A Balancing Current Ratio based State-of-Health Estimation Solution for Lithium-ion Battery Pack

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

17 Scopus Citations
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  • Xiaopeng Tang
  • Furong Gao
  • Kailong Liu
  • Qi Liu
  • Aoife M. Foley

Related Research Unit(s)


Original languageEnglish
Pages (from-to)8055-8065
Number of pages11
Journal / PublicationIEEE Transactions on Industrial Electronics
Issue number8
Online published3 Sep 2021
Publication statusPublished - Aug 2022


The inevitable battery ageing is a bottleneck that hinders the advancement of battery-based energy storage systems. Developing a reliable health assessment strategy for battery pack is important but challenging when the joint requirements of computational burden, modelling cost, estimation accuracy, and battery equalisation are considered. This paper proposes a balancing current ratio (BCR)-based solution to realise satisfactory state-of-health (SoH) estimation performance of all series-connected cells within a pack, while the overall reliance on cell-level battery models is also reduced. Specifically, after employing BCR to describe the properties of the balancing process, the voltage-based active balancing is combined into the SoH estimator design for the first time, leading to a weighted fusion strategy for effectively estimating SoHs of all cells within a pack. Hardware-in-the-loop experiments show that even if a parameter-fixed open-circuit-voltage-resistance (OCV-R) model is adopted, the typical estimation error of our proposed solution can still be bounded within only 1.5%, which is 70% lower than that of the benchmarking algorithms. Due to the model-free nature of the integrated voltage-based balancing, the robustness and flexibility of the proposed pack SoH estimation solution are also improved.

Research Area(s)

  • Aging, Balancing current ratio (BCR), Batteries, Battery charge measurement, Computational modeling, Current measurement, Electric Vehicle, Estimation, Lithium-ion Battery Pack, Sensors, State-of-health (SoH) Estimation