Robust and Rapid Diagnosis of Early Internal Short Circuits in Lithium‐Ion Batteries Based on an Improved Circuit Model

ABSTRACT

Internal short circuits (ISCs) in lithium‐ion batteries (LIBs) pose a significant safety threat, necessitating rapid and reliable diagnostic methods. This paper proposes an improved diagnostic model that leverages the electrochemical characteristics of the constant current constant voltage (CCCV) charging phase. By using the pseudo‐steady‐state properties of the constant current stage, the proposed model reduces the system state vector from four dimensions to two dimensions and directly observes the short‐circuit current as a state variable. A Recursive Least Squares (RLS) algorithm is then employed to identify the short‐circuit resistance while suppressing sensor‐noise interference. Experimental results show that the proposed method substantially shortens the detection time and improves resistance‐identification accuracy compared with the traditional second‐order RC‐model‐based method. In addition, the proposed model reduces the theoretical computational cost from 553 to 129 FLOPs per iteration, supporting online implementation in battery management systems.