Dynamic energy management for a novel hybrid electric system based on driving pattern recognition
Dynamic energy management for a novel hybrid electric system based on driving pattern recognition
复制标题
DOI：
10.1016/j.apm.2017.01.036
复制
发表时间：
2017-05
影响因子：
5
通讯作者：
Z. Lei;D. Qin;Yonggang Liu;Peng Zhiyuan;Lu Lilai
中科院分区：
文献类型：
--
作者：
Z. Lei;D. Qin;Yonggang Liu;Peng Zhiyuan;Lu Lilai
研究了基于驾驶模式识别的混合动力汽车动态能量管理。本文研究的混合动力系统在结构上包括单向离合器、多片离合器和行星齿轮作为动力耦合装置。通过对发动机、电池和综合起动机/发电机（ISG）的部件级模型进行集成，建立了动力总成效率模型。分析了各工况下的动力系统效率，包括电动驾驶模式、行驶充电模式、发动机驾驶模式和混合动力驾驶模式。基于静态系统效率，设计了混合动力系统的模式切换计划。采用粒子群优化算法（PSO）对混合动力汽车随机循环下以电池寿命和油耗为主要考虑因素的自适应控制进行了优化。在此基础上，利用聚类分析实现了基于20个典型参考周期的驾驶模式识别。最后，提出了基于驾驶模式识别的混合动力汽车动态能量管理策略。在Matlab/Simulink平台上建立了混合动力汽车动力总成系统的仿真模型。在随机驾驶条件下，分别实施了基于知识和基于驾驶模式识别的两种能量管理策略。模型仿真结果验证了本文混合动力汽车控制策略在驱动模式识别和能量管理优化方面的有效性。
This paper focuses on the dynamic energy management for Hybrid Electric Vehicles (HEV) based on driving pattern recognition. The hybrid electric system studied in this paper includes a one-way clutch, a multi-plate clutch and a planetary gear unit as the power coupling device in the architecture. The powertrain efficiency model is established by integrating the component level models for the engine, the battery and the Integrated Starter/Generator (ISG). The powertrain system efficiency has been analyzed at each operation mode, including electric driving mode, driving and charging mode, engine driving mode and hybrid driving mode. The mode switching schedule of HEV system has been designed based on static system efficiency. Adaptive control for hybrid electric vehicles under random driving cycles with battery life and fuel consumption as the main considerations has been optimized by particle swarm optimization algorithm (PSO). Furthermore, driving pattern recognition based on twenty typical reference cycles has been implemented using cluster analysis. Finally, the dynamic energy management strategy for the hybrid electric vehicle has been proposed based on driving pattern recognition. The simulation model of the HEV powertrain system has been established on Matlab/Simulink platform. Two energy management strategies under random driving condition have both been implemented in the study, one is knowledge-based and the other is based on driving pattern recognition. The model simulation results have validated the control strategy for the hybrid electric vehicle in this study in terms of drive pattern recognition and energy management optimization.