Implementation of an improved Coulomb-Counting Algorithm Based on a Piecewise SOC-OCV Relationship for SOC Estimation of Li-Ion Battery

ines Baccouche, sabeur Jemmali, Asma Mlayah, Bilal Manai, Najoua Essoukri Ben Amara

Abstract


Considering the expanding use of mobile devices equipped with rechargeable batteries, especially Li-ion batteries that have higher power and energy density, the battery management function becomes increasingly important. In fact, the accuracy of the amount of remaining charges estimation is critical as it affects the device autonomy. Therefore, the battery State-Of-Charge (SOC) is defined to indicate its estimated available capacity. In this paper, a method for Li-ion battery SOC estimation based on an enhanced Coulomb-counting is proposed to be implemented for multimedia applications. Assuming that Coulomb-counting suffers from cumulative errors due to the initial SOC and the measurements uncertainties errors, we used a piece-wise linear SOC-OCV relationship and periodic re-calibration to overcome these limitations. This solution has been implemented and validated on a hardware platform based on PIC18F MCU Family. The measurement results were correlated with theoretical ones and the method has shown a reliable estimation since accuracy is less than 2%.

 


Keywords


Li-ion Battery; Monitoring;State Of Charge (SOC); Coulomb-counting; Piecewise linear SOC-OCV; Hardware implementation

Full Text:

PDF

References


Reddy, T. Linden’s, Handbook of Batteries, 4th ed.; McGraw-Hill Education: New York, USA 2010.

Y. Nishi, “Lithium ion secondary batteries; past 10 years and the future,†Journal of Power Sources, vol. 100, no. 1, pp. 101–106, 2001.

V. Pop, H. J. Bergveld, P. Notten, and P. P. Regtien, “State-of-the-art of battery state-of-charge determination†Measurement Science and Technology, vol. 16, no. 12, p. R93,2005.

B. Xiao, Y. Shi, and L. He, “A universal state-of-charge algorithm for batteries,†in Proceedings of the 47th Design Automation Conference, pp. 687–692, ACM, 2010.

W.-Y. Chang, “The state of charge estimating methods for battery: a review,†ISRN Applied Mathematics, vol. 2013, 2013.

S. Piller, M. Perrin, and A. Jossen, “Methods for state-of-charge determination and their applications,†Journal of power sources, vol. 96, no. 1, pp. 113–120, 2001.

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, 2009.

I. Baccouche, A. Mlayah, S. Jemmali, B. Manai, and N. E. B. Amara, “Implementation of a coulomb counting algorithm for soc estimation of li-ion battery for multimedia applications,†in Systems, Signals & Devices (SSD), 12th International Multi-Conference on, pp. 1–6, IEEE, 2015.

A. Nugroho, E. Rijanto, F. D. Wijaya, and P. Nugroho, “Battery state of charge estimation by using a combination of coulomb counting and dynamic model with adjusted gain,†in Sustainable Energy Engineering and Application (ICSEEA), International Conference on, pp. 54–58, IEEE, 2015.

T. H. Wu, J. K. Wang, C. S. Moo, and A. Kawamura, “State-of-charge and state-of-health estimating method for lithium-ion batteries,†in 2016 IEEE 17th Workshop on Control and Modeling for Power Electronics (COMPEL), pp. 1–6, June 2016.

Z. Zou, J. Xu, C. Mi, B. 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, 2014.

X. Chen, W. Shen, Z. Cao, and A. Kapoor, “A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles,†Journal of Power Sources, vol. 246, pp. 667–678, 2014.

Y. Shen, “Adaptive online state-of-charge determination based on neuro-controller and neural network,†Energy Conversion and Management, vol. 51, no. 5, pp. 1093–1098, 2010.

P. Singh, C. Fennie, and D. Reisner, “Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries,†Journal of Power Sources, vol. 136, no. 2, pp. 322–333, 2004.

S. J. Moura, M. Krstic, and N. A. Chaturvedi, “Adaptive pde observer for battery soc/soh estimation,†11th Motion and Vibration Conference, pp. 101–110, American Society of Mechanical Engineers, 2012.

H. He, R. Xiong, and J. Fan, “Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach,†Energies, vol. 4, no. 4, pp. 582–598, 2011.

M. Lagraoui, S. Doubabi, and A. Rachid, “SoC estimation of lithium-ion battery using kalman filter and luenberger observer: A comparative study,†in Renewable and Sustainable Energy Conference (IRSEC), 2014 International, pp. 636–641, IEEE, 2014.

