The lead‐acid battery has been widely used in various fields. In civil aviation aircraft, it plays a vital role in the power system to maintain normal operation during the flight mission. Thus, an effective abnormal detection system for monitoring and diagnosing the status of aircraft lead‐acid battery is essential to ensure its safety and reliability. This paper aims to effectively identify aircraft battery faulty using unsupervised anomaly detection techniques. It introduces state‐of‐the‐art anomaly detection algorithms and evaluates their performance on a large real civil aviation battery data. The experimental results show that the latest isolation‐based anomaly detectors, iForest and iNNE, have outstanding performance on this task and have promising applicability as efficient methods for guaranteeing the lead‐acid battery quality and reliability in civil aviation aircraft.