Comprehensive study and improvement of experimental methods for obtaining referenced battery state-of-power

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

5 Scopus Citations
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  • Xiaopeng Tang
  • Kailong Liu
  • Qi Liu
  • Qiao Peng
  • Furong Gao

Related Research Unit(s)


Original languageEnglish
Article number230462
Journal / PublicationJournal of Power Sources
Online published15 Sep 2021
Publication statusPublished - 15 Nov 2021


As a soft sensor, the state-of-power (SoP) estimator reveals critical information on battery-based energy storage systems. A set of reliable ‘referenced values’ is the key to evaluate the precision of such soft sensors at their designing stage and could influence the overall reliability of the battery systems. However, experimentally obtaining the ‘referenced SoP’ is non-trivial since high-current pulse tests (>10C) are required to charge/discharge the batteries to their cut-off conditions. The associated high-power experimental platforms could be expensive, while frequently applying large current at boundary conditions may leave potential safety issues. Aiming at these problems, this paper focuses on obtaining referenced SoP, rather than onboard SoP estimations. A novel equivalent discharging test is designed to accurately recover the voltage response of high-current pulses from a set of low-current tests, resulting in a 33% reduction of the peak discharging current. In addition, a flexible softmax neural network is further proposed to generate SoP values for the intervals between pulse tests. With these tools, reliable SoP values with errors lower than 0.5% can be readily obtained. The SoP obtained from our approach can be further utilised as a highly accurate benchmark to evaluate the accuracy of other onboard battery SoP estimators.

Research Area(s)

  • Battery management system, Discharging test, Electric vehicles, Lithium-ion battery, Machine learning, State-of-power