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双电机驱动电动拖拉机实时自适应能量管理策略研究 被引量:13

Real-time Adaptive Energy Management Strategy for Dual-motor-driven Electric Tractors
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摘要 提出了一种用于纯电动拖拉机的双电机多模动力耦合驱动系统(DMCDS),通过对两电机与制动器的协调控制可以实现4种驱动模式:电机EM_S独立驱动、电机EM_R独立驱动、双电机耦合驱动和双电机独立驱动,多种驱动模式有利于提高电机负荷率和运行效率,从而提高整机驱动效率。为实现双电机动力耦合驱动系统的高效运行,增强能量管理策略对电动拖拉机不同作业工况的适应性,设计了一种基于随机动态规划+极值搜索算法(SDP_PESA)的实时自适应能量管理策略,该策略利用随机动态规划离线生成的状态反馈控制表作为控制输入参考,以保证近似全局最优,在此基础上,引入自适应寻优算法极值搜索算法动态搜索系统输出的局部极大值,以反馈校正SDP的控制输入,并生成能耗更低、效率更高的工作点。基于SDP_PESA的能量管理策略综合考虑了全局优化算法的良好优化性能和瞬时优化算法的实时性、鲁棒性,利用两种算法的优势,实现更加优异的控制性能。基于Matlab/Simulink建立了带有SDP状态反馈控制表的双电机驱动电动拖拉机(DMET)整机仿真模型,利用真实作业工况数据分别对基于SDP和SDP_PESA的能量管理策略进行仿真实验。仿真结果表明,DMET实际车速可以实时跟踪目标车速的变化,控制策略能够快速响应作业负载的变化;基于SDP的能量管理策略,DMET在犁耕和运输工况的每千米平均耗电量分别为1.77、1.17 kW·h/km,整机驱动效率分别为0.80和0.81。引入PESA输出反馈控制器后,整机驱动效率分别提高了2.13%和1.97%,平均耗电量分别降低了10.17%和16.2%,这表明基于SDP_PESA的能量管理策略可以有效增加纯电动拖拉机的作业里程,并且SDP_PESA完全具备实时应用能力。 Considering different power requirements of the tractor under various working conditions,the single-motor powertrain system is usually in low load state under low load working conditions,with low efficiency,resulting in energy waste.To solve this problem,a dual-motor multi-mode coupling driving system for electric tractors was proposed.Through the coordinated control of two motors and brakes,four driving modes could be realized:motor EM_S independent drives,motor EM_R independent drives,dual-motor coupling drives and dual-motor independent drives.These modes could meet the tractor power demand under various working conditions,improve the load rate of motor,and thus improve the efficiency of the whole vehicle.A parameter matching method was proposed to match the parameters of the two motors and the main transmission ratios of the coupling box according to the dynamic performance indexes of the typical working conditions.In order to realize the efficient operation of DMET,and enhance the adaptability of the energy management strategy to different operating conditions of electric tractors,a real-time energy management strategy based on stochastic dynamic programming+extreme seeking algorithm(SDP_PESA)was proposed.The state feedback control table generated offline by SDP as the control input reference was used to ensure the approximate global optimum.On this basis,the adaptive optimization algorithm-PESA was introduced to dynamically search the local maximum value of the system output to compensate for the control input of SDP and generate operating points with lower energy consumption and higher efficiency.The EMS based on SDP_PESA considered the good optimization performance of the global optimization algorithm and the robustness of the instantaneous optimization algorithm comprehensively,and the advantages of the two algorithms were used to achieve more excellent control performance.A DMET simulation model with SDP state feedback control table was established based on Matlab/Simulink,and real operating conditions were used to simulate the energy management strategies based on SDP and SDP_PESA.The simulation results demonstrated that the actual vehicle speed can track the change of the target vehicle speed in real time,and the control strategy can quickly respond to the change of the work load,indicating that the DMET simulation model was efficient and feasible,and can meet the simulation accuracy requirements.Based on the energy management strategy of SDP,the average power consumption of DMET in plowing and transportation conditions were respectively 1.77 kW·h/km and 1.17 kW·h/km,the driving efficiency was 0.80 and 0.81,respectively.However,after adding the PESA output feedback controller,the driving efficiency was increased by 2.13%and 1.97%,and the average power consumption was reduced by 10.17%and 16.2%,respectively,which meant the energy management strategy based on SDP_PESA can effectively increase the operating range of pure electric tractors,and SDP_PESA was fully capable of real-time application.
作者 李同辉 谢斌 王东青 张胜利 武丽萍 LI Tonghui;XIE Bin;WANG Dongqing;ZHANG Shengli;WU Liping(Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment,China Agricultural University,Beijing 100083,China;State Key Laboratory of Power System of Tractor,Luoyang 471039,China)
出处 《农业机械学报》 EI CAS CSCD 北大核心 2020年第S02期530-543,共14页 Transactions of the Chinese Society for Agricultural Machinery
基金 拖拉机动力系统国家重点实验室开放基金项目(SKT2020001)。
关键词 电动拖拉机 双电机耦合驱动 随机动态规划 极值搜索算法 功率分配 electric tractor dual-motor coupling driving stochastic dynamic programming extremum seeking algorithm power distribution
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