
  93. Wassiliadis, N., Adermann, J., Frericks, A., Pak, M., Reiter, C., Lohmann, B., Lienkamp, M.: Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation: a use-case life cycle analysis. J Energy Storage 19, 73-87 (2018) https://doi.org/10.1016/j.est.2018.07.006
  94. Topan, P.A., Ramadan, M.N., Fathoni, G., Cahyadi, A.I., Wahyunggoro, O.: State-of Charge (SOC) and State of Health (SOH) estimation on lithium polymer battery via Kalman filter, 2016 2nd ICST (2016)
  95. Qiu, X., Wu, W., Wang, S.: Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method. J. Power Sources 450, 227700 (2020) https://doi.org/10.1016/j.jpowsour.2020.227700
  96. Huang, S.C., Tseng, K.H., Liang, J.W., Chang, C.L., Pecht, M.G.: An Online SOC and SOH estimation model for lithium-ion batteries. Energies 10(4), 512 (2017) https://doi.org/10.3390/en10040512
  97. Birkl, C.R., Roberts, M.R., McTurk, E., Bruce, P.G., Howey, D.A.: Degradation diagnostics for lithium ion cells. J. Power Sources 86, 341-373 (2017)
  98. Saxena, S., Xing, Y., Kwon, D.I., Pecht, M.: Accelerated degradation model for C-rate loading of lithium-ion batteries. Electr. Power Energy Syst. 107, 438-445 (2019) https://doi.org/10.1016/j.ijepes.2018.12.016
  99. Kim, T.S., Adhikaree, A., Pandey, R., Kang, D.W., Kim, M.H., Oh, C.Y., Baek, J.W.: An on-board model-based condition monitoring for lithium-ion batteries. IEEE Trans. Ind. Appl. 55(2), 1835-1843 (2019) https://doi.org/10.1109/tia.2018.2881183
  100. Zhang, X., Wang, Y., Liu, C., Chen, Z.: A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm. J. Power Sources 376, 191-199 (2018) https://doi.org/10.1016/j.jpowsour.2017.11.068
  101. Chen, L., Lu, Z., Lin, W., Li, J., Pan, H.: A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity. Measurement 116, 586-595 (2018) https://doi.org/10.1016/j.measurement.2017.11.016
  102. Tang, X., Wnag, Y., Zou, C., Yao, K., Xia, Y., Gao, F.: A novel framework for lithium-ion battery modeling considering uncertainties of temperature and aging. Energy Convers. Manag. 180, 162-170 (2019) https://doi.org/10.1016/j.enconman.2018.10.082
  103. Qu, J., Liu, F., Ma, Y., Fan, J.: A neural-network-based method for RUL prediction and SOH monitoring of lithium-ion battery. IEEE Access 7, 87178-87191 (2019) https://doi.org/10.1109/access.2019.2925468
  104. Deng, Y., Ying, H., Jiaqiang, E., Zhu, H., Wei, K., Chen, J., Zhang, F., Liao, G.: Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries. Energy 176, 91-102 (2019) https://doi.org/10.1016/j.energy.2019.03.177
  105. Leijen, P., Steyn-Ross, D.A., Kularatna, N.: Use of effective capacitance variation as a measure of state-of-health in a series-connected automotive battery pack. IEEE Trans. Veh. Technol. 67, 1961-1968 (2017) https://doi.org/10.1109/tvt.2017.2733002
  106. Cui, Y., Zuo, P., Du, C., Gao, Y., Yang, J.: State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method. Energy 144, 647-656 (2018) https://doi.org/10.1016/j.energy.2017.12.033
  107. Li, Y., Abdel-Monem, M., Gopalakrishnan, R., Berecibar, M., Nanini-Maury, E., Omar, N., Bossche, P., Mierlo, J.V.: A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter. J. Power Sources 373, 40-53 (2018) https://doi.org/10.1016/j.jpowsour.2017.10.092
