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Online Parameter Estimation of the Ni-MH Batteries Based on Statistical Methods

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  • Chang-hao Piao

    (Key Laboratory of Network control & Intelligent Instrument, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065 China
    Research Center of Energy Electronics, College of Automation Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065 China)

  • Wen-li Fu

    (Research Center of Energy Electronics, College of Automation Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065 China)

  • Gai-hui Lei

    (Research Center of Energy Electronics, College of Automation Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065 China)

  • Chong-du Cho

    (Department of mechanical Engineering, INHA University of Korea, Incheon, 402751, Korea)

Abstract

Based on the application of the power batteries, this paper uses a statistical method to estimate the internal resistance and open-circuit voltage of Ni-MH battery. Battery status is monitored and simulated by battery pack test bench. Through using ideal battery model and fitting the data of measured voltage and current, the battery internal resistance and open-circuit voltage are estimated. The average relative error between battery statistic internal resistance and pulse internal resistance is less than 15% in different state of charge. Relative error is influenced by dispersion and symmetry of charge or discharge current. Average of absolute error in open-circuit voltage is about 5% respectively. The results show that it is feasible and accurate to estimate the parameters of Ni-MH battery by using statistical method.

Suggested Citation

  • Chang-hao Piao & Wen-li Fu & Gai-hui Lei & Chong-du Cho, 2010. "Online Parameter Estimation of the Ni-MH Batteries Based on Statistical Methods," Energies, MDPI, vol. 3(2), pages 1-10, February.
    • Handle: RePEc:gam:jeners:v:3:y:2010:i:2:p:206-215:d:7105
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    References listed on IDEAS

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    1. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    2. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    3. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    4. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 1.
    5. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    6. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    7. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
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    Cited by:

    1. Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
    2. Xian Wang & Zhengxiang Song & Kun Yang & Xuyang Yin & Yingsan Geng & Jianhua Wang, 2019. "State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries," Energies, MDPI, vol. 12(1), pages 1-22, January.
    3. Daehyun Kim & Keunhwi Koo & Jae Jin Jeong & Taedong Goh & Sang Woo Kim, 2013. "Second-Order Discrete-Time Sliding Mode Observer for State of Charge Determination Based on a Dynamic Resistance Li-Ion Battery Model," Energies, MDPI, vol. 6(10), pages 1-14, October.
    4. Muhammad Umair Ali & Muhammad Ahmad Kamran & Pandiyan Sathish Kumar & Himanshu & Sarvar Hussain Nengroo & Muhammad Adil Khan & Altaf Hussain & Hee-Je Kim, 2018. "An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method," Energies, MDPI, vol. 11(11), pages 1-19, October.

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