인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2026.6
- 수록면
- 51 - 64 (14page)
이용수
초록· 키워드
Accurate prediction of driving torque is critical for traction analysis, drivetrain design, and the energy-efficient operation of electric agricultural platforms operating on deformable soils. This study presents and experimentally validates a parameter-based driving torque prediction framework that integrates real-time wheel slip and acceleration measurements with soil–tire interaction and vehicle dynamics models. Field experiments were performed using a 4-kW electric four-wheel-drive, four-wheel-steering orchard platform under irregular orchard soil conditions. Driving torque, wheel rotational speed, vehicle speed, and acceleration were measured to quantify slip ratio and dynamic resistance components. Multiple torque prediction models were established based on Brixius slip-dependent formulations and an acceleration-based vehicle dynamics approach, and their predictive performance was systematically evaluated against measured torque data. The predictive capability of each model was assessed using statistical metrics, including root mean square error (RMSE), mean absolute error (MAE), bias, and the coefficient of determination (R²), together with regression analysis. The results indicated that conventional fixed-parameter models produced substantial systematic errors and exhibited weak agreement with measured torque values. In contrast, slip-based models incorporating real-time measurements markedly improved prediction accuracy, with the best-performing model yielding an RMSE of 3.09 Nm, an MAE of 2.48 Nm, and an R² of 0.87. Although the vehicle-dynamics-based model successfully captured overall torque trends, it was unable to adequately represent high-frequency torque fluctuations. Overall, the findings demonstrate that real-time slip-based modeling constitutes a robust and physically meaningful approach for predicting driving torque in electric agricultural platforms, providing practical value for traction-aware control, energy management, and platform design optimization.
#Driving torque prediction
#Electric agricultural platform
#Soil–tire interaction
#Traction performance
#Wheel slip ratio
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목차
- Abstract
- 1. 서론
- 2. Materials and Methods
- 3. Results and Discussions
- 4. Conclusions
- References
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