## 2 Related Work  
### Overview of Fault Detection Methods for Power Batteries  
Currently, fault detection methods for power batteries can be broadly categorized into two types: model-based approaches (also referred to as traditional methods) and data-driven approaches. The former relies on thermodynamic, electrochemical, and control models to provide interpretable mathematical formulations for monitoring and predicting fault conditions. In contrast, the latter leverages machine learning or deep learning techniques to learn patterns of state evolution from large volumes of battery operation data, enabling intelligent fault diagnosis and early warning.  
Model-based approaches typically monitor key parameters such as voltage, current, and temperature, and trigger alarm signals through the Battery Management System (BMS) when any measurement exceeds preset thresholds [7]. [PERSON] et al. provided a comprehensive review of equivalent circuit models (ECMs) and electrochemical modelsused in BMSs, highlighting their roles in thermal management and overcharge protection [8]. [PERSON] et al. proposed an internal short-circuit detection method based on residual voltage, capable of identifying latent faults under real driving conditions [9]. Although these approaches can achieve high accuracy under ideal or well-defined conditions, they are heavily dependent on model parameters and often struggle to adapt to non-linear battery behaviors, aging effects, and variable operating conditions.  
In contrast, data-driven methods have gained increasing attention in recent years due to their strong capability in capturing complex patterns. These methods learn the distributions of fault and normal conditions from historical battery data and build classifiers for fault detection [10]. [PERSON] et al. reviewed the applications of traditional machine learning techniques such as Support Vector Machines (SVMs), decision trees, and random forests in battery health evaluation, noting their good generalization performance in low-dimensional scenarios [11]. With the rise of deep learning, researchers have explored using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for battery fault diagnosis [12]. [PERSON] et al. integrated incremental capacity analysis (ICA) with CNNs to develop a deep feature-driven early fault detection method that significantly improved classification performance [13]. [PERSON] and [PERSON], from a model perspective, systematically reviewed the applications of artificial intelligence in BMSs, and highlighted the importance of LSTM networks, autoencoders, and reinforcement learning in life prediction and state monitoring [14; 15]. [PERSON] et al. constructed a deep neural network framework based on incremental voltage topology to efficiently identify multiple types of battery faults [16]. [PERSON] et al. improved detection accuracy by combining segmented regression with gated recurrent units (GRUs) [17], while [PERSON] et al. proposed a hybrid algorithm based on symmetric deviation point (SDP) patterns and CNNs to detect typical failures such as overcharging, over-discharging, aging, and leakage [18].  
In summary, model-based approaches provide strong interpretability and theoretical grounding and are well-suited to scenarios with well-understood mechanisms and clearly defined structures. However, their performance is limited when dealing with real-world conditions involving multivariate coupling, non-linear dynamics, and complex operating environments. Data-driven methods, while more sensitive to data quality and model selection, offer greater flexibility, adaptability, and predictive accuracy in modeling non-linear relationships and identifying complex features, thus demonstrating superior robustness and practical value in power battery fault detection.