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.

中文翻译：

---

PHEV协同自适应巡航控制与能量管理策略的多目标协同优化

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协同优化以提高能源效率、交通安全和乘坐舒适性。以前解决这些多目标协同优化问题的方法通常会忽略目标之间的权衡和规模差异，从而导致优化误导。为了克服这些限制，本文提出了一种基于帕累托的框架，该框架可证明优化了插电式混合动力汽车 (PHEV) 的协作自适应巡航控制 (CACC) 和能量管理策略 (EMS) 的系统参数。高级帕累托知识有助于找到最佳折衷解决方案。本文的结果表明，能量和舒适目标是和谐的，但都与安全目标相冲突。使用真实世界驾驶数据的验证表明，相对于基线，CACC 和 EMS 系统的帕累托最优可以减少能耗（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，所提出的帕累托方法可以最佳地平衡和缩放多目标函数。此外，灵敏度分析证明，车辆反应时间对跟踪安全性影响显着，但对节能的影响不大。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，所提出的帕累托方法可以最佳地平衡和缩放多目标函数。此外，灵敏度分析证明，车辆反应时间对跟踪安全性影响显着，但对节能的影响不大。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，所提出的帕累托方法可以最佳地平衡和缩放多目标函数。此外，灵敏度分析证明，车辆反应时间对跟踪安全性影响显着，但对节能的影响不大。