作者:吴石，车翠茹，管诣博
Authors:WU Shi,CHE Cuiru , GUAN Yibo摘要:针对双轮毂电机驱动汽车的高效低耗能问题，提出一种考虑双轮毂电机驱动力矩在线实时反馈的优化方法 。首先，建立轮毂电机能耗模型，动力学模型以及转矩分配控制模型; 其次，以轮毂电机驱动效率为目标，以轮毂电机转矩和转速能限为不等式约束，建立基于加入异变序列的 QPSO-LSTM 算法的优化模型; 最后，搭建 Lab- VIEW 软件实验平台，使轮毂电机在 716 N · m 转矩峰值和1 000 r / min 转速峰值的约束条件稳定输出 。结果表明， 软件平台可实时监测并调整驱动车辆系统数据，在 FTP-75 工况下，加入异变序列的 QPSO-LSTM 算法比量子遗传算法和粒子群算法的单循环能耗分别降低了 2. 98% 和 4. 64% ; 在 CLTC-P 工况下，单循环能耗分别降低了 3. 06%和 4. 81% 。

Abstract:Aiming at the problem of high efficiency and low energy consumption of vehicle driven by dual hub motor ，an optimization method considering the online real-time feedback of driving distance of dual hub motor was proposed. Firstly，the hub motor energy consumption model，dynamics model and torque distribution control model are established. Secondly，an optimization model based on QPSO-LSTM algorithm with variable sequence was established by taking the driving efficiency of wheel motor as the objective and taking the torque and speed limit of wheel motor as inequality constraints. Finally，the LabVIEW software experimental platform was built to make the hub motor output stably under the constraints of torque peak value of 716n. m and speed peak value of 1000r / min. Experimental results show that the software platform can monitor and adjust the driving vehicle system data in real time. In FTP-75 condition，the QPSO-LSTM algorithm with the addition of heterogeneous sequences reduces the single-cycle energy consumption by 2. 98% and 4. 64% ，respectively，compared with the quantum genetic algorithm and the particle swarm algorithm in the FTP-75 operating condition，and by 3. 06% and 4. 81% ，respectively，in the CLTC-P operating condition.


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