SOC Estimation with Extended KalmanFilter Based on Fuzzy Control

doi:10.16182/j.issn1004731x.joss.201801043

Abstract

Abstract: Many factors affect battery’s state of charge (SOC), such as temperature, charge/discharge rate, cycle life and so on.Extended Kalman filter (EKF) iscommonly used to estimate battery’s SOC.The observation equation’s error affects the accuracy of battery’s SOC estimationusing traditional EKF.Considering the effects of temperature and charge/discharge rateon the observation equation’s error, an SOC estimation method using EKF based on fuzzy controlis presented.Mamdani type fuzzy controller is establishedwiththe proposed method, in which temperature and charge/discharge rateare selected as thecontroller’sinput, and the observation matrix’s correction coefficient isused as thecontroller’s output to improvethe filtering process in real time.The simulationresults show that the proposed methodcan reduce the errorscoming fromthe observation equation and improve the accuracy of SOC estimation.It also has strong adaptability in practical workingcondition.

Key words: fuzzy control, extended Kalmanfilter, SOC estimation, lithium-ion battery

CLC Number:

TM912

Fang Lei, Chen Yong, Zhao Li, Yin Kangsheng, Zheng Yang. SOC Estimation with Extended KalmanFilter Based on Fuzzy Control[J]. Journal of System Simulation, 2018, 30(1): 325-331.

References

[1] Sepasi S, Roose L R, Matsuura M M.Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation[J]. Energies(S1996-1073), 2015, 8(6): 5217-5233.
[2] 王琪, 孙玉坤, 黄永红. VRLA蓄电池SOC预测的粒子群—模糊逻辑方法[J]. 电源技术, 2015, 39(12): 2656-2702.WANG Qi, SUN Yu-kun, HUANG Yong-hong. A particle swarm optimization-fuzzy logic method based on batterySOC prediction for VRLA battery [J]. Chinese Journal of Power Sources, 2015, 39(12): 2656-2702.
[3] Ala A Hussein.Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries Using Artificial Neural Networks[J]. IEEE Transactions on Industry Applications(S0093-9994), 2015, 51(3): 2321-2330.
[4] Chen Xiaopeng, Shen Weixiang, Dai Mingxiang, et al.Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles[J]. IEEE Transactions on Vehicular Technology(S0018-9545), 2016, 65(4): 1936-1947.
[5] Hossein Gholizade-Narm, Mohammad Charkhgard.Lithium-ion battery state of charge estimation based on square-root unscented Kalman filter[J]. IET Power Electronics(S1755-4535), 2013, 6(9): 1833-1841.
[6] Yu V, Headley A, Chen D.A Constrained Extended Kalman Filter for State-of-Charge Estimation of a Vanadium Redox Flow Battery With Crossover Effects[J]. Journal of Dynamic Systems Measurement &Control(S0022-0434), 2014, 136(4): 112-120.
[7] Xiong R, Sun F C, He H W.Data-driven State-of-Charge estimator for electric vehicles battery using robust extended Kalmanfilter[J]. International Journal of Automotive Technology(S1229-9138), 2014, 15(1): 89-96.
[8] Pérez G, Garmendia M, Reynaud J F, et al.Enhanced closed loop State of Charge estimator for lithium-ion batteries based on Extended Kalman Filter[J]. Applied Energy(S0306-2619), 2015, 155: 834-845.
[9] Deng Z, Yang L, Cai Y, et al.Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries[J]. Energies(S1996-1073), 2016, 9(6): 472.
[10] Sepasi S, Ghorbani R, Liaw B Y.Improved extended Kalman filter for state of charge estimation of battery pack[J]. Journal of Power Sources(S0378-7753), 2014, 255(6): 368-376.
[11] Sepasi S, Ghorbani R, Liaw B Y.A novel on-board state-of-charge estimation method for aged Li-ion batteries based on model adaptive extended Kalmanfilter[J]. Journal of Power Sources(S0378-7753), 2014, 245(1): 337-344.
[12] 王笑天, 杨志家, 王英男, 等. 双卡尔曼滤波算法在锂电池SOC估算中的应用[J]. 仪器仪表学报, 2013, 34(8): 1732-1738.Wang Xiaotian, Yang Zhijia, Wang Yingnan, et al. Application of dual extended Kalman filtering algorithm in thestate-of-charge estimation of lithium-ion battery[J]. Chinese Journal of Scientific Instrument, 2013, 34(8): 1732-1738.
[13] Xiong R, Sun F, Chen Z, et al.A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles[J]. Applied Energy(S0306-2619), 2014, 113(C): 463-476.
[14] 麻友良, 陈全世, 齐占宁. 电动汽车用电池SOC定义与检测方法[J]. 清华大学学报(自然科学版), 2001, 41(11): 95-97, 105.MA Youliang, CHEN Quanshi, QI Zhanning. Research on the SOC definition andmeasurement method of batteriesused in EVs[J]. Journal of Tsinghua University (Science and Technology),2001, 41(11): 95-97, 105.
[15] CHENG K W E, DIVAKAR B P, WU H, et al. Battery-Management System (BMS) and SOC development for electrical vehicles[J]. IEEE Transactions on Vehicular Technology(S0018-9545), 2011, 60(1): 76-88.
[16] Bumby J R, Clarke P H, Forster I.Computer modelling of the automotive energy requirements for internal combustion engine and battery electric-powered vehicles[J]. Science Measurement & Technology IEE Proceedings A(S0143-702X), 1985, 132(5): 265-279.
[17] 朱浩, 高利琴, 钱承. 动力电池SOC估算的模糊最小二乘支持向量机法[J]. 电源技术, 2013, 37 (5): 797-799.ZHU Hao, GAO Li-qin, QIAN Cheng. SOC estimation of power battery for electric car based on method offuzzy least square support vector machine[J]. Chinese Journal of Power Sources, 2013, 37(5): 797-799.