Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter March 20, 2018

Estimation of Battery Soc for Hybrid Electric Vehicle using Coulomb Counting Method

Bachir Zine EMAIL logo , Khoudir Marouani , Mohamed Becherif and Said Yahmedi

Abstract:

The autonomy of the Battery Electric Vehicle is a key point in the development and commercialization of this kind of vehicle. The requested autonomy is directly linked to the amount of the stored and remaining energy in the battery which is the State of Charge (SOC).This paper presents battery state of charge (SOC) estimation using coulomb counting method. So, the quantity of electric charge is calculated during the battery cycle of charge and discharge and compared to the estimated value based on the battery generic model. Also, experimental results are carried out in order to validate this study.

Appendix

The list of abbreviations and symbols is provided in Table 4.

Table 4:

List of abbreviations and symbols.

Symbols/AbbreviationsMeaning
SOCState of Charge
AhAmpere hour
OCVOpen Circuit Voltage
ACAlternatif Current
SVMSupport Vector Machine
SOHState of Health
HEVHybrid Electric Vehicle
CCConstant Current
CVConstant Voltage
EMElectro-Magnetic
I(τ)The current versus time (negative during charge and positive during discharge)
VbattThe battery voltage (V)
E0The constant battery voltage (V)
KThe polarization constant (V/(Ah)) or polarization resistance (Ω)
QThe battery capacity (Ah)
it= ∫Idt: Actual battery charge (Ah)
AThe exponential voltage (V)
BThe exponential capacity (Ah) -1
RThe internal resistance (Ω)
IThe battery current (A)
i*The filtered current (A)
TAmbiant temperature
IdisThe value of the current of discharge
SOC0Initial State of Charge
SOCminMinimum State of Charge
SOCexpExperimental State of Charge
SOCthTheoretical State of Charge
VchintInitial charge voltage
VchendEnd charge voltage
TchsumSimulation charge time
VdisintInitial discharge voltage
VdisendEnd discharge voltage
TdissumSimulation discharge time

References

[1] Alloui H, Becherif M, Marouani K. Modelling and frequency separation energy management of fuel Cell-Battery Hybrid sources system for Hybrid Electric Vehicle. 21st Mediterranean Conference on Control and Automation. 2013;2013:646–51. DOI: 10.1109/MED.2013.6608791Search in Google Scholar

[2] Tang X, Liu B, Gao F, Lv Z. State-of-Charge estimation for Li-Ion power batteries based on a tuning free observer. Energies. 2016;9:675.10.3390/en9090675Search in Google Scholar

[3] Leksono E, Haq IN, Iqbal M, Soelami FXN, Merthayasa IGN. 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;2013. DOI: 10.1109/rICT-ICeVT.2013.6741545Search in Google Scholar

[4] Becherif M, Claude F, Hervier T, Boulon L. Multi-stack fuel cells powering a vehicle. In: International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, Energy Procedia, 74.308–19, 2015.10.1016/j.egypro.2015.07.613Search in Google Scholar

[5] Elabadine DZ, Ali M, Mourad H. A novel hybrid technique to predict the Lithium-Ion battery’s behavior and estimate the intern impedance. International Journal of Emerging Electric Power Systems. 2017;18(4):2016025110.1515/ijeeps-2016-0251Search in Google Scholar

[6] Chukwu UC, Mahajan SM. Impact assessment of V2G on the power loss of unbalanced radial distribution network. International Journal of Emerging Electric Power Systems. 2013;14(5):401–10.10.1515/ijeeps-2012-0012Search in Google Scholar

[7] Ng KS, 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. Applied Energy. 2009;86:1506–11.10.1016/j.apenergy.2008.11.021Search in Google Scholar

[8] Ausswamaykin A, Plangklang B. Design of real time battery management unit for PV-Hybrid system by application of coulomb counting method. Energy and Power Engineering. 2014;6:186–93.10.4236/epe.2014.67017Search in Google Scholar

[9] Weng C, Sun J, Peng H. A unified open-circuit-voltage model of Lithium-Ion batteries for state-of-charge estimation and state-of-health monitoring. Journal of Power Sources. 2014;258:228–37.10.1016/j.jpowsour.2014.02.026Search in Google Scholar

[10] Dilip PA. Intelligent battery monitoring system with State of Charge estimation using artificial neural network. Ijifr. 2016;3(6):2034–40Search in Google Scholar

[11] Hussein AA. Derivation and comparison of Open-loop and Closed-loop neural network battery State-of-Charge estimators. In: the 7th International Conference on Applied Energy – ICAE, 2015.10.1016/j.egypro.2015.07.163Search in Google Scholar