  108. Cabrera-Castillo, E., Niedermeier, F., Jossen, A.: Calculation of the state of Safety (SOS) for lithium ion batteries. J. Power Source 324, 509-520 (2016) https://doi.org/10.1016/j.jpowsour.2016.05.068
  109. Xiong, R., He, H., Sun, F., Zhao, K.: Online estimation of peak power capability of Li-ion batteries in electric vehicles by a hardware-in-loop approach. Energies 5(5), 1455-1469 (2012) https://doi.org/10.3390/en5051455
  110. Dong, G., Wei, J., Chen, Z.: Kalman filter for onboard state of charge estimation and peak power capability analysis of lithium-ion batteries. J. Power Sources 328, 615-626 (2016) https://doi.org/10.1016/j.jpowsour.2016.08.065
  111. Guo, G., Long, B., Cheng, B., Zhou, S., Xu, P., Cao, B.: Three-dimensional thermal fnite element modeling of lithium-ion battery in thermal abuse application. J. Power Sources 195, 2393-2398 (2010) https://doi.org/10.1016/j.jpowsour.2009.10.090
  112. Kim, G.H., Pesaran, A., Spotnitz, R.: A three-dimensional thermal abuse model for lithium-ion cells. J. Power Sources 170, 476-489 (2007) https://doi.org/10.1016/j.jpowsour.2007.04.018
  113. Xie, Y., He, X., Hu, X., Li, W., Zhang, Y., Liu, B., Sum, Y.: An improved resistance-based thermal model for a pouch lithium-ion battery considering heat generation of posts. Appl. Therm. Eng. 164, 114455 (2020) https://doi.org/10.1016/j.applthermaleng.2019.114455
  114. Yang, N., Fu, Y., Yue, H., Zheng, J., Zhang, X., Yang, C., Wang, J.: An improved semi-empirical model for thermal analysis of lithium-ion batteries. Electrochim. Acta 311, 8-20 (2019) https://doi.org/10.1016/j.electacta.2019.04.129
  115. Zhang, C., Li, K., Deng, J.: Real-time estimation of battery internal temperature based on a simplified thermoelectric model. J. Power Sources 302, 146-154 (2016) https://doi.org/10.1016/j.jpowsour.2015.10.052
  116. Zhu, J., Sun, Z., Wei, X., Dai, H.: Battery internal temperature estimation for LiFePO4 battery based on impedance phase shift under operating conditions. Energies 10(60), 60 (2017) https://doi.org/10.3390/en10010060
  117. Jeong, M.G., Cho, J.H., Lee, B.J.: Heat transfer analysis of a high-power and large-capacity thermal battery and investigation of effective thermal model. J. Power Sources 424, 35-41 (2019) https://doi.org/10.1016/j.jpowsour.2019.03.067
  118. Li, J., Sun, D., Jin, X., Shi, W., Sun, C.: Lithium-ion battery overcharging thermal characteristics analysis and an impedance-based electro-thermal coupled model simulation. Appl. Energy 254, 113574 (2019) https://doi.org/10.1016/j.apenergy.2019.113574

Cited by

  1. An ASIC-Based Miniaturized System for Online Multi-Measurand Monitoring of Lithium-Ion Batteries vol.7, pp.3, 2021, https://doi.org/10.3390/batteries7030045
  2. Application domain extension of incremental capacity-based battery SoH indicators vol.234, 2020, https://doi.org/10.1016/j.energy.2021.121224
  3. Overdischarge Detection and Prevention With Temperature Monitoring of Li-Ion Batteries and Linear Regression-Based Machine Learning vol.18, pp.4, 2020, https://doi.org/10.1115/1.4051296
  4. Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries vol.14, pp.24, 2021, https://doi.org/10.3390/en14248560
  5. State of Health Estimation Method for Lithium-Ion Batteries Based on Nonlinear Autoregressive Neural Network Model With Exogenous Input vol.19, pp.2, 2020, https://doi.org/10.1115/1.4052274