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Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC

Author

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  • Md Ashiqur Rahman

    (Department of Mechanical Engineering, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202, USA)

  • Sohel Anwar

    (Department of Mechanical Engineering, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202, USA)

  • Afshin Izadian

    (Energy Systems and Power Electronics Laboratory, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202, USA)

Abstract

Electrochemical model-based condition monitoring of a Li-Ion battery using an experimentally identified battery model and Hybrid Pulse Power Characterization (HPPC) cycle is presented in this paper. LiCoO 2 cathode chemistry was chosen in this work due to its higher energy storage capabilities. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24 h over-discharged battery, and overcharged battery. Stated battery fault conditions can cause significant variations in a number of electrochemical battery model parameters from nominal values, and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers have been used to generate the residual voltage signals in order to identify these abusive conditions. These residuals are then used in a Multiple Model Adaptive Estimation (MMAE) algorithm to detect the ongoing fault conditions of the battery. HPPC cycle simulated load profile based analysis shows that the proposed algorithm can detect and identify the stated fault conditions accurately using measured input current and terminal output voltage. The proposed model-based fault diagnosis can potentially improve the condition monitoring performance of a battery management system.

Suggested Citation

  • Md Ashiqur Rahman & Sohel Anwar & Afshin Izadian, 2017. "Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC," Energies, MDPI, vol. 10(9), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1266-:d:109877
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    References listed on IDEAS

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    1. J.-M. Tarascon & M. Armand, 2001. "Issues and challenges facing rechargeable lithium batteries," Nature, Nature, vol. 414(6861), pages 359-367, November.
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    Cited by:

    1. Xiaopeng Tang & Ke Yao & Boyang Liu & Wengui Hu & Furong Gao, 2018. "Long-Term Battery Voltage, Power, and Surface Temperature Prediction Using a Model-Based Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-16, January.

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