[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. |