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Feedback-based fault-tolerant and health-adaptive optimal charging of batteries

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  • Sattarzadeh, Sara
  • Padisala, Shanthan K.
  • Shi, Ying
  • Mishra, Partha Pratim
  • Smith, Kandler
  • Dey, Satadru

Abstract

The key technology barriers that hinder the growth of Electric Vehicles (EVs) are long charging time, the shorter life-time of EV batteries, and battery safety. Specifically, EV charging protocols have significant effects on battery lifetime and safety. If not charged properly, the battery could end up with shorter life, and more importantly, improper charging can cause battery faults leading to catastrophic failures. To overcome these barriers, we propose a closed-loop feedback based approach, that enables real-time optimal fast charging protocol adaptation to battery health and possess active diagnostic capabilities in the sense that, during charging, it detects real-time faults and takes corrective action to mitigate such fault effects. We utilize battery electrical–thermal model, explicit battery capacity and power fade aging models, and thermal fault model to capture battery behavior. In conjunction with the models, we adopt linear quadratic optimal control techniques to realize the feedback-based control algorithm. Simulation studies are presented to illustrate the effectiveness of the proposed scheme.

Suggested Citation

  • Sattarzadeh, Sara & Padisala, Shanthan K. & Shi, Ying & Mishra, Partha Pratim & Smith, Kandler & Dey, Satadru, 2023. "Feedback-based fault-tolerant and health-adaptive optimal charging of batteries," Applied Energy, Elsevier, vol. 343(C).
    • Handle: RePEc:eee:appene:v:343:y:2023:i:c:s0306261923005512
    DOI: 10.1016/j.apenergy.2023.121187
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    References listed on IDEAS

    as
    1. Jichao Hong & Zhenpo Wang & Peng Liu, 2017. "Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-16, July.
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