Design of Energy Management Strategy for Dual-Motor-Driven Electric Tractors

Abstract

At present, electric tractors experience significant battery energy loss during operation, resulting in a short continuous running time. Therefore, in order to reduce the power consumption of the tractor drive system, minimize battery energy loss, and extend the operating time under various conditions, this paper presents a method for driving an electric tractor based on dual-motor coupling. Based on the characteristics of the transmission structure, an online torque distribution strategy for dual-motor coupling-driven electric tractors using a fuzzy control approach is proposed. First, an enhanced genetic algorithm is utilized to optimize the fuzzy rule table. Simultaneously, it is compared with the offline optimization strategy of dynamic programming. Subsequently, a method that integrates test data models and theoretical models is employed to establish an efficiency model of key components of the electric tractor drive system and a longitudinal dynamics model of the entire machine. The performance of the entire vehicle was simulated and analyzed under plowing conditions. Finally, on the experimental bench, conduct steady-state load tests and dynamic performance tests on the dual-motor coupled drive system. The results show that the State of Charge (SOC) change trends of the fuzzy control strategy based on the improved genetic algorithm and the dynamic programming strategy are similar. The SOC change values are close, which enhances the adaptability of the electric tractor in various operating conditions. Compared with the fuzzy control strategy, the improved strategy reduced average power consumption by 8.8%, demonstrating that the fuzzy control energy management strategy based on the enhanced genetic algorithm is both economical and superior. The bench experiment demonstrated that the dual-motor drive system can adapt to load changes to achieve power distribution between the two motors, meeting the required workload while reducing power consumption.