# Large Language Model Based Intelligent Fault Information Retrieval System for New Energy Vehicles
## 6 Conclusions and Prospects  
In conclusion, the development of an intelligent retrieval system for NEV fault diagnosis has successfully met the research objectives. The integration of ChatGLM3-6B with a knowledge graph and an improved fault retrieval algorithm has enhanced the system's capability. The model was fine-tuned using the LoRA methodology, leading to superior performance compared to baseline models. Additionally, a fault tree analysis  
Figure 16: Fault information system interaction process.  
method was combined with an expert system to efficiently process fault data. Comparative evaluations demonstrated the system's effectiveness in improving retrieval accuracy and diagnostic precision.  
The development of this system not only enhances the efficiency and accuracy of NEV fault diagnosis but also lays the foundation for the practical application of intelligent fault retrieval technologies. By integrating advanced generative models and knowledge graph techniques, it drives innovation in fault data processing and diagnostic algorithms, offering new technological pathways and solutions for the field.  
While the system has achieved its initial goals, there is still room for improvement. Future enhancements will focus on three key areas:  
1. Enhancing fault data retrieval--Improving entity extraction, fault categorization, and relational identification, and optimizing graph-based retrieval processes.
2. Automating knowledge graph updates--Leveraging generative models to enable real-time knowledge graph construction and updates, ensuring data relevance and accuracy.
3. System integration--Incorporating the NEV fault retrieval system with vehicular networking and maintenance decision support systems to create a comprehensive NEV service ecosystem, facilitating data interchange and functional integration.  
Conceptualization, H.Z.; methodology, H.Z.; software, H.Z.; validation, B.S.; formal analysis, Y.Z. and B.S.; investigation, Y.Z.; resources, H.Z.; data curation, B.S.; writing--original draft preparation, H.Z.; writing--review and editing, Y.Z. and Y.W.; visualization, H.Z. and B.S.; supervision, Z.F.; project administration, X.X.; funding acquisition, Z.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available at [[https://figshare.com/s/700](https://figshare.com/s/700) fd7 ffaa4 df07 bf698]([https://figshare.com/s/700](https://figshare.com/s/700) fd7 ffaa4 df07 bf698). Accessed on 26 February 2025.  
The authors declare no conflict of interest.  
The standards referenced in this study are primarily national standards related to NEVs; the following are some of the key standards referenced (Table A1).  
Please note that these are just a few of the relevant standards, and the full list of applicable national standards could not be included here due to the large number.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Standard (GB/T)** & **Title** \\ \hline
1 & GB/T 18385-2005 & Electric Vehicle Power Performance \\
2 & GB/T 18386.1-2021 & Energy Consumption and Range for Light-duty Electric Vehicles \\
3 & GB/T 18388-2005 & Electric Vehicle Type Testing Regulations \\
4 & GB/T 24552-2009 & Electric Vehicle Windshield Defogging and Dew Removal \\
5 & GB/T 28382-2012 & Technical Conditions for Pure Electric Passenger Vehicles \\
6 & GB/T 36980-2018 & Energy Consumption Rate Limit for Electric Vehicles \\
7 & GB/T 19750-2005 & Hybrid Electric Vehicle Type Testing Regulations \\
8 & GB/T 19752-2005 & Hybrid Electric Vehicle Power Performance \\
9 & GB/T 19753-2021 & Energy Consumption Testing Methods for Hybrid Electric Vehicles \\ \hline \hline \end{tabular}
\end{table}
Table A1: Partial List of National Standards for NEVs.