CSEE Journal of Power and Energy Systems
2022, 8(1): 105-114
https://doi.org/10.17775/CSEEJPES.2019.03060
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06 July 2020
Recent Progress and Future Trends on the State of Charge Estimation Methods to Improve Battery-storage Efficiency: A Review
Md Ohirul Qays(
),
),
Keywords:
Battery Management System (BMS), battery modeling, battery storage efficiency, state of charge (SOC)
Cite this article:
Qays MO, Buswig Y, Hossain ML, et al.
Recent Progress and Future Trends on the State of Charge Estimation Methods to Improve Battery-storage Efficiency: A Review.
CSEE Journal of Power and Energy Systems,
2022, 8(1): 105-114.
https://doi.org/10.17775/CSEEJPES.2019.03060
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Battery storage systems are subject to frequent charging/discharging cycles, which reduce the operational life of the battery and reduce system reliability in the long run. As such, several Battery Management Systems (BMS) have been developed to maintain system reliability and extend the battery’s operative life. Accurate estimation of the battery’s State of Charge (SOC) is a key challenge in the BMS due to its non-linear characteristics. This paper presents a comprehensive review on the most recent classifications and mathematical models for SOC estimation. Future trends for SOC estimation methods are also presented.
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Recent Progress and Future Trends on the State of Charge Estimation Methods to Improve Battery-storage Efficiency: A Review
Md Ohirul Qays(
),
),
Abstract
Battery storage systems are subject to frequent charging/discharging cycles, which reduce the operational life of the battery and reduce system reliability in the long run. As such, several Battery Management Systems (BMS) have been developed to maintain system reliability and extend the battery’s operative life. Accurate estimation of the battery’s State of Charge (SOC) is a key challenge in the BMS due to its non-linear characteristics. This paper presents a comprehensive review on the most recent classifications and mathematical models for SOC estimation. Future trends for SOC estimation methods are also presented.
Keywords:
Battery Management System (BMS), battery modeling, battery storage efficiency, state of charge (SOC)
References(90)
[1]
H. Branco, R. Castro, and A. Setas Lopes, “Battery energy storage systems as a way to integrate renewable energy in small isolated power systems,”Energy for Sustainable Development, vol. 43, pp. 90–99, Apr. 2018.
[2]
E. Hossain, M. Zawad, K. H. Rakibul Islam, and Q. Akash, “Design a novel controller for stability analysis of microgrid by managing controllable load using load shaving and load shifting techniques; and optimizing cost analysis for energy storage system,”International Journal of Renewable Energy Research, vol. 6, no. 3, pp. 772–786, Jan. 2016.
[3]
U. Caldera and C. Breyer, “The role that battery and water storage play in Saudi Arabia’s transition to an integrated 100% renewable energy power system,”Journal of Energy Storage, vol. 17, pp. 299–310, Jun. 2018.
[4]
M. Partovibakhsh and G. J. Liu, “An adaptive unscented kalman filtering approach for online estimation of model parameters and state-of-charge of lithium-ion batteries for autonomous mobile robots,”IEEE Transactions on Control Systems Technology, vol. 23, no. 1, pp. 357–363, Jan. 2015.
[5]
J. S. Lee, S. T. Kim, R. G. Cao, N. S. Choi, M. L. Liu, K. T. Lee, and J. Cho, “Metal-air batteries with high energy density: Li-air versus Zn-air,”Advanced Energy Materials, vol. 1, no. 1, pp. 34–50, Jan. 2011.
[6]
J. Balach, J. Linnemann, T. Jaumann, and L. Giebeler, “Metal-based nanostructured materials for advanced lithium–sulfur batteries,”Journal of Materials Chemistry A, vol. 6, no. 46, pp. 23127–23168, Nov. 2018.
[7]
M. A. Hannan, M. Hoque, A. Hussain, Y. Yusof, and P. J. Ker, “State-of-the-art and energy management system of lithium-ion batteries in electric vehicle applications: Issues and recommendations,”IEEE Access, vol. 6, pp. 19362–19378, Mar. 2018.
[8]
R. Xiong, J. Y. Cao, Q. Q. Yu, H. W. He, and F. C. Sun, “Critical review on the battery state of charge estimation methods for electric vehicles,”IEEE Access, vol. 6, pp. 1832–1843, Dec. 2017.
