基于制动意图识别的电动物流车换挡策略研究

为了使纯电动汽车的制动安全性和制动能量回收最大化，利用基于RBF神经网络的模糊控制方法识别制动意图，设计了基于逻辑规则的AMT挡位下移策略提高制动能量回收，最后通过仿真实验验证了所提出方法的正确性和可行性。结果表明:识别制动意图的准确率达到了99%；在轻度制动、中度制动强度下以不同制动初速度进行制动，整个制动过程中采用了AMT挡位下移策略所回收能量比未采用此策略所回收能量明显提高，电机效率明显提高；在紧急制动强度下，电机制动不参与制动，无能量回收。

Research on Shift Strategy of Electric Logistics Vehicle Based on Braking Intention Recognition

In order to maximize the braking safety and braking energy recovery of pure electric vehicles,a fuzzy control method based on RBF neural network was used to identify braking intention,and a logic rule-based AMT gear shifting strategy was designed to improve braking energy recovery.Finally,simulation experiments verified the correctness and feasibility of the proposed method.The results show that the accuracy of identifying the braking intention reached 99%;the braking was performed at different initial braking speeds under light braking and moderate braking intensity,and during the entire braking process,the energy recovered by using the AMT gear shift down strategy is significantly higher than the energy recovered without this strategy,the motor efficiency is significantly improved;under the emergency braking intensity,the motor braking does not participate in braking,and there is no energy recovery.