## 6 Conclusions and Future Work  
With the rapid adoption of new energy vehicles, power battery safety has become an increasingly important research focus. Among related technologies, fault detection plays a critical role in ensuring the stable operation of battery systems and is gaining attention as a prominent topic in the academic community. In recent years, data-driven deep learning methods have shown great potential in battery fault detection. However, two major challenges remain in practical applications:  
1. the strong temporal dependencies inherent in battery data;
2. the extreme scarcity of fault samples, leading to severe class imbalance.  
To address these challenges, this paper proposes a GAN-CNN-LSTM-based deep learning fault detection model. The model takes real-world charging process data from electric vehicles as input and automatically learns both spatial and temporal features to determine whether a battery fault has occurred. The GAN module generates high-quality synthetic fault samples by learning the distribution of a small number of real fault cases, thereby balancing the dataset. The CNN-LSTM module directly models the multivariate time-series input--without the need for feature extraction or flattening--preserving the original structure and enabling efficient classification.  
Experiments were conducted on a real-world dataset collected from battery operations. Results demonstrate that the proposed model achieved excellent fault detection performance. For fault samples (positive class)--the core target--the model achieved a Precision of 95.23%, Recall of 87.23%, and F1-Score of 91.12%, all significantly outperforming baseline models. For normal samples, all three metrics exceeded 97%. In addition, the model achieved outstanding results under PR and ROC curve evaluations, with an Average Precision (AP) of 97.45% and Area Under the Curve (AUC) of 99.00%, further validating its strong detection capability and overall classification performance.  
Despite these promising results, this study still has certain limitations. First, the proposed model has not yet been deployed and validated in real vehicle environments. In future work, we plan to integrate the model into actual Battery Management Systems (BMSs) and evaluate its stability under dynamic operational conditions.  
Second, the dataset used in this study is derived from a single manufacturer and vehicle type. This may limit the generalizability of the model to other vehicles or usage scenarios. Therefore, we aim to expand our dataset to include data from multiple manufacturers, vehicle types, and driving conditions, in order to systematically evaluate and enhance the robustness and transferability of the proposed method.  
Additionally, combining mechanism-based diagnostic methods with data-driven models could help establish a multi-modal, hierarchical fault detection framework, further improving the model's generalizability and practical utility in complex real-world scenarios.  
Conceptualization, S.L. ([PERSON]) and T.X.; Methodology, S.L. ([PERSON]); Software, S.L. ([PERSON]); Data curation, S.L. ([PERSON]); Formal analysis, Y.L.; Validation, Y.L.; Visualization, Y.L.; Writing--original draft, S.L. ([PERSON]); Writing--review & editing, S.L. ([PERSON]) and S.L. ([PERSON]); Project administration, T.X.; Supervision, T.X. All authors have read and agreed to the published version of the manuscript.  
Funding: This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets presented in this article are not readily available due to the ongoing use of the dataset in related research; the data are not publicly shared at this time. Requests to access the datasets should be directed to [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.