[9]
J. P. Rivera-Barrera, N. Muñoz-Galeano, and H. O. Sarmiento-Maldonado, “SoC estimation for lithium-ion batteries: Review and future challenges,”Electronics, vol. 6, no. 4.pp. 102, Dec. 2017.
[10]
W. Y. Chang, “The state of charge estimating methods for battery: A review,”International Scholarly Research Notices, vol. 2013, pp. 953792, Jul. 2013.
[11]
A. B. Ahmad, C. A. Ooi, D. Ishak, and J. Teh, “State-of-charge balancing control for ON/OFF-line internal cells using hybrid modular multi-level converter and parallel modular dual l-bridge in a grid-scale battery energy storage system,”IEEE Access, vol. 7, pp. 131–147, Dec. 2018.
[12]
Y. Q. Yang, S. Bremner, C. Menictas, and M. Kay, “Battery energy storage system size determination in renewable energy systems: A review,”Renewable and Sustainable Energy Reviews, vol. 91, pp. 109–125, Aug. 2018.
[13]
Y. J. Zheng, X. B. Han, L. G. Lu, J. Q. Li, and M. G. Ouyang, “Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles,”Journal of Power Sources, vol. 223, pp. 136–146, Feb. 2013.
[14]
R. Ahmed, M. El Sayed, I. Arasaratnam, J. Tjong, and S. Habibi, “Reduced-order electrochemical model parameters identification and state of charge estimation for healthy and aged li-ion batteries—part II: Aged battery model and state of charge estimation,”IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 2, no. 3, pp. 678–690, Sep. 2014.
[15]
Z. Y. Zou, J. Xu, C. Mi, B. G. Cao, and Z. Chen, “Evaluation of model based state of charge estimation methods for lithium-ion batteries,”Energies, vol. 7, no. 8, pp. 5065–5082, Aug. 2014.
[16]
C. Lin, L. Q. Q. Yu, R. Xiong, and L. Y. Wang, “A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries,”Applied Energy, vol. 205, pp. 892–902, Nov. 2017.
[17]
X. J. Dang, L. Yan, H. Jiang, X. R. Wu, and H. X. Sun, “Open-circuit voltage-based state of charge estimation of lithium-ion power battery by combining controlled auto-regressive and moving average modeling with feedforward-feedback compensation method,”International Journal of Electrical Power & Energy Systems, vol. 90, pp. 27–36, Sep. 2017.
[18]
G. Z. Dong, J. W. Wei, C. B. Zhang, and Z. H. Chen, “Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO
[19]
R. E. Tudoroiu, M. Zaheeruddin, S. M. Radu, and N. Tudoroiuv, “Estimation techniques for state of charge in battery management systems on board of hybrid electric vehicles implemented in a real-time MATLAB/SIMULINK environment,”in New Trends in Electrical Vehicle Powertrains, L. R. Martinez, Ed. London: IntechOpen, 2019, pp. 13.
[20]
T. Blank, C. Lipps, W. Ott, P. Hoffmann, and M. Weber, “Influence of environmental conditions on the sensing accuracy of Li-Ion battery management systems with passive charge balancing,”in Proceedings of the 2015 17th Europea Conference on Power Electronics and Applications, 2015, pp. 1–9.
[21]
R. F. Zhang, B. Z. Xia, B. H. Li, L. B. Cao, Y. Z. Lai, W. W. Zheng, H. W. Wang, W. Wang, and M. W. Wang, “A study on the open circuit voltage and state of charge characterization of high capacity lithium-ion battery under different temperature,”Energies, vol. 11, no. 9, pp. 2408, Sep. 2018.
[22]
M. Coleman, C. K. Lee, C. B. Zhu, and W. G. Hurley, “State-of-charge determination from EMF voltage estimation?: Using impedance, terminal voltage, and current for lead-acid and lithium-ion batteries,”IEEE Transactions on Industrial Electronics, vol. 54, no. 5, pp. 2550–2557, Oct. 2007.
[23]
H. B. Ren, Y. Z. Zhao, S. Z. Chen, and T. P. Wang, “Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation,”Energy, vol. 166, pp. 908–917, Jan. 2019.
