The drive torque for each motor can be performed independently for the distributed drive electric wheel loader (DDEWL). On the shovelling condition, the torque distribution can be optimised according to the tire load. Then the parasitic power and wheel slippage can be reduced, and the tractive force and efficiency are improved. In this study, the singular value decomposition unscented Kalman filter is adopted to estimate the shovelling load. Based on the estimated shovelling load, the vertical tire force is calculated. The tire load rate is used to build the optimisation objective. The drive antiskid is set as the boundary condition. And a modified particle swarm optimisation is applied as the optimisation algorithm. A wheel loader is utilised for the testing data acquisition. Based on the acquired testing data, the comparative study about the optimisation and un-control strategies is operated. The results show that the slippage occurs less in the optimisation strategy, and the tractive force and efficiency are higher than the un-control strategy. In this study, the feasibility of the shovel load estimator is verified. The advantages of the optimisation strategy of distributed drive electric wheel loaders in shovelling process are analysed.

对于分布式驱动电动轮式装载机（DDEWL），每个电机的驱动扭矩可以独立执行。在铲车状态下，扭矩分配可以根据轮胎负载进行优化。然后可以减少寄生功率和车轮打滑，提高牵引力和效率。在本研究中，采用奇异值分解无迹卡尔曼滤波器来估计铲土载荷。基于估计的铲土载荷，计算垂直轮胎力。轮胎负载率用于建立优化目标。以驱动防滑为边界条件。并采用改进的粒子群优化算法作为优化算法。轮式装载机用于测试数据采集。根据获得的测试数据，进行了优化与失控策略的比较研究。结果表明，优化策略的打滑发生率较低，牵引力和效率均高于非控制策略。在本研究中，验证了铲式载荷估计器的可行性。分析了分布式驱动电动轮式装载机铲土过程优化策略的优势。