Optimizing Current Profiles for Efficient Online Estimation of Battery Equivalent Circuit Model Parameters Based on Cramer-Rao Lower Bound

被引:7
|
作者
Pillai, Prarthana [1 ]
Sundaresan, Sneha [1 ]
Pattipati, Krishna R. [2 ]
Balasingam, Balakumar [1 ]
机构
[1] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada
[2] Univ Connecticut, Dept Elect & Comp Engn, Storrs, CT 06269 USA
基金
加拿大自然科学与工程研究理事会;
关键词
battery management system; battery equivalent circuit model; least squares estimation; battery internal resistance; Cramer-Rao lower bound; OF-CHARGE ESTIMATION; PREDICTION;
D O I
10.3390/en15228441
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Battery management systems (BMS) are important for ensuring the safety, efficiency and reliability of a battery pack. Estimating the internal equivalent circuit model (ECM) parameters of a battery, such as the internal open circuit voltage, battery resistance and relaxation parameters, is a crucial requirement in BMSs. Numerous approaches to estimating ECM parameters have been reported in the literature. However, existing approaches consider ECM identification as a joint estimation problem that estimates the state of charge together with the ECM parameters. In this paper, an approach is presented to decouple the problem into ECM identification alone. Using the proposed approach, the internal open circuit voltage and the ECM parameters can be estimated without requiring the knowledge of the state of charge of the battery. The proposed approach is applied to estimate the open circuit voltage and internal resistance of a battery.
引用
收藏
页数:21
相关论文
共 48 条
  • [31] Cramer-Rao Lower Bound for A/D and D/A Converters Linearity Testing Performance of integral nonlinearity estimation with Gaussian and sinusoidal test signals
    Lanzolla, Anna Maria Lucia
    Di Nisio, Attilio
    Giaquinto, Nicola
    Savino, Mario
    2010 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE I2MTC 2010, PROCEEDINGS, 2010,
  • [32] Parameter identification of lithium-ion battery pack for different applications based on Cramer-Rao bound analysis and experimental study
    Song, Ziyou
    Hofmann, Heath
    Lin, Xinfan
    Han, Xuebing
    Hou, Jun
    APPLIED ENERGY, 2018, 231 : 1307 - 1318
  • [33] On the True Cramer-Rao Lower Bound for Data-Aided Carrier-Phase-Independent Frequency Offset and Symbol Timing Estimation
    Tavares, Goncalo N.
    Tavares, Luis M.
    Petrolino, Antonio
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2010, 58 (02) : 442 - 447
  • [34] Optimizing Proton Stopping Power Ratio Prediction with Dual-Energy Cone-Beam CT Using the Cramer-Rao Lower Bound
    Leibold, David
    Schaart, Dennis R.
    Goorden, Marlies C.
    MEDICAL IMAGING 2024: PHYSICS OF MEDICAL IMAGING, PT 1, 2024, 12925
  • [35] Estimation of Equivalent Circuit Model Parameters for a Generic Battery Using Least-Square Method
    Das, Sourabh
    Samanta, Susovon
    2022 IEEE INTERNATIONAL CONFERENCE ON POWER ELECTRONICS, DRIVES AND ENERGY SYSTEMS, PEDES, 2022,
  • [36] Online reduced complexity parameter estimation technique for equivalent circuit model of lithium-ion battery
    Saleem, Komal
    Mehran, Kamyar
    Ali, Zunaib
    ELECTRIC POWER SYSTEMS RESEARCH, 2020, 185
  • [37] A review of sliding mode observers based on equivalent circuit model for battery SoC estimation
    Sui, Xin
    He, Shan
    Stroe, Daniel-Ioan
    Huang, Xinrong
    Meng, Jinhao
    Teodorescu, Remus
    2019 IEEE 28TH INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE), 2019, : 1965 - 1970
  • [38] State of power estimation of power lithium-ion battery based on an equivalent circuit model
    Wu, Muyao
    Qin, Linlin
    Wu, Gang
    JOURNAL OF ENERGY STORAGE, 2022, 51
  • [39] Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction
    Feng, Tianheng
    Yang, Lin
    Zhao, Xiaowei
    Zhang, Huidong
    Qiang, Jiaxi
    JOURNAL OF POWER SOURCES, 2015, 281 : 192 - 203
  • [40] Estimation of lithium-ion battery electrochemical properties from equivalent circuit model parameters using machine learning
    Nicodemo, Niccolo
    Di Rienzo, Roberto
    Lagnoni, Marco
    Bertei, Antonio
    Baronti, Federico
    JOURNAL OF ENERGY STORAGE, 2024, 99