[12] Cristín Valdez MA, Orozco Valera JA, Pacheco Arteaga MJO. Estimating SOC in Lead-Acid batteries using neural networks in a microcontroller-based charge controller. In: IEEE international joint conference on neural networks, Canada, July-16-21, 2006.10.1109/IJCNN.2006.247175Search in Google Scholar

[13] Wang Z, Xu J, Wang T. The online monitoring system software design and the SOC estimation algorithm research for power battery. Proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety. 2013. DOI: 10.1109/ICVES.2013.6619609Search in Google Scholar

[14] Hussein AA. Capacity fade estimation in electric vehicles li-ion batteries using artificial neural networks. IEEE Transactions on Industry Applications. 2015;51(3):2321–30. DOI:10.1109/TIA.2014.2365152Search in Google Scholar

[15] Ananto P, Syabani F, Indra WD, Wahyunggoro O, Cahyadi AI. The state of health of Li-Po batteries based on the battery’s parameters and a fuzzy logic system. 2013 Joint International Conference on Rural Information & Communication Technology and Electric-Vehicle Technology (rICT & ICeV-T). 2013. DOI: 10.1109/rICT-ICeVT.2013.6741508Search in Google Scholar

[16] Bhangu BS, Bentley P, Stone DA, Bingham CM. Observer techniques for estimating the State-of-Charge and State-of-Health of VRLABs for Hybrid Electric Vehicles. 2005 IEEE Vehicle Power and Propulsion Conference. 2005. DOI: 10.1109/VPPC.2005.1554646Search in Google Scholar

[17] Li Y, Wang L, Liao C, Wang L, Xu D. State-of-Charge estimation of Lithium-Ion battery using multi-state estimate technic for electric vehicle applications. 2013 IEEE Vehicle Power and Propulsion Conference (VPPC). 2013. DOI: 10.1109/VPPC.2013.6671711Search in Google Scholar

[18] Sepasi S, Ghorbani R, Liaw BY. SOC estimation for aged lithium-ion batteries using model adaptive extended Kalman filter. 2013 IEEE Transportation Electrification Conference and Expo (ITEC). 2013. DOI: 10.1109/ITEC.2013.6573479Search in Google Scholar

[19] Surendar V, Mohankumar V, Anand S, Prasanna VD. Estimation of State of Charge of a Lead Acid battery using support vector regression. Procedia Technology. 2015;21:264–70.10.1016/j.protcy.2015.10.026Search in Google Scholar

[20] Anton JC, Nieto PJ, Viejo CB, Vilán JA. Support vector machines used to estimate the battery State of Charge. IEEE Transactions On Power Electronics. 2013;28:12.Search in Google Scholar

[21] Xu J, Mi CC, Cao B, Deng J, Chen Z, Li S. The State of Charge estimation of Lithium-Ion batteries based on a proportional-integral observer. IEEE Transactions On Vehicular Technology. 2014;63:4.Search in Google Scholar

[22] Wang H, Xue C, Fan Q, Liu P. Lithium battery SOC estimation method study based on principal component analysis. International Journal of Control and Automation. 2015;8(7):20.10.14257/ijca.2015.8.7.20Search in Google Scholar

[23] Montenegro D, Rodriguez S, Fuelagan JR, Jimenez JB. An estimation method of state of charge and lifetime for lead-acid batteries in smart grid scenario. 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM) 2015. DOI: 10.1109/ISGT-LA.2015.7381216Search in Google Scholar

[24] Xuyun F, Zechang S. A battery model including hysteresis for State-of-Charge estimation in Ni-MH Battery. In: IEEE Vehicle Power and Propulsion Conference, September 3-5, Harbin, China, 2008.Search in Google Scholar

[25] Xiao R, Shen J, Li X, Yan W, Pan E, Chen Z. Comparisons of modeling and State of Charge estimation for Lithium-Ion battery based on fractional order and integral order methods. Energies. 2016;9:184.10.3390/en9030184Search in Google Scholar

[26] Sato S, Kawamura A. A new estimation method of state of charge using terminal voltage and internal resistance for lead acid battery. Proceedings of the Power Conversion Conference-Osaka 2002 (Cat. No.02TH8579). 2002. DOI: 10.1109/PCC.2002.997578Search in Google Scholar

[27] Feng F, Lu R, Zhu C. A combined State of Charge estimation method for Lithium-Ion batteries used in a wide ambient temperature range. Energies. 2014;7:3004–32.10.3390/en7053004Search in Google Scholar

