基于数字孪生的动力换挡拖拉机换挡策略研究

Shift Strategy for Powershift Tractors Based on Digital Twins

发动机性能变化和牵引阻力波动等时变因素对大功率动力换挡拖拉机(PST)换挡策略的适应性具有巨大威胁。为了构建动态精准模型和应对负载波动,以提高PST换挡策略的适应性,提出了一种基于数字孪生的自适应换挡策略。一方面,将发动机状态变化视作内部干扰,基于深度确定性策略梯度(DDPG)算法对虚拟PST发动机进行实时校准,结合PST机理模型,实现对PST的实时动态精准建模。另一方面,将牵引阻力波动视作外部干扰,提出基于深度Q网络(DQN)的换挡策略生成方法。在实时动态精准建模和换挡策略自动生成两个机制的协同作用下,实现换挡策略的自适应调整。最后,开展了犁耕工况下的虚拟PST训练仿真及本文方法与模糊自适应方法的车速跟踪对比试验。结果表明,发动机转矩和燃油消耗率跟踪误差均值不超过7.28 N·m和1.55 g/(kW·h),实现了对物理PST的动态精准建模。在长时间使用之后,发动机和牵引阻力的变化导致模糊自适应方法的换挡点和模糊规则不再完全适用,换挡表现逐渐变差,而本文方法的换挡表现和车速跟踪效果全程良好,其车速跟踪误差均值、燃油消耗率均值和总换挡次数分别为0.0125 m/s、229.76 g/(kW·h)和42,比模糊自适应方法分别减小0.91%、11.14%、34.38%,验证了本文方法的适应性和优越性。

Engine performance variations and traction fluctuations have a great impact on the adaptability of shift strategies for high-power power shift tractors(PSTs).In order to construct a dynamic accurate model and deal with traction fluctuation to improve the adaptability of PST shift strategy,an adaptive shift strategy development method was proposed based on digital twins.On one hand,the engine state change was regarded as an internal disturbance,and the virtual PST engine was calibrated in real time based on the deep deterministic strategy gradient algorithm,which was combined with the PST mechanism model to realize the real-time dynamic and accurate modeling of the PST.On the other hand,the traction fluctuation was treated as an external disturbance,and a deep Q-network was used to automatically generate the shift strategy.Finally,the virtual PST training simulation under plowing conditions and the speed tracking comparison test between the proposed method and the fuzzy adaptive method were carried out.The results showed that the average tracking errors of engine torque and fuel consumption rate did not exceed 7.28 N·m and 1.55 g/(kW·h),and dynamic and accurate modeling of physical PST was achieved.After using for a long time,the changes of engine and traction force caused that the shift points and fuzzy rules of the fuzzy adaptive method were no longer fully applicable,and the shift performance was gradually deteriorated.In contrast,the shift performance and speed tracking effect of the proposed method were good throughout,and the mean value of speed tracking error,mean value of fuel consumption rate,and total number of shifts were 0.0125 m/s,229.76 g/(kW·h),and 42,respectively,which were reduced by 0.91%,11.14%,and 34.38% compared with those of the fuzzy adaptive method.The adaptability and superiority of the proposed method were verified.