[24]
L. Y. Wang, M. P. Polis, G. G. Yin, W. Chen, Y. H. Fu, and C. C. Mi, “Battery cell identification and soc estimation using string terminal voltage measurements,”IEEE Transactions on Vehicular Technology, vol. 61, no. 7, pp. 2925–2935, Sep. 2012.
[25]
H. F. Dai, B. Jiang, and X. Z. Wei, “Impedance characterization and modeling of lithium-ion batteries considering the internal temperature gradient,”Energies, vol. 11, no. 1, pp. 220, Jan. 2018.
[26]
B. G. Carkhuff, P. A. Demirev, and R. Srinivasan, “impedance-based battery management system for safety monitoring of lithium-ion batteries,”IEEE Transactions on Industrial Electronics, vol. 65, no. 8, pp. 6497–6504, Aug. 2018.
[27]
W. Waag, S. Käbitz, and D. U. Sauer, “Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application,”Applied Energy, vol. 102, pp. 885–897, Feb. 2013.
[28]
X. H. Zhu, L. Fernández Macía, J. Jaguemont, J. de Hoog, A. Nikolian, N. Omar, and A. Hubin, “Electrochemical impedance study of commercial LiNi
[29]
S. Skoog and S. David, “Parameterization of linear equivalent circuit models over wide temperature and SOC spans for automotive lithium-ion cells using electrochemical impedance spectroscopy,”Journal of Energy Storage, vol. 14, pp. 39–48, Dec. 2017.
[30]
Q. A. Huang, Y. Shen, Y. H. Huang, L. Zhang, and J. J. Zhang, “Impedance characteristics and diagnoses of automotive lithium-ion batteries at 7.5% to 93.0% state of charge,”Electrochimica Acta, vol. 219, pp. 751–765, Nov. 2016.
[31]
M. W. Cheng, Y. S. Lee, M. Liu, and C. C. Sun, “State-of-charge estimation with aging effect and correction for lithium-ion battery,”IET Electrical Systems in Transportation, vol. 5, no. 2, pp. 70–76, Jun. 2015.
[32]
K. W. E. Cheng, B. P. Divakar, H. J. Wu, K. Ding, and H. F. Ho, “Battery-management system (BMS) and SOC development for electrical vehicles,”IEEE Transactions on Vehicular Technology, vol. 60, no. 1, pp. 76–88, Jan. 2011.
[33]
J. L. Xie, J. C. Ma, and K. Bai, “Enhanced coulomb counting method for state-of-charge estimation of lithium-ion batteries based on peukert’s law and coulombic efficiency,”Journal of Power Electronics, vol. 18, no. 3, pp. 910–922, May 2018.
[34]
A. Fotouhi, D. J. Auger, K. Propp, and S. Longo, “Lithium-sulfur battery state-of-charge observability analysis and estimation,”IEEE Transactions on Power Electronics, vol. 33, no. 7, pp. 5847–5859, Jul. 2018.
[35]
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.
[36]
T. H. Wu and C. S. Moo, “State-of-charge estimation with state-of-health calibration for lithium-ion batteries,”Energies, vol. 10, no. 7, pp. 987, Jul. 2017.
[37]
M. S. H. Lipu, M. A. Hannan, A. Hussain, M. H. M. Saad, A. Ayob, and F. Blaabjerg, “State of charge estimation for lithium-ion battery using recurrent NARX neural network model based lighting search algorithm,”IEEE Access, vol. 6, pp. 28150–28161, May 2018.
[38]
H. Chaoui and C. C. Ibe-Ekeocha, “State of charge and state of health estimation for lithium batteries using recurrent neural networks,”IEEE Transactions on Vehicular Technology, vol. 66, no. 10, pp. 8773–8783, Oct. 2017.
[39]
L. W. Kang, X. Zhao, and J. Ma, “A new neural network model for the state-of-charge estimation in the battery degradation process,”Applied Energy, vol. 121, pp. 20–27, May 2014.
[40]
B. Q. Wang, L. J. Wang, Y. L. Yin, Y. L. Xu, W. T. Zhao, and Y. C. Tang, “An improved neural network with random weights using backtracking search algorithm,”Neural Processing Letters, vol. 44, no. 1, pp. 37–52, Aug. 2016.
[41]
A. A. Hussein, “Capacity fade estimation in electric vehicle li-ion batteries using artificial neural networks,”IEEE Transactions on Industry Applications, vol. 51, no. 3, pp. 2321–2330, May-Jun. 2015.
