Online Estimation of Internal Short Circuit Resistance for Large-Format Lithium-Ion Batteries Combining a Reconstruction Method of Model-Predicted Voltage

Abstract

The resistance of the internal short-circuit (ISC) has a potential evolution trend accompanied by an increasing safety risk. Thus, an accurate online resistance estimation for the ISC is crucial for evaluating its safety risk and taking staged handling measures. Since the ISC battery mainly presents abnormal stage of charge (SOC) depletion behaviors, the SOC estimation processes based on state observers and battery models will act an important basis of the ISC resistance estimation problem. However, as it will be exhibited in this paper, when directly using the measured voltage of the ISC battery as the output variable of the state observer, the battery model error will limit the SOC estimation accuracy and further lead to very inaccurate or even divergent ISC resistance estimation results for large-format batteries, which present quite slight SOC depletion behaviors at the ISC state. To this end, this paper proposes a novel SOC and ISC resistance co-estimation method which combines a reconstruction method of the model-predicted voltage of the ISC battery. Experimental validations are carried out with a 37 Ah battery, results show that the proposed method which uses the reconstructed model-predicted voltage (RMPV) as the output variable of the state observer only present maximum estimation errors of 39.96 Ω and 2.00 Ω for the ISC resistances of 100 Ω and 10 Ω, respectively.