Fault Detection in Series Battery Modules: An Integrated Approach for Micro Short Circuits

Net-zero carbon emissions are becoming increasingly important, and the rapid development of renewable energy underscores the crucial role of energy storage systems. Among these systems, lithium-ion batteries are the dominant energy storage option. Compared to other types of batteries, lithium-ion batteries exhibit higher charge-discharge efficiency and longer lifespans, providing them with an advantage throughout the entire product lifecycle. As a result, whether it's electric vehicles or energy storage systems, they are typically equipped with lithium-ion battery packs. Battery management systems play a significant role in these lithium-ion battery packs. Improper usage of batteries, such as excessive charge-discharge cycling, can lead to the failure of the insulation layer between electrodes, resulting in micro short circuits within the batteries. When such a phenomenon occurs, the electrolyte often undergoes exothermic reactions, resulting in thermal runaway and threatening user safety.Therefore, this thesis develops an integrated fault detection algorithm for diagnosing micro short circuits in batteries. By integrating five methods, including 1. the battery capacity difference detection method based on the open circuit voltage curve fitting, 2. the remaining charge capacity detection method, 3. the density-based spatial clustering of applications with noise algorithm, 4. the standard score algorithm based on the recursive least square algorithm, 5. and the mean difference model based on the recursive least square algorithm. A highly accurate fault detection system for lithium-ion batteries has been developed.