Integration of Coulomb Counting Method in Battery Management System for Electric Vehicle

Download Full-Text PDF Cite this Publication

Text Only Version

Integration of Coulomb Counting Method in Battery Management System for Electric Vehicle

Mrs. Rakshitha. R

P.hd Scholar, Christ university; Assistant Professor RVITM

Abstract : The coulomb counting (CC) approach is widely utilized in SOC estimation thanks to its simplicity and low calculation cost. However, in practical applications, the shortage of error correction ability limits its accuracy thanks to the measured noise within the practical occasion. To address the difficulty , an improved CC (ICC) approach supported numerical iteration is proposed during this paper. In the proposed approach, A battery model supported a 2nd- order, RC circuit is first formulated to work out the SOC- OCV curve, R-OCV curve, and inner parameters.

Index Terms: Improved coulomb counting (ICC), state of charge (SOC), accumulative error correction, numerical iteration, error accumulation rate.


    To cut fossil energy consumption and mitigate the greenhouse effect, electric vehicle (EV) has been widely concerned.



    1. BATTERY MODEL The nth-order Thevenin model is widely used for SOC estimation because of its relatively high accuracy and computation efficiency compared with other models [1]. dynamic voltage of the where, k denotes the present step, k-1 denotes the previous step.

    2. OFF-LINE PARAMETERS IDENTIFICATION To verify the performance of the proposed algorithm, an 18650 Li-ion battery with the nominal capacity of 3Ah and rated voltage of 3.6V was modeled.

    1. IDENTIFICATION OF SOC-OCV CURVE:The hybrid pulse power characterization (HPPC) test with one hour interval and 5% SOC whenever was administered [27].

    2. DENTIFICATION OF R-OCV CURVE In the paper, the R-OCV curve will be utilized in the numerical iteration approach, which will be Generally, the lumped resistance R defined in (5) can be calculated through the identification ofro,rp1 and rp2, respectively. where, I(k) and Uo(k) are measured based on accurate measuring instrument.

    3. IDENTIFICATION OF INNER PARAMETERS: the opposite inner parameters are identified supported HPPC test also .




    The principle of conventional CC approach is shown in (13). Due to the measured noise in I(k), an increasing accumulative error are going to be introduced into the SOC. Therefore, following criterion could be formulated: where, s1 and s2 are defined as the steady coefficients, reflecting the divergence degree of the dynamic voltages from steady state.

  2. OCV ESTIMATION USING NUMERICAL ITERATION A simple iteration algorithm is employed for OCV estimation and then the SOC could be estimated directly through SOC OCV curve. The equation to be solved needs to be deformed into (16) firstly, where 8(x) is named as iterative function.

  3. COMPENSATION COEFICIENT TO PREVENT ERROR ACCUMULATION IN CC APPROACH Assuming that numeral iteration happens at ki . Therefore, the error accumulation rate ?? can be estimated by following equation.

DESIGN OF ALGORITHMS USED: Algorithms used for SOC and SOH Estimation are:

1. Coulomb Counting Method.

The Coulomb counting method is associated with monitoring the input and the output current continuously. Enhanced Coulomb Counting Algorithm:In order to beat the shortcomings of the coulomb counting method and to enhance its estimation accuracy, an enhanced coulomb counting algorithm has been proposed for estimating the SOC and SOH parameters of Li-ion batteries. Technical Principle The releasable capacity (Creleasable), of an operating battery is that the released capacity when it's completely discharged. When A battery is discharging, the depth of discharge (DOD) are often expressed because the percentage of the capacity that has been discharged relative to Crated, where Creleased is that the capacity discharged by any amount of current. As time elapsed, the DOD is accumulated. DOD(t) = DOD(t0) + ???DOD DOD(t) = DOD(t0) + ?????DOD with ?? adequate to ??c during charging stage and adequate to ??d during discharging stage. SOC(t) = 100% ??? DOD(t) Considering the SOH, the SOC is estimated as SOC(t) = SOH(t) ??? DOD(t) .

simple calculation and therefore the uncomplicated hardware requirements, the improved coulomb counting algorithm are often easily implemented altogether portable devices, also as electric vehicles. In addition, the estimation error can be reduced to 1% at the operating cycle next to the reevaluation of the SOH.


In the paper, an ICC approach with real-time error correction ability is proposed. Furthermore, a compensation coefficient ?? is employed into the CC approach to reduce the erroraccumulation rate. Experimental results suggest that the SOC error of ICC is effectively limited within 1% and its calculation cost is 94% lower than that of EKF. Therefore, it provides beneficial guidance for the real-time SOC estimation in EVs.


  1. Z. Li, J. Huang, B. Y. Liaw, and J. Zhang, On state-of-charge determination for lithium-ion batteries, J. Power Sources, vol. 348, pp. 281301, Apr. 2017.

  2. W.Yan,B.Zhang,G.Zhao,S.Tang,G.Niu,andX.Wang, Abatterymanagementsystemwithalebesgue-sampling- basedextendedKalman???lter,. IEEE Trans. Ind. Electron., vol. 66, no. 4, pp. 32273236, Apr. 2019.

  3. C. Huang, Z. Wang, Z. Zhao, L. Wang, C. S. Lai, and D. Wang,

    .Robustness evaluation of extended and unscented Kalman ???lter for battery state of charge estimation,. IEEE Access, vol. 6, pp. 27617???27628, 2018

  4. S. Piller, M. Perrin, and A. Jossen, .Methods for state-of-charge determination and their applications,. J. Power Sources, vol. 96, no. 1, pp. 113???120, Jun. 2001.

  5. 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,. Appl. Energy, vol. 86, no. 9, pp. 1506???1511, Sep. 2009.

  6. S.G.Li,S.M.Sharkh,F.C.Walsh,andC.N.Zhang,.Energyandbattery management of a plug-in series hybrid electric vehicle using symbolic logic ,. IEEE Trans. Veh. Technol., vol. 60, no. 8, pp. 3571???3585, Oct. 2011.

  7. J. Chen, O. Ouyang, C. Xu, and H. Su, .Neural network-based state of charge observer design for lithium-ion batteries,. IEEE Trans. Control Syst. Technol., vol. 26, no. 1, pp. 313???320, Jan. 2018.

  8. E. Chemali, P. J. Kollmeyer, M. Preindl, R. Ahmed, and A. Emadi, .Long short-term memory networks for accurate state-of- charge estimation of Li- ionbatteries,.IEEETrans.Ind.Electron.,vol.65,no.8,pp.6730???673 9, Aug. 2018.

Leave a Reply

Your email address will not be published. Required fields are marked *