Authors

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.

Abstract in Chinese

目前电动拖拉机在在工作时电池能量损耗较大，持续运行时间较短，因此，本文为降低拖拉机驱动系统功率消耗，减少电池能量的损耗，延长工况运行时间，根据双电机耦合驱动电动拖拉机的传动结构特性，提出了一种基于模糊控制策略的双电机耦合驱动电动拖拉机的在线转矩分配策略，首先采用改进遗传算法对模糊规则表进行优化，同时与动态规划的离线优化策略进行对照，随后采用试验数据模型和理论模型相结合的方法，建立了电动拖拉机驱动系统关键部件效率模型和整机纵向动力学模型，在犁耕下对整车性能进行仿真分析，最后在搭建的实验台架上对双电机耦合驱动系统进行稳态负载试验和动态性能试验。结果表明：基于改进遗传算法的模糊控制策略与动态规划策略的SOC变化趋势相似，SOC变化值接近，改善了电动拖拉机不同作业工况的适应性，且改进后的策略与模糊控制策略相比，平均耗电量降低了8.8%，证明了基于改进遗传算法的模糊控制能量管理策略具有良好的经济性和优越性，台架实验表明双电机驱动系统能够跟随负载变化实现两电机的功率分配，满足作业负载的同时降低了功率损耗。