Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming
Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming
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DOI：
10.1016/j.energy.2020.118851
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发表时间：
2021-01
期刊：
影响因子：
9
通讯作者：
Hu Jiayi;Jianqiu Li;Hu Zunyan;Liangfei Xu;M. Ouyang
中科院分区：
文献类型：
--
作者：
Hu Jiayi;Jianqiu Li;Hu Zunyan;Liangfei Xu;M. Ouyang
为了提高重型车辆的动力并带来额外的节能可能性，在串联混合动力汽车中采用了一种新型的双引擎系统。然而，这种配置的最低燃料消耗仍然不清楚，并且该系统缺乏有效的在线能源管理策略。所研究的车辆采用双引擎配置，由锂离子电池和两个辅助动力单元提供动力。本文提出了一个两步框架来解决能源管理问题。采用基于地图的策略在发动机之间分配功率。采用动态规划（DP）算法在发动机和蓄电池之间进行功率分配，并求出最小油耗。仿真结果表明，DP算法比耗尽荷电和保持荷电策略可节省7.3%的燃料。与传统的单引擎混合动力系统相比，双引擎系统的燃油消耗降低了1.9%。分析了由DP得到的发动机工作点和电池功率分布，并设计了相应的基于规则的策略。所提出的基于规则的算法可以降低2.2%-6.0%的油耗，并且对电池尺寸、基于规则的策略参数和行驶周期的变化不敏感。
In order to enhance the power and bring additional energy-saving possibilities of heavy-duty vehicles, a novel dual-engine system is utilized in a series hybrid electric vehicle. However, the minimum fuel consumption of this configuration is still unclear, and the effective online energy management strategy for this system is absent. The studied vehicle adopts the dual-engine configuration, which is powered by Li-ion batteries and two auxiliary power units. This paper presents a two-step framework to address the energy management problem. A map-based strategy is adopted to distribute power between engines. The dynamic programming (DP) algorithm is incorporated to distribute power between engines and batteries and find the minimum fuel consumption. Simulation results show that the DP algorithm can save 7.3% fuel compared to the charge depleting and charge sustaining strategy. The dual-engine system achieves a 1.9% lower fuel consumption compared with the conventional hybrid system with one engine. The operation points of engines and the power profiles of batteries derived from the DP are analyzed, and a rule-based strategy is designed correspondingly. The proposed rule-based algorithm can reduce 2.2%–6.0% fuel consumption and is not sensitive to the variations of battery size, parameters of the rule-based strategy, and driving cycles.