G. L. Plett, “Extended kalman filtering for battery management systems of lipb-based hev battery packs: Part 2. modeling and identification,†Journal of power sources, vol. 134, no. 2, pp. 262–276, 2004.

J. Li, J. K. Barillas, C. Guenther, and M. A. Danzer, “A comparative study of state of charge estimation algorithms for lifepo 4 batteries used in electric vehicles,†Journal of power sources, vol. 230, pp. 244–250, 2013.

L. Xu, J.Wang, and Q. 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, 2012.

X. Hu, F. Sun, and Y. Zou, “Estimation of state of charge of a lithium-ion battery pack for electric vehicles using an adaptive luenberger observer,†Energies, vol. 3, no. 9, pp. 158 1603, 2010.

A. Belhani, N. K. M’Sirdi, and A. Naamane, “Adaptive sliding mode observer fo estimation of state of charge,†Energy Procedia, vol. 42, pp. 377–386, 2013.

J. Xu, C. C. Mi, B. Cao, J. Deng, Z. Chen, and S. Li, “The state of charge estimation of lithium-ion batteries based on a proportional-integral observer,†Vehicular Technology, IEEE Transactions on, vol. 63, no. 4, pp. 1614–1621, 2014.

F. Baranti, R. Roncella, R. Saletti, and W. Zamboni, “Fpga implementation of the mix algorithm for state-of-charge estimation of lithium-ion batteries,†in IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society, pp. 5641–5646, IEEE, 2014.

Y. Xing, W. He, M. Pecht, and K. L. Tsui, “State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures,†Applied Energy, vol. 113, pp. 106–115, 2014.

Y.-M. Jeong, Y.-K. Cho, J.-H. Ahn, S.-H. Ryu, and B.-K. Lee, “Enhanced coulomb counting method with adaptive soc reset time for estimating ocv,†in Energy Conversion Congress and Exposition (ECCE), 2014 IEEE, pp. 1313–1318, IEEE, 2014.

S. Yuan, H. Wu, and C. Yin, “State of charge estimation using the extended kalman filter for battery management systems based on the arx battery model,†Energies, vol. 6, no. 1, pp. 444–470, 2013.

T. Huria, M. Ceraolo, J. Gazzarri, and R. Jackey, “Simplified extended kalman filter observer for soc estimation of commercial power-oriented lfp lithium battery cells,†tech. rep., SAE Technical Paper, 2013.

C. Weng, J. Sun, and H. Peng, “A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring,†Journal of Power Sources, vol. 258, pp. 228–237, 2014.

X. Hu, S. Li, H. Peng, and F. Sun, “Robustness analysis of state-of-charge estimation methods for two types of li-ion batteries,†Journal of power sources, vol. 217, pp. 209–219, 2012.

M. Petzl and M. A. Danzer, “Advancements in ocv measurement and analysis for lithium-ion batteries,†IEEE Transactions on Energy Conversion, vol. 28, pp. 675–681, Sept 2013.

I. Baccouche, S. Jemmali, B. Manai, R. Chaibi, and N. E. B. Amara, “Hardware implementation of an algorithm based on kalman filtrer for monitoring low capacity li-ion batteries,†, 7th International Renewable Energy Congress (IREC), pp. 1–6, March 2016.

H. Rahimi-Eichi, F. Baronti, and M.-Y. Chow, “Online adaptive parameter identification and state-of-charge co-estimation for lithium-polymer battery cells,†Industrial Electronics, IEEE Transactions on, vol. 61, no. 4, pp. 2053–2061, 2014.

K.-S. Ng, Y.-F. Huang, C.-S. Moo, and Y.-C. Hsieh, “An enhanced coulomb counting method for estimating state-of-charge and state-of-health of lead-acid batteries,†in Telecommunications Energy Conference. INTELEC 2009. 31st International, pp. 1–5, IEEE, 2009.

N. Omar, “Phd. thesis: Assessment of rechargeable energy storage systems for plug-in hybrid electric vehiclesâ€, Vrije Universiteit Brussel, 2012.




DOI (PDF): https://doi.org/10.20508/ijrer.v8i1.6686.g7292

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE in 2025; 

h=35,

Average citation per item=6.59

Last three Years Impact Factor=(1947+1753+1586)/(146+201+78)=5286/425=12.43

Category Quartile:Q4