[42]
O. Qays, Y. Buswig, and M. Anyi, “Active cell balancing control method for series-connected lithium-ion battery,”International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9, pp. 2424–2430, Jul. 2019.
[43]
M. A. Hannan, M. S. H. Lipu, A. Hussain, M. H. Saad, and A. Ayob, “Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm,”IEEE Access, vol. 6, pp. 10069–10079, Jan. 2018.
[44]
T. Leveringhaus and L. Hofmann, “Optimized voltage and reactive power adjustment in power grids using the least-squares-method: Optimization of highly utilized power grids with stochastic renewable energy-sources,”in Proceedings of 2011 International Conference on Power and Energy Systems, 2011, pp. 1–6.
[45]
F. Husnayain, A. R. Utomo, and P. S. Priambodo, “State of charge estimation for a lead-acid battery using backpropagation neural network method,”in Proceedings of 2014 International Conference on Electrical Engineering and Computer Science, 2014, pp. 274–278.
[46]
M. A. Awadallah and B. Venkatesh, “Accuracy improvement of SOC estimation in lithium-ion batteries,”Journal of Energy Storage, vol. 6, pp. 95–104, May 2016.
[47]
H. F. Dai, P. J. Guo, X. Z. Wei, Z. C. Sun, and J. Y. Wang, “ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries,”Energy, vol. 80, pp. 350–360, Feb. 2015.
[48]
C. Fleischer, W. Waag, Z. Bai, and D. U. Sauer, “Adaptive on-line state-of-available-power prediction of lithium-ion batteries,”Journal of Power Electronics, vol. 13, no. 4, pp. 516–527, Jul. 2013.
[49]
T. Zahid, K. Xu, W. M. Li, C. M. Li, and H. Z. Li, “State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles,”Energy, vol. 162, pp. 871–882, Nov. 2018.
[50]
S. M. Zhang, L. Yang, X. W. Zhao, and J. X. Qiang, “A GA optimization for lithium-ion battery equalization based on SOC estimation by NN and FLC,”International Journal of Electrical Power & Energy Systems, vol. 73, pp. 318–328, Dec. 2015.
[51]
L. Xu, J. P. Wang, and Q. S. Chen, “Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model,”Energy Conversion and Management, vol. 53, no. 1, pp. 33–39, Jan. 2012.
[52]
L. Zhang, K. Li, and E. W. Bai, “A new extension of newton algorithm for nonlinear system modelling using RBF neural networks,”IEEE Transactions on Automatic Control, vol. 58, no. 11, pp. 2929–2933, Nov. 2013.
[53]
X. P. Chen, W. X. Shen, M. X. Dai, Z. W. Cao, J. Jin, and A. Kapoor, “Robust adaptive sliding-mode observer using RBF neural network for lithium-ion battery state of charge estimation in electric vehicles,”IEEE Transactions on Vehicular Technology, vol. 65, no. 4, pp. 1936–1947, Apr. 2016.
[54]
F. C. Sun, R. Xiong, and H. W. He, “A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique,”Applied Energy, vol. 162, pp. 1399–1409, Jan. 2016.
[55]
W. Y. Chang, “Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA,”International Journal of Electrical Power & Energy Systems, vol. 53, pp. 603–611, Dec. 2013.
[56]
V. Surendar, V. Mohankumar, S. Anand, and V. D. Prasanna, “Estimation of state of charge of a lead acid battery using support vector regression,”Procedia Technology, vol. 21, pp. 264–270, 2015.
[57]
J. N. Hu et al., “State-of-charge estimation for battery management system using optimized support vector machine for regression,”J. Power Sources, vol. 269, pp. 682–693, Nov. 2014.
[58]
J. C. Álvarez Antón, P. J. García Nieto, F. J. de Cos Juez, F. Sánchez Lasheras, M. González Vega, and M. N. Roqueñí Gutiérrez, “Battery state-of-charge estimator using the SVM technique,”Applied Mathematical Modelling, vol. 37, no. 9, pp. 6244–6253, May 2013.
[59]
J. C. Alvarez Anton, P. J. Garcia Nieto, C. Blanco Viejo, and J. A. Vilan Vilan, “Support vector machines used to estimate the battery state of charge,”IEEE Transactions on Power Electronics, vol. 28, no. 12, pp. 5919–5926, Dec. 2013.
