基于工况识别的HEV自适应能量管理算法

Adaptive HEV Energy Management Algorithms Based on Drive-cycle Recognition

提出了自适应能量管理算法,通过简化的神经网络对实时车速进行采集、分析和比较,在运行一段时间后,自动寻找出与之相近的标准循环工况,控制参数也相应转换为标准工况的已优化参数.基于CRUISE软件进行建模实现,选取城市客车循环工况进行仿真,分析结果表明,多工况自适应整车管理算法能够较好地根据车速对工况进行分类识别,并达到在保证混合动力汽车(HEV)电量平衡基础上节约燃油的目的.

An adaptive energy management algorithms based on drive-cycle recognition for HEV was proposed. The real vehicle speed was converted to the parameters, which represented the unknown driving cycle by the functional module. With the reconstructed neural networks, the unknown driving cycle was automatically recognized as several standard cycles, and the control parameters switched to the standard＇ s which were optimized. The model was built on CRIUSE. A simulation was performed on a city bus driving cycle. The results show that the algorithms can meet the target and the fuel consumption is decreased as wall as balancing the Battery State of Charge （SOC） for the HEV bus.