## 8 Discussion  
To address the issue of low accuracy in the prediction of oil temperature for HCSs, this study has proposed a precise oil temperature prediction model based on CAM. Building upon this, the optimal ORFR for an HCS was analyzed and determined to ensure an efficient drive of oil temperature within an appropriate range. First, this study analyzed the principles of HCSs and pointed out that the temperature of hydraulic oil has an important impact on HCS. By employing mathematical analytical calculations and hydraulic-thermal coupling modeling techniques, the study addressed the oil temperature variation patterns under different parameters and identified issues and limitations in current methods for solving oil temperature. Consequently, a highly effective and succinct method for calculating oil temperature based on CAM was proposed, achieving a coefficient of determination of the CAM model at 0.935. Additionally, the optimal ORFR under various operating conditions was analyzed and determined. In conclusion, an experimental platform was developed for HCS to validate the accuracy of the theoretical analysis. The test data demonstrated that the CAM model exhibited strong predictive accuracy for oil temperature, and the optimal ORFR data obtained could effectively enhance system efficiency by regulating the oil temperature within a reasonable range.  
In comparison to prior research, the temperature forecasting model presented in this study demonstrates superior timeliness and cost-effectiveness. Utilizing the designed CAM model for temperature resolution enables the immediate acquisition of precise data. Consequently, the optimal oil refilling parameters for various operating conditions are established, thereby ensuring that the HCS system attains both high performance and efficiency. Undoubtedly, the process of constructing CAM models in this study is not without its imperfections. A significant concern pertains to the identification of more scientific methods for determining the weight coefficients of sub-models, which directly impacts the upper limit of fitting accuracy achievable by the CAM model. This question merits further investigation in future research. In summary, the procedures and findings of this study can provide effective guidance for engineering applications.  
Figure 23: Temperature variation under optimal ORFR.  
**Author Contributions:** Conceptualization, K.H. and W.Z.; methodology, K.H. and W.Z.; software, K.H. and W.Z.; validation, K.H. and W.Z.; formal analysis, K.H. and W.Z.; investigation, K.H.; resources, K.H. and W.Z.; data curation, K.H.; writing--original draft preparation, K.H.; writing--review and editing, W.Z.; visualization, K.H.; supervision, W.Z.; project administration, K.H.; funding acquisition, K.H. and W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been supported by the National Key Research and Development Program of China (Grant no.2021 YFD2000401) and partly supported by the Basic Research Project of Nanjing Research Institute of Agricultural Mechanization of the CAAS (Grant no. S202401), the Innovation Program of Chinese Academy of Agricultural Sciences, and the Primary Research and Development Plan of Jiangsu Province (Grant no. BE2021308).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
**Acknowledgments:** The authors thank the Xuzhou Oida mechanical and electrical equipment company for providing equipment support.  
**Conflicts of Interest:** The authors declare no conflicts of interest.