Lithium-ion batteries have been rapidly developed as clean energy sources in many industrial fields, such as new energy vehicles and energy storage. The core issues hindering their further promotion and application are reliability and safety. A digital twin model that maps onto the physical entity of the battery with high simulation accuracy helps to monitor internal states and improve battery safety. This work focuses on developing a digital twin model via a mechanism-data-driven parameter updating algorithm to increase the simulation accuracy of the internal and external characteristics of the full-time domain battery under complex working conditions. An electrochemical model is first developed with the consideration of how electrode particle size impacts battery characteristics. By adding the descriptions of temperature distribution and particle-level stress, a multi-particle size electrochemical-thermal-mechanical coupling model is established. Then, considering the different electrical and thermal effect among individual cells, a model for the battery pack is constructed. A digital twin model construction method is finally developed and verified with battery operating data.

Micro short circuit (MSC) fault diagnosis is thought functional in preventing thermal runaway of lithium-ion battery packs. Inconsistencies in the initial state-of-charge and aging state inevitably exist among cells of a battery pack. The existing method for MSC diagnosis disregards the symptoms originating from cell-to-cell inconsistency, which may lead to misdiagnosing inconsistent cells as MSC cells and vice versa. This work presents a method for detecting and quantitatively diagnosing MSC faults in lithium-ion battery packs, while taking cell inconsistency into consideration. Initially, the median incremental capacity (IC), derived based on ranking the terminal voltages of cells, is used as a benchmark representing the state of normal cells. Subsequently, the correlation coefficients between the ICs of individual cells and their median IC are calculated in both the time and frequency domains, as to distinguish the normal, inconsistent, and MSC cells. After detecting the MSC cell, an algorithm, which is based on a recursive least squares algorithm with forgetting factor and an adaptive H_∞ Kalman filtering, is designed to calculate the short-circuit resistance online. The experimental results demonstrate that the short-circuit resistance estimated by the proposed algorithm exhibits rapid convergence to the actual values, thereby confirming the utility of the proposed algorithm in real-life contexts.