### Battery State of Charge Estimation Based on Internal Resistance and Recovery Effect Analysis

#### Abstract

*State of Charge (SoC) is a parameter used to determine the current capacity on a battery as well as indicate the operational characteristics. The SoC is an important parameter for optimizing battery utilization in many applications requiring DC current source. However, estimating the SoC value is the major problem since it cannot be measured directly. In this study, we proposed SoC measurement method based on analysis internal resistance of battery. The internal resistance is correlated with the parameters of the magnitude of the terminal voltage and open circuit. Both voltages come from the influence of current during the charging-discharging process. We report that the proposed method has successfully obtained the correlation between the SoC and the internal resistance value for two process, which are the charging- and discharging process.*

#### Keywords

#### Full Text:

PDF#### References

Reddy T.B., 2011. Linden’s handbook of batteries: McGraw-Hill Education, 2011.

Movassagh K., Raihan A., Balasingam B., and Pattipati K., 2021. A critical look at coulomb counting approach for state of charge estimation in batteries. Energies 14: 4074.

Zhang S., Guo X., Dou X., and Zhang X., 2020. A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery. Sustainable Energy Technologies and Assessments 40: 100752.

Ausswamaykin A. and B. Plangklang. 2014. Design of real time battery management unit for PV-hybrid system by application of Coulomb counting method. Energy and Power Engineering 6: 186-193.

Li J., Barillas J.K., Guenther C., and Danzer M.A., 2013. A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles. Journal of Power Sources 230: 244-250.

Tran N.-T., Khan A., and Choi W., 2017. State of charge and state of health estimation of AGM VRLA Batteries by employing a dual extended Kalman filter and an ARX model for online parameter estimation. Energies 10: 137.

Wahono B., Ismail K., and Ogai H., 2015. Prediction model of battery state of charge and control parameter optimization for electric vehicle. Journal of Mechatronics, Electrical Power, and Vehicular Technology 6: 31.

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

Chiasserini C.-F. and R.R. Rao. 1999. A model for battery pulsed discharge with recovery effect. In WCNC. 1999 IEEE Wireless Communications and Networking Conference (Cat. No. 99TH8466), 1999, pp. 636-639.

Chau C.-K., Qin F., Sayed S., Wahab M.H., and Yang Y., 2010. Harnessing battery recovery effect in wireless sensor networks: Experiments and analysis. IEEE Journal on Selected Areas in Communications 28: 1222-1232.

Cai Y.-Y., Zhang Z., Zhang Y., and Liu Y.-F., 2015. A self-reconfiguration control regarding recovery effect to improve the discharge efficiency in the distributed battery energy storage system. In 2015 IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 1774-1778.

Arora H., Sherratt R.S., Janko B., and Harwin W., 2017. Experimental validation of the recovery effect in batteries for wearable sensors and healthcare devices discovering the existence of hidden time constants. The Journal of Engineering, 2017: 548-556.

E.T. Buletin, "Battery Internal Resistance," Buletin2005, 2005.

Schweiger H.G., Obeidi O., Komesker O., Raschke A., Schiemann M., Zehner C., 2010. Comparison of several methods for determining the internal resistance of lithium ion cells. Sensors (Basel) 10: pp. 5604-25.

Rand D., Holden L., May G., Newnham R., and Peters K., 1996. Valve-regulated lead/acid batteries. Journal of Power Sources 59: 191-197.

Chiang Y.-H., Sean W.-Y., and Ke J.-C., 2011. Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles. Journal of Power Sources 196: 3921-3932.

Cui X., Jing Z., Luo M., Guo Y., and Qiao H., 2018. A new method for state of charge estimation of lithium-ion batteries using square root cubature Kalman filter. Energies 11: 209.

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

Aylor J.H., Thieme A., and Johnson B., 1992. A battery state-of-charge indicator for electric wheelchairs. IEEE Transactions on Industrial Electronics 39: 398-409.

Chan H., 2000. A new battery model for use with battery energy storage systems and electric vehicles power systems. In 2000 IEEE power engineering society winter meeting. conference proceedings (Cat. No. 00CH37077), pp. 470-475.

Ramadan M.N., Pramana B.A., Widayat S.A., Amifia L.K., Cahyadi A., and Wahyunggoro O., 2015. Comparative study between internal ohmic resistance and capacity for battery state of health estimation. Mechatronics, Electrical Power & Vehicular Technology 6.

Xiong R., Yu Q., Wang L.Y., and Lin C., 2017. 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, 2017.

Fathoni G., Widayat S.A., Topan P.A., Jalil A., Cahyadi A.I., and Wahyunggoro O., 2017. Comparison of state-of-charge (SOC) estimation performance based on three popular methods: Coulomb counting, open circuit voltage, and Kalman filter. In 2017 2nd International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), pp. 70-74.

Yanhui Z., Wenji S., Shili L., Jie L., and Ziping F., 2013. A critical review on state of charge of batteries. Journal of Renewable and Sustainable Energy 5: 021403.

Casals L.C., González A.M.S., García B.A., and Llorca J., 2015. PHEV battery aging study using voltage recovery and internal resistance from onboard data. IEEE Transactions on Vehicular Technology 65: 4209-4216.

Pei L., Zhu C., and Lu R., 2013. Relaxation model of the open-circuit voltage for state-of-charge estimation in lithium-ion batteries. IET Electrical Systems in Transportation 3: 112-117.

Eichi H.R. and M.-Y. Chow. 2012. Modeling and analysis of battery hysteresis effects. In 2012 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 4479-4486.