[60]
M. Shehab El Din, M. F. Abdel-Hafez, and A. A. Hussein, “Enhancement in Li-Ion battery cell state-of-charge estimation under uncertain model statistics,”IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 4608–4618, Jun. 2016.
[61]
Y. W. Li, C. Wang, and J. F. Gong, “A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty,”Energy, vol. 109, pp. 933–946, Aug. 2016.
[62]
S. C. Yang, C. Deng, Y. L. Zhang, and Y. L. He, “State of charge estimation for lithium-ion battery with a temperature-compensated model,”Energies, vol. 10, no. 10, pp. 1560, Oct. 2017.
[63]
W. D. Wang, X. T. Wang, C. L. Xiang, C. Wei, and Y. L. Zhao, “Unscented kalman filter-based battery SOC estimation and peak power prediction method for power distribution of hybrid electric vehicles,”IEEE Access, vol. 6, pp. 35957–35965, Jun. 2018.
[64]
L. F. Zheng, J. G. Zhu, G. X. Wang, D. D. C. Lu, and T. T. He, “Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter,”Energy, vol. 158, pp. 1028–1037, Sep. 2018.
[65]
C. Huang, Z. H. Wang, Z. H. Zhao, L. Wang, C. S. Lai, and D. Wang, “Robustness evaluation of extended and unscented kalman filter for battery state of charge estimation,”IEEE Access, vol. 6, pp. 27617–27628, May 2018.
[66]
H. S. Ramadan, M. Becherif, and F. Claude, “Extended kalman filter for accurate state of charge estimation of lithium-based batteries: A comparative analysis,”International Journal of Hydrogen Energy, vol. 42, no. 48, pp. 29033–29046, Nov. 2017.
[67]
B. Li, X. Q. Yuan, and L. Zhao, “Li-ion battery SOC estimation based on EKF algorithm,”in Proceedings of the 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, 2015, pp. 1584–1588.
[68]
N. Watrin, “Modélisation multi-physique des batteries à base lithium et application à l’estimation de l’état de charge,” Ph. D. dissertation, Department Electrical Engineering, Belfort-Montbéliard University of Technology, Belfort, France, 2013.
[69]
S. M. Peng, C. Chen, H. B. Shi, and Z. L. Yao, “State of charge estimation of battery energy storage systems based on adaptive unscented Kalman filter with a noise statistics estimator,”IEEE Access, vol. 5, pp. 13202–13212, Jul. 2017.
[70]
S. Muhammad, M. U. Rafique, S. Li, Z. L. Shao, Q. X. Wang, and N. Guan, “A robust algorithm for state-of-charge estimation with gain optimization,”IEEE Transactions on Industrial Informatics, vol. 13, no. 6, pp. 2983–2994, Apr. 2017.
[71]
L. H. Zhao, Z. Y. Liu, and G. H. Ji, “Lithium-ion battery state of charge estimation with model parameters adaptation using H ∞
[72]
Z. Liu, X. J. Dang, and H. X. Sun, “Online state of charge estimation for lithium-ion battery by combining incremental autoregressive and moving average modeling with adaptive h-infinity filter,”Mathematical Problems in Engineering, vol. 2018, pp. 7480602, Jul. 2018.
[73]
R. Xiong, Q. Q. Yu, L. Y. Wang, and C. Lin, “A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter,”Applied Energy, vol. 207, pp. 346–353, Dec. 2017.
[74]
Q. Q. Yu, R. Xiong, C. Lin, W. X. Shen, and J. J. Deng, “Lithium-ion battery parameters and state-of-charge joint estimation based on h-infinity and unscented kalman filters,”IEEE Transactions on Vehicular Technology, vol. 66, no. 10, pp. 8693–8701, Oct. 2017.
[75]
X. W. Guo, L. Y. Kang, Y. Yao, Z. Z. Huang, and W. B. Li, “Joint estimation of the electric vehicle power battery state of charge based on the least squares method and the Kalman filter algorithm,”Energies, vol. 9, no. 2, pp. 100, Feb. 2016.
