The safety of battery energy storage systems (BES) is of paramount importance for societal development and the wellbeing of the people. This is particularly true for retired batteries, as their performance degradation increases the likelihood of thermal runaway occurrences. Existing early warning methods for BES thermal runaway face two main challenges: mechanism-based research methods only consider a single operating state, making their application and promotion difficult; while data-driven methods based on supervised learning struggle with limited sample sizes. To address these issues, this paper proposes a data-driven early warning method for BES thermal runaway. The method utilizes unsupervised learning to create a framework that measures BES differences through reconstruction errors, enabling effective handling of limited samples. Additionally, ensemble learning is employed to enhance the method’s stability and quantify the probability of BES experiencing thermal runaway. To accurately capture the time-varying behaviors of BES, such as voltage, temperature, current, and state of charge (SOC), and detect performance differences in BES before and after thermal runaway, a bidirectional long short-term memory (Bi-LSTM) network with an attention mechanism is utilized. This approach effectively extracts features from training data. Subsequently, a Case study was conducted using the actual operation data of retired lithium batteries to verify the effectiveness of the proposed method.