Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions
Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions
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DOI：
10.1016/j.energy.2015.12.071
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发表时间：
2016-02-01
期刊：
影响因子：
9
通讯作者：
Cao, Jiayi
中科院分区：
文献类型：
--
作者：
Chen, Zeyu;Xiong, Rui;Cao, Jiayi
针对不确定行驶条件下的插电式混合动力汽车，提出了一种新的最优功率管理方法。为了在一定的行驶周期下对基于规则的功率管理策略的阈值参数进行优化，采用粒子群优化算法，并根据优化结果确定最优控制动作。为了更好地实时实现电源管理策略，提出了一种驾驶状态识别算法，通过模糊逻辑算法对驾驶状态进行实时识别。为了在行驶周期不确定的情况下自适应地调整规则策略的阈值，进一步建立了动态优化参数算法，避免了规则策略的阈值对行驶周期非常敏感的问题。最后，结合以上工作，提出了基于粒子群优化算法的驾驶周期识别优化功率管理的详细操作流程。仿真结果表明，在不同的驾驶条件下，所提出的控制策略均能显著提高控制性能。特别是在行驶周期不确定的情况下，最高可降低1.76%的能量损失。(C)2015爱思唯尔有限公司。保留所有权利。
This paper proposes a novel optimal power management approach for plug-in hybrid electric vehicles against uncertain driving conditions. To optimize the threshold parameters of the rule-based power management strategy under a certain driving cycle, the particle swarm optimization algorithm was employed, and the optimization results were used to determine the optimal control actions. To better implement the power management strategy in real time, a driving condition recognition algorithm was proposed to identify real-time driving conditions through a fuzzy logic algorithm. To adjust the thresholds of the rule-based strategy adaptively under uncertain driving cycles, a dynamic optimal parameters algorithm has been further established accordingly, and it is helpful for avoiding the problem that the thresholds of the rule-based strategy are very sensitive to the driving cycles. Finally, in combination with the above efforts, a detailed operational flowchart of the particle swarm optimization algorithm-based optimal power management through driving cycle recognition has been proposed. The results illustrate that the proposed strategy could greatly improve the control performance for different driving conditions. Especially for the uncertain driving cycles, the reduction in energy loss can be up to 1.76%. (C) 2015 Elsevier Ltd. All rights reserved.