An event-triggered intelligent energy management strategy for plug-in hybrid electric buses based on vehicle cloud optimization
An event-triggered intelligent energy management strategy for plug-in hybrid electric buses based on vehicle cloud optimization
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基于车辆云优化的插电式混合动力客车智能能量管理策略
DOI：
10.1049/iet-its.2019.0690
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发表时间：
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通讯作者：
Wang Wei
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文献类型：
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作者：
Liu Kaijia;Jiao Xiaohong;Yang Chao;Wang Weida;Xiang Changle;Wang Wei
插电式混合动力客车(PHEBs)的能量管理策略(EMS)对燃油经济性有着非常重要的影响。目前，在优化和实时应用方面，EMS的性能仍有很大的提高空间。随着智能交通系统的快速发展，远程信息处理、远程车辆监控、云优化等新兴技术为实现这一目标提供了机会。基于此，本研究提出了一种面向PHEBs的事件触发式智能EMS。以PHEB的需求扭矩和电池的荷电状态为输入，电机扭矩为输出，选取三角形和梯形隶属度函数构成专用模糊控制器，完成PHEB的扭矩分配任务。为了进一步改进模糊控制器，提出了一种改进的遗传算法来优化其隶属度函数参数。此外，为了减少车辆云优化过程中的计算量，引入了事件触发机制。最后，对所提出的策略进行了验证，结果表明，在真实驾驶循环和中国典型城市行驶循环下，所提出的策略分别比基于规则的策略提高了所研究的PHEB的燃油经济性19%和22%。
Energy management strategy (EMS) of plug‐in hybrid electric buses (PHEBs) has a very important impact on the fuel economy. Currently there is still huge room to improve the performance of EMS, regarding the optimisation and real‐time application potentials. With the rapid development of intelligent transportation systems, the emerging technologies, such as telematics, remote vehicle monitoring, cloud optimisation, and so on, provide an opportunity to realize this goal. Based on this, this study proposes an event‐triggered intelligent EMS for PHEBs. Taking the demand torque of PHEB and the battery's state‐of‐charge as the inputs, and the electric motor torque as the output, triangle and trapezoid membership functions are chosen to construct a specialised fuzzy controller to accomplish torque split task in the studied PHEB. To further improve the fuzzy controller, an enhanced genetic algorithm is proposed to optimize its membership function parameters. Furthermore, to reduce the calculated load in the vehicle cloud optimisation process, an event‐triggered mechanism is introduced. Finally, the proposed strategy is verified, and results show that the proposed strategy improves the fuel economy of the studied PHEB by 19% and 22% over that using the rule‐based strategy, under the real‐world driving cycle and China typical urban driving cycle, respectively.