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Prediction of State-of-Health for Nickel-Metal Hydride Batteries by a Curve Model Based on Charge-Discharge Tests

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  • Huan Yang

    (School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Key laboratory of Material Chemistry for Energy Conversion and Storage, Huazhong University of Science and Technology, Ministry of Education, Wuhan 430074, China)

  • Yubing Qiu

    (School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Xingpeng Guo

    (School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Key laboratory of Material Chemistry for Energy Conversion and Storage, Huazhong University of Science and Technology, Ministry of Education, Wuhan 430074, China)

Abstract

Based on charge-discharge cycle tests for commercial nickel-metal hydride (Ni-MH) batteries, a nonlinear relationship is found between the discharging capacity ( C discharge , Ah) and the voltage changes in 1 s occurring at the start of the charging process (Δ V charge , mV). This nonlinear relationship between C discharge and Δ V charge is described with a curve equation, which can be determined using a nonlinear least-squares method. Based on the curve equation, a curve model for the state-of-health (SOH) prediction is constructed without battery models and cycle numbers. The validity of the curve model is verified using ( C discharge , Δ V charge ) data groups obtained from the charge-discharge cycle tests at different rates. The results indicate that the curve model can be effectively applied to predict the SOH of the Ni-MH batteries and the best prediction root-mean-square error (RMSE) can reach upto 1.2%. Further research is needed to confirm the application of this empirical curve model in practical fields.

Suggested Citation

  • Huan Yang & Yubing Qiu & Xingpeng Guo, 2015. "Prediction of State-of-Health for Nickel-Metal Hydride Batteries by a Curve Model Based on Charge-Discharge Tests," Energies, MDPI, vol. 8(11), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:11:p:12322-12487:d:58247
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    References listed on IDEAS

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    1. Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
    2. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    3. Yinjiao Xing & Eden W. M. Ma & Kwok L. Tsui & Michael Pecht, 2011. "Battery Management Systems in Electric and Hybrid Vehicles," Energies, MDPI, vol. 4(11), pages 1-18, October.
    4. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
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    Cited by:

    1. Chen, Kunlong & Zheng, Fangdan & Jiang, Jiuchun & Zhang, Weige & Jiang, Yan & Chen, Kunjin, 2017. "Practical failure recognition model of lithium-ion batteries based on partial charging process," Energy, Elsevier, vol. 138(C), pages 1199-1208.
    2. Bumin Meng & Yaonan Wang & Jianxu Mao & Jianwen Liu & Guochang Xu & Jian Dai, 2018. "Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat," Energies, MDPI, vol. 11(3), pages 1-20, March.
    3. Nicola Campagna & Vincenzo Castiglia & Rosario Miceli & Rosa Anna Mastromauro & Ciro Spataro & Marco Trapanese & Fabio Viola, 2020. "Battery Models for Battery Powered Applications: A Comparative Study," Energies, MDPI, vol. 13(16), pages 1-26, August.

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