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Battery remaining useful life prediction at different discharge rates

  • Dong Wang
  • , Fangfang Yang*
  • , Yang Zhao
  • , Kwok-Leung Tsui
  • *Corresponding author for this work

    Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

    Abstract

    Lithium-ion batteries are widely used in hybrid electric vehicles, consumer electronics, etc. As of today, given a room temperature, many battery prognostic methods working at a constant discharge rate have been proposed to predict battery remaining useful life (RUL). However, different discharge rates (DDRs) affect both usable battery capacity and battery degradation rate. Consequently, it is necessary to take DDRs into consideration when a battery prognostic method is designed. In this paper, we propose a discharge-rate-dependent battery prognostic method that is able to track usable battery capacity affected by DDRs in the process of battery degradation and to predict RUL at DDRs. An experiment was designed to collect accelerated battery life testing data at DDRs, which are used to investigate how DDRs influence usable battery capacity, to design a discharge-rate-dependent state space model and to validate the effectiveness of the proposed battery prognostic method. Results show that the proposed battery prognostic method can work at DDRs and achieve high RUL prediction accuracies at DDRs.
    Original languageEnglish
    Pages (from-to)212-219
    JournalMicroelectronics Reliability
    Volume78
    Online published14 Sept 2017
    DOIs
    Publication statusPublished - Nov 2017

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy

    Research Keywords

    • Different discharge rates
    • Lithium-ion batteries
    • Particle filter
    • Prognostics and health management
    • Remaining useful life

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