[76]
C. Zhang, W. Allafi, Q. Dinh, P. Ascencio, and J. Marco, “Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique,”Energy, vol. 142, pp. 678–688, Jan. 2018.
[77]
Z. B. Wei, C. F. Zou, F. Leng, B. H. Soong, and K. J. Tseng, “Online model identification and state-of-charge estimate for lithium-ion battery with a recursive total least squares-based observer,”IEEE Transactions on Industrial Electronics, vol. 65, no. 2, pp. 1336–1346, Feb. 2018.
[78]
V. H. Duong, H. A. Bastawrous, K. C. Lim, K. W. See, P. Zhang, and S. X. Dou, “Online state of charge and model parameters estimation of the LiFePO
[79]
J. Kim, J. Shin, C. Chun, and B. H. Cho, “Stable configuration of a li-ion series battery pack based on a screening process for improved voltage/SOC balancing,”IEEE Transactions on Power Electronics, vol. 27, no. 1, pp. 411–424, Jan. 2012.
[80]
R. Klein, N. A. Chaturvedi, J. Christensen, J. Ahmed, R. Findeisen, and A. Kojic, “Electrochemical model based observer design for a lithium-ion battery,”IEEE Transactions on Control Systems Technology, vol. 21, no. 2, pp. 289–301, Mar. 2013.
[81]
R. Xiong, H. W. He, F. C. Sun, and K. Zhao, “Evaluation on State of Charge estimation of batteries with adaptive extended kalman filter by experiment approach,”IEEE Transactions on Vehicular Technology, vol. 62, no. 1, pp. 108–117, Jan. 2013.
[82]
C. Zhang, K. Li, J. Deng, and S. Song, “Improved realtime state-of-charge estimation of LiFePO
[83]
Z. L. Zhang, X. Cheng, Z. Y. Lu, and D. J. Gu, “SOC estimation of lithium-ion battery pack considering balancing current,”IEEE Transactions on Power Electronics, vol. 33, no. 3, pp. 2216–2226, Mar. 2018.
[84]
Q. Q. Wang, J. Wang, P. J. Zhao, J. Q. Kang, F. Yan, and C. Q. Du, “Correlation between the model accuracy and model-based SOC estimation,”Electrochimica Acta, vol. 228, pp. 146–159, Feb. 2017.
[85]
Y. Q. Shen, “Adaptive extended Kalman filter based state of charge determination for lithium-ion batteries,”Electrochimica Acta, vol. 283, pp. 1432–1440, Sep. 2018.
[86]
A. Bartlett, J. Marcicki, S. Onori, G. Rizzoni, X. G. Yang, and T. Miller, “Model-based state of charge estimation and observability analysis of a composite electrode lithium-ion battery,”in Proceedings of the 52nd IEEE Conference on Decision and Control, 2013, pp. 7791–7796.
[87]
K. A. Smith, C. D. Rahn, and C. Y. Wang, “Model-based electrochemical estimation and constraint management for pulse operation of lithium ion batteries,”IEEE Transactions on Control Systems Technology, vol. 18, no. 3, pp. 654–663, May 2010.
[88]
M. Corno, N. Bhatt, S. M. Savaresi, and M. Verhaegen, “Electrochemical model-based state of charge estimation for Li-Ion cells,”IEEE Transactions on Control Systems Technology, vol. 23, no. 1, pp. 117–127, Jan. 2015.
[89]
J. P. Tian, R. Xiong, and Q. Q. Yu, “Fractional-order model-based incremental capacity analysis for degradation state recognition of lithium-ion batteries,”IEEE Transactions on Industrial Electronics, vol. 66, no. 2, pp. 1576–1584, Feb. 2019.
[90]
M. M. Hoque, M. A. Hannan, A. Mohamed, and A. Ayob, “Battery charge equalization controller in electric vehicle applications?: A review,”Renewable and Sustainable Energy Reviews, vol. 75, pp. 1363–1385, Aug. 2017.
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Publication history
Received: 26 November 2019
Revised: 20 January 2020
Accepted: 23 May 2020
Published:
06 July 2020
Issue date: January 2022
Copyright
© 2019 CSEE
Acknowledgements
This work was supported by research and innovation management center (RIMC) UNIMAS through Fundamental Research Grant Scheme FRGS/1/2017/TK10/UNIMAS/03/1, Ministry of Higher Education, Malaysia.
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