The invention discloses a hybrid vehicle PF-AEMS energy management method based on working condition identification, and belongs to the technical field of automobile real-time energy optimization management, and the method comprises the steps: 1, considering the correlation between the characteristic parameters of the driving working condition of a hybrid vehicle and the correlation between each parameter and oil consumption, and optimizing the characteristic parameters of the driving working condition; 2, carrying out the division and analysis of typical random working conditions based on a working condition recognition algorithm of K-means clustering; 3, optimal charge and discharge equivalent factors under different typical random working conditions are calculated, and a working condition recognition algorithm based on K-means clustering is combined with a PF-EEMS algorithm to obtain a self-adaptive energy management strategy of the hybrid power vehicle based on working condition recognition; results prove that the method provided by the invention has stronger working condition adaptability while improving the economical efficiency, realizes better voltage stabilization and current stabilization effects, and can be widely applied to energy management of real vehicles.
本发明公开了基于工况识别的混动车辆PF‑AEEMS能量管理方法,属于汽车实时能量优化管理技术领域,包括步骤1:考虑混动车辆行驶工况特征参数之间的相关性、各参数与油耗之间的相关性,优化行驶工况特征参数;步骤2:基于K‑means聚类的工况识别算法对典型随机工况进行划分与分析;步骤3:计算不同典型随机工况下的最优充放电等效因子,并将基于K‑means聚类的工况识别算法与PF‑EEMS算法结合,得到基于工况识别混合动力车辆的自适应能量管理策略;结果证明,本发明所提出的上述方法法可以改善经济性的同时具有更强的工况适应性,并实现了更好的稳压、稳流效果,能够广泛应用于实车的能量管理中。
Hybrid vehicle PF-AEMS energy management method based on working condition recognition
基于工况识别的混动车辆PF-AEEMS能量管理方法
2024-07-23
Patent
Electronic Resource
Chinese
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