Effects analysis of model parameters uncertainties on battery SOC estimation using H-infinity observer

被引:0
|
作者
Li Xue [1 ]
Jiang Jiuchun [1 ]
Zhang Caiping [1 ]
Zhang Weige [1 ]
Sun Bingxiang [1 ]
机构
[1] Beijing Jiaotong Univ, Natl Act Distribut Network Technol Res Ctr NANTEC, Beijing, Peoples R China
来源
2014 IEEE 23RD INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE) | 2014年
关键词
electric vehicles; lithium-ion battery; SOC estimation; H-infinity observer; LITHIUM-ION BATTERIES; OPTIMAL SENSITIVITY; CHARGE ESTIMATION; STATE; FEEDBACK; SYSTEMS;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The accurate estimation of the state-of-charge (SOC) of battery is the basic premise for the effective energy management and the important guarantee for the safe and efficient operation in electric vehicles. To improve SOC estimation accuracy and robustness, the paper analyzes the effects of different initial SOC errors and parameters variation on SOC estimation accuracy and robustness with H-infinity observer. A model in Matlab/Simulink is established to make calculation process come true, which is based on a new battery with nominal capacity of 90Ah. The simulation and experiment al results indicate that the H-infinity observer based SOC estimation can converge to the true values quickly even if at the set maximum initial error and its steady error can be controlled within 2%. The effects of model parameters change resulted from battery degradation including SOC-OCV relationship, capacity and internal resistance on H-infinity observer are investigated. It is concluded that SOC estimation accuracy with H-infinity observer largely depends on the accurate of the curve of SOC-OCV (Open circuit voltage, OCV), which provide a foundation for battery management.
引用
收藏
页码:1647 / 1653
页数:7
相关论文
共 50 条
  • [41] Estimation of Lithium-ion Battery Model Parameters Using Experimental Data
    Santos, Rafael M. S.
    Alves, Caio L. G. de S.
    Macedo, Euler C. T.
    Villanueva, Juan M. M.
    Hartmann, Lucas V.
    2017 2ND INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION SYSTEMS, CIRCUITS AND TRANSDUCERS (INSCIT), 2017, : 143 - 148
  • [42] SOC estimation of lithium battery based on the combination of electrical parameters and FBG non-electrical parameters and using NGO-BP model
    Wang, Chen
    Wang, Yan
    Dong, Leyi
    Yao, Fengqi
    OPTICAL FIBER TECHNOLOGY, 2023, 81
  • [43] Relay Approach for Parameter Extraction of Li-ion Battery and SOC Estimation using Finite Time Observer
    Nizami, Tousif Khan
    Karteek, Yanumula Venkata
    Chakravarty, Arghya
    Alam, Nawab
    Nayak, Sisir Kumar
    2017 INDIAN CONTROL CONFERENCE (ICC), 2017, : 59 - 64
  • [44] Recursive ARMAX-Based Global Battery SOC Estimation Model Design using Kalman Filter with Optimized Parameters by Radial Movement Optimization Method
    Kaleli, Aliriza
    Akolas, Halil I.
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2023, 51 (11) : 1027 - 1039
  • [45] Adaptive observer-based H-infinity FTC for T-S fuzzy systems. Application to cart motion model
    Kharrat, Dhouha
    Gassara, Hamdi
    El Hajjaji, Ahmed
    Chaabane, Mohamed
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2020, 357 (17): : 12062 - 12084
  • [46] Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique
    Zhang, Cheng
    Allafi, Walid
    Dinh, Quang
    Ascencio, Pedro
    Marco, James
    ENERGY, 2018, 142 : 678 - 688
  • [47] Li-ion battery SOC estimation method using a Neural Network trained with data generated by a P2D model
    Kuchly, Jean
    Goussian, Alain
    Merveillaut, Mathieu
    Baghdadi, Issam
    Franger, Sylvain
    Nelson-Gruel, Dominique
    Nouillant, Cedric
    Chamaillard, Yann
    IFAC PAPERSONLINE, 2021, 54 (10): : 336 - 343
  • [48] A Novel Mechanical Analogy-Based Battery Model for SoC Estimation Using a Multicell EKF
    Paschero, Maurizio
    Storti, Gian Luca
    Rizzi, Antonello
    Mascioli, Fabio Massimo Frattale
    Rizzoni, Giorgio
    IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2016, 7 (04) : 1695 - 1702
  • [49] Soc Estimation of the Lithium-Ion Battery Pack using a Sigma Point Kalman Filter Based on a Cell's Second Order Dynamic Model
    Chi Nguyen Van
    Thuy Nguyen Vinh
    APPLIED SCIENCES-BASEL, 2020, 10 (05):
  • [50] Model Parameters Online Identification and SOC Joint Estimation for Lithium-Ion Battery Based on a Composite Algorithm
    Hong-Yu Long
    Cheng-Yong Zhu
    Bi-Bin Huang
    Chang-Hao Piao
    Ya-Qing Sun
    Journal of Electrical Engineering & Technology, 2019, 14 : 1485 - 1493