###### Abstract  
With the rapid proliferation of new energy vehicles, the safety of power batteries has attracted increasing attention. As a crucial approach to ensuring system stability, fault detection has become a research focus. However, strong temporal dependencies in battery operation data and the scarcity of fault samples hinder the accuracy and robustness of existing methods. To address these challenges, this paper proposes a deep learning-based fault detection model that integrates a Generative Adversarial Network (GAN) with a Convolutional Long Short-Term Memory (CNN-LSTM) network. The GAN is employed to augment minority-class fault samples, effectively mitigating the class imbalance in the dataset. Then, the CNN-LSTM module directly processes raw multivariate time-series data, combining the capability of CNN in extracting local spatial patterns with the LSTM strength in modeling temporal dependencies, enabling accurate identification of battery faults. Experiments conducted on real-world datasets collected from electric vehicles demonstrate that the proposed model achieves a Precision of 95.23%, Recall of 87.23%, and F1-Score of 91.12% for fault detection. Additionally, it yields an Average Precision (AP) of 97.45% and an Area Under the ROC Curve (AUC) of 99%, significantly outperforming conventional deep learning and machine learning baselines. This study provides a practical and high-performance solution for fault detection in power battery systems, with promising application potential. p 16 April 202516 April 202516 May 202  
are prone to various types of faults, such as thermal runaway, lithium plating, and electrolyte leakage. These failures are often triggered by internal short circuits, overcharging, or casing damage, potentially leading to severe consequences including fires and explosions [2]. In major markets like China and the United States, battery incidents are frequently reported by government agencies and media. Since 2019, over 600 power battery-related fires have been documented in cities like New York and San Francisco alone. According to China's Ministry of Emergency Management, more than 3000 EV-induced fire accidents occurred in 2023. Such incidents are characterized by rapid combustion and high destructiveness, leaving very limited escape time for passengers. Consequently, developing efficient battery fault detection mechanisms has become a pressing research focus in both academia and industry [3]. Current battery fault detection methods can be broadly classified into two categories: (1) physics-based approaches, which rely on thermodynamic, electrochemical, or control models to analyze battery status; and (2) data-driven approaches, which use machine learning or deep learning techniques to model and classify battery behaviors from collected data [4]. Compared with traditional methods, data-driven approaches offer automated modeling capabilities without requiring complex physical assumptions, and they exhibit stronger adaptability when handling complex, non-linear, and multivariate data. Nevertheless, two key challenges remain: first, battery operation data exhibit strong temporal dependencies, and effectively modeling these time-series correlations is critical [5]; second, fault samples are rare, resulting in highly imbalanced datasets that hinder the model's ability to learn fault patterns, thus degrading detection performance [6]. To address the above challenges, this paper proposes a deep learning-based fault detection framework that integrates a Generative Adversarial Network (GAN) with a Convolutional Long Short-Term Memory (CNN-LSTM) network. The main contributions of this work are as follows:  
1. A GAN-based sample generation method is developed to synthesize time-consistent pseudo-fault samples by learning the distribution of real fault data, effectively alleviating class imbalance. 2.