Cyber Physical Energy Optimization Control Design for PHEVs Based on Enhanced Firework Algorithm
Cyber Physical Energy Optimization Control Design for PHEVs Based on Enhanced Firework Algorithm
复制标题
基于增强烟花算法的插电式混合动力汽车信息物理能量优化控制设计
DOI：
10.1109/tvt.2020.3046520
复制
发表时间：
2021-01-01
影响因子：
6.8
通讯作者：
Ma, Mingyue
中科院分区：
文献类型：
--
作者：
Wang, Weida;Liu, Kaijia;Ma, Mingyue
能源管理策略（EMS）对于提高插电式混合动力汽车（PHEV）的燃油经济性起着至关重要的作用。凭借优异的实时性能，基于确定性规则（DRB）的方法被广泛引入EMS中用于实际PHEV的控制。然而，传统DBR控制通常采用固定参数作为阈值，这使得PHEV难以实现优异的燃油经济性。为了解决这个问题，需要对相关参数进行优化，但由此产生的耗时且复杂的过程是该方案实际应用的障碍。如今，无线通信、远程监控等技术的出现，催生了信息物理系统（CPS）的概念。它提供了优化 DRB EMS 参数的机会。受此启发，本文提出了一种针对插电式混合动力汽车的信息物理能量优化控制设计。其中，DRB控制旨在根据电池的充电状态（SOC）和车辆的需求功率来分配发动机和电动机（EM）的动力任务。此外，为了进一步提高EMS的性能，首先提出了增强烟花算法（EFWA）来优化控制器参数。与原始算法相比，EFWA引入了一种新颖的非CF选择机制。使得可以在更短的时间内获得优良的参数，更适合EMS的复杂优化。最后，验证和评估了所提出的EMS的有效性。结果表明，在中国典型城市工况和实际工况下，与使用未优化的基于规则的EMS相比，插电式混合动力汽车的燃油经济性分别提高了10%和12%。
Energy management strategy (EMS) plays a vital role in improving the fuel economy of plug-in hybrid electric vehicle (PHEV). By virtue of excellent real-time performance, deterministic rule-based (DRB) method is widely introduced into EMS for the control of actual PHEV. However, fixed parameters are usually used as thresholds in traditional DBR control, which makes it difficult for PHEV to achieve excellent fuel economy. To solve this problem, relevant parameters need to be optimized, but the resulting time-consuming and complex process is an obstacle for practical application of this scheme. Nowadays, the emergence of technologies, such as wireless communication, remote monitoring and so on, has gave birth to the concept of cyber-physical system (CPS). It provides an opportunity to optimize parameters of DRB EMS. Motivated by this, this paper proposes a cyber physical energy optimization control design for PHEVs. Among them, DRB control is designed to allocate power tasks for the engine and electric motor (EM), according to state of the charge (SOC) of battery and demand power of vehicle. Moreover, to further improve performance of EMS, an enhanced firework algorithm (EFWA) is firstly proposed to optimize parameters of controller. Compared with original algorithm, a novel selection mechanism for non-CF is introduced in EFWA. It makes the excellent parameters could be obtained in a shorter time, which is more suitable for the complex optimization of EMS. Finally, the effectiveness of proposed EMS is verified and evaluated. The results show that it improves the fuel economy of PHEV by 10% and 12% over that using the unoptimized rule-based EMS, under the China typical urban driving cycle and real-world driving cycle, respectively.