Real-Time Energy-Efficient Control Strategy for Distributed Drive Electric Tractor Based on Operational Speed Prediction

Xiaoting Deng (), Zheng Wang, Zhixiong Lu, Kai Zhang, Xiaoxu Sun and Xuekai Huang
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Xiaoting Deng: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Zheng Wang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Zhixiong Lu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Kai Zhang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Xiaoxu Sun: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Xuekai Huang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2025, vol. 15, issue 13, 1-24

Abstract: This study develops a real-time energy-efficient control strategy for distributed-drive electric tractors (DDETs) to minimize electrical energy consumption during traction operations. Taking a four-wheel independently driven DDET as the research object, we conduct dynamic analysis of draft operations and establish dynamic models of individual components in the tractor’s drive and transmission system. A backpropagation (BP) neural network-based operational speed prediction model is constructed to forecast operational speed within a finite prediction horizon. Within the model predictive control (MPC) framework, a real-time energy-efficient control strategy is formulated, employing a dynamic programming algorithm for receding horizon optimization of energy consumption minimization. Through plowing operation simulation with comparative analysis against a conventional equal torque distribution strategy, the results indicate that the proposed real-time energy-efficient control strategy exhibits superior performance across all evaluation metrics, providing valuable technical guidance for future research on energy-efficient control strategies in agricultural electric vehicles.

Keywords: distributed drive system; electric tractor; energy efficiency optimization; backpropagation neural network; model predictive control; dynamic programming (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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