Identification of ohmic internal resistance of batteries based on cloud data

Abstract

The accurate identification of ohmic internal resistance has a great influence on the estimation of battery state, which is an objective requirement of battery management system (BMS). This paper proposes an ohmic internal resistance calculation method by transforming the low-rate charge voltage curve of cloud data. Firstly, the characteristics of ohmic internal resistance of two type lithium-ion batteries are analyzed under different working conditions. Then, the ohmic internal resistances are identified from the discharge segments of cloud data based on the analogous hybrid pulse power characterization (HPPC) method. Results show that the ohmic internal resistance calculated by the analogous HPPC method is larger due to the effect of sampling period. Subsequently, three universal open circuit voltage (OCV) solution methods are discussed, and the polarization and hysteresis characteristics of the calculated OCV curves are analyzed. Further, an ohmic internal resistance calculation method is proposed, which uses the OCV curve measured by the small charging current method as a baseline. Finally, the ohmic internal resistances are calculated based on the proposed method and compared with the results obtained in the laboratory. Results show that the absolute error of the calculated ohmic internal resistance is 0.085 mΩ and 0.11 mΩ for the two type batteries, which verifies the accuracy and applicability of the proposed method.