Multiobjective Co-Optimization of Cooperative Adaptive Cruise Control and Energy Management Strategy for PHEVs
Multiobjective Co-Optimization of Cooperative Adaptive Cruise Control and Energy Management Strategy for PHEVs
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DOI：
10.1109/tte.2020.2974588
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发表时间：
2020-03-01
影响因子：
7
通讯作者：
Xu, Hongming
中科院分区：
文献类型：
--
作者：
He, Yinglong;Zhou, Quan;Xu, Hongming
汽车和运输行业的电气化、自动化和连接性正在加速发展，但人们越来越担心它们需要共同优化，以提高能源效率、交通安全和乘坐舒适性。以前的方法，这些多目标协同优化问题往往忽略了权衡和目标之间的规模差异，导致误导性的优化。为了克服这些限制，本文提出了一个基于帕累托的框架，证明优化的系统参数的合作自适应巡航控制（CACC）和能量管理策略（EMS）的插电式混合动力电动汽车（PHEV）。高级帕累托知识有助于找到最佳折衷解决方案。本文的研究结果表明，能源和舒适性目标是协调的，但两者都与安全目标相冲突。使用真实驾驶数据进行的验证表明，CACC和EMS系统的帕累托最优值相对于基线可以降低能耗（7.57%）和跟踪误差（68.94%），同时满足乘坐舒适性需求。与加权和方法相比，所提出的Pareto方法可以最优地平衡和缩放多目标函数。此外，灵敏度分析表明，车辆反应时间对跟踪安全性影响显著，但对节能效果影响不大。
Electrification, automation, and connectivity in the automotive and transport industries are gathering momentum, but there are escalating concerns over their need for co-optimization to improve energy efficiency, traffic safety, and ride comfort. Previous approaches to these multiobjective co-optimization problems often overlook tradeoffs and scale differences between the objectives, resulting in misleading optimizations. To overcome these limitations, this article proposes a Pareto-based framework that demonstrably optimizes the system parameters of the cooperative adaptive cruise control (CACC) and the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs). The high-level Pareto knowledge assists in finding a best compromise solution. The results of this article suggest that the energy and the comfort targets are harmonious, but both conflict with the safety target. Validation using real-world driving data shows that the Pareto optimum for CACC and EMS systems, relative to the baseline, can reduce energy consumption (by 7.57%) and tracking error (by 68.94%) while simultaneously satisfying ride comfort needs. In contrast to the weighted-sum method, the proposed Pareto method can optimally balance and scale the multiple-objective functions. In addition, sensitivity analysis proves that the vehicle reaction time impacts significantly on tracking safety, but its effect on energy saving is trivial.