[28] Purwadi A, Rizqiawan A, Kevin A, Heryana N. State of Charge estimation method for Lithium battery using combination of coulomb counting and adaptive system with considering the effect of temperature. In: IEEE Conference on Power Engineering and Renewable Energy, 2014.10.1109/ICPERE.2014.7067233Search in Google Scholar

[29] Kim J, Lee S, Cho B. The State of Charge estimation employing empirical parameters measurements for various temperatures. 2009 IEEE 6th International Power Electronics and Motion Control Conference 2009. DOI: 10.1109/IPEMC.2009.5157518Search in Google Scholar

[30] Yan X, Yang Y, Guo Q, Zhang H, Qu W. Electric vehicle battery SOC estimation based on fuzzy Kalman filter. 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA). 2013. DOI: 10.1109/IMSNA.2013.6743414Search in Google Scholar

[31] Puranik SV, Chakrasali S. Comparative study on SOC estimation techniques for optimal battery sizing for hybrid vehicles. International Journal on Recent and Innovation Trends in Computing and Communication. 2015;3(4):1957–63.10.17762/ijritcc2321-8169.150444Search in Google Scholar

[32] Jiang J and Zhang C. Fundamentals and applications of Lithium-Ion batteries in electric drive vehicles, chapter 1, 2015.10.1002/9781118414798Search in Google Scholar

[33] Bergveld HJ, Kruijt WS, Notten PHL. Battery management systems. Battery management systems–design by modelling. Dordrecht: Springer, 2002:9–30.10.1007/978-94-017-0843-2_2Search in Google Scholar

[34] Pop V, Bergveld HJ, Danilov D, Regtien PPL, Notten PHL. Battery management systems; accurate State-of-Charge indication for battery-powered applications. Vol. 9. Netherlands: Springer Science & Business Media, 2008.10.3390/wevj1010038Search in Google Scholar

[35] DinhVinh DO. Lithium-Ion batteries diagnosis in embedded applications. Thesis for obtaining the degree of doctor of Compiegne University of Technology in Information Technology and Systems; Backed July 5, 2010.Search in Google Scholar

[36] Xiaosong FS, Zou Y. Estimation of State of Charge of a Lithium-Ion battery pack for electric vehicles using an adaptive Luenberger observer. Energies. 2010;3:1586–603.10.3390/en3091586Search in Google Scholar

[37] Lindner D, Niedermayr F. Report for work package 6, FRAUNHOFR - ITALIA; September 29, 2014.Search in Google Scholar

[38] Meskani A, Haddi A, Becherif M. Modeling and simulation of a hybrid energy source based on solar energy and battery. International Journal of Hydrogen Energy. 2015;40(39):13702–1370710.1016/j.ijhydene.2015.03.098Search in Google Scholar

[39] Vyroubal P, Maxa J, Kazda T. Simulation of the behavior of the Lithium-Ion battery. Advances in Military Technology. 2014;9(1):107–15.Search in Google Scholar

[40] Nassor TS, Senjyu T, Yona A. Enhancement of voltage stability of DC smart grid during islanded mode by load shedding scheme. Ijeeps. 2015;16(5):491–501.10.1515/ijeeps-2015-0016Search in Google Scholar

[41] Xu S, Yan Z, Zhao X, Zhang L, Feng D, Xu X. Decentralized charging of plug-in electricvehicles using Lagrange relaxation method at the residential transformer level. Ijeeps. 2016;17(3):267–76.10.1515/ijeeps-2015-0148Search in Google Scholar

[42] Chauhan RK, Rajpurohit BS, Gonzalez-Longatt FM, Singh SN. Intelligent energy management system for PV-battery-based microgrids in future DC homes. Ijeeps. 2016;17(3):339–50.10.1515/ijeeps-2015-0210Search in Google Scholar

[43] Becherif M, Ayad MY, Hissel D, Mkahl R. Design and sizing of a stand-alone recharging point for battery electrical vehicles using photovoltaic energy. 2011 IEEE Vehicle Power and Propulsion Conference. 2011. DOI: 10.1109/VPPC.2011.6043075Search in Google Scholar

[44] Melentjev S, Lebedev D. Overview of simplified mathematical models of batteries. Tallinn University of Technology (Estonia), Publication of Doctoral School of Energy and Geotechnology, Pärnu, 2013.Search in Google Scholar

Received: 2017-8-30
Revised: 2018-1-22
Accepted: 2018-1-28
Published Online: 2018-3-20

© 2018 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 27.1.2023 from https://www.degruyter.com/document/doi/10.1515/ijeeps-2017-0181/html
Scroll Up Arrow