State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China.
Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China.
Sensors (Basel). 2022 Aug 20;22(16):6256. doi: 10.3390/s22166256.
Energy management strategies are vitally important to give full play to energy-saving four-wheel-drive plug-in hybrid electric vehicles (4WD PHEV). This paper proposes a novel dual-adaptive equivalent consumption minimization strategy (DA-ECMS) for the complex multi-energy system in the 4WD PHEV. In this strategy, management of the multi-energy system is optimized by introducing the categories of future driving conditions to adjust the equivalent factors and improving the adaptability and economy of driving conditions. Firstly, a self-organizing neural network (SOM) and grey wolf optimizer (GWO) are adopted to classify the driving condition categories and optimize the multi-dimensional equivalent factors offline. Secondly, SOM is adopted to identify driving condition categories and the multi-dimensional equivalent factors are matched. Finally, the DA-ECMS completes the multi-energy optimization management of the front axle multi-energy sources and the electric driving system and releases the energy-saving potential of the 4WD PHEV. Simulation results show that, compared with the rule-based strategy, the economy in the DA-ECMS is improved by 13.31%.
能量管理策略对于充分发挥节能四轮驱动插电式混合动力汽车（4WD PHEV）的作用至关重要。本文针对 4WD PHEV 复杂的多能源系统提出了一种新颖的双自适应等效能耗最小化策略（DA-ECMS）。在该策略中，通过引入未来驾驶条件的类别来调整等效因子，提高了驾驶条件的适应性和经济性，从而优化了多能源系统的管理。首先，采用自组织神经网络（SOM）和灰狼优化器（GWO）对驾驶条件类别进行分类，并离线优化多维等效因子。其次，采用 SOM 识别驾驶条件类别并匹配多维等效因子。最后，DA-ECMS 完成前轴多能源和电驱动系统的多能源优化管理，释放 4WD PHEV 的节能潜力。仿真结果表明，与基于规则的策略相比，DA-ECMS 的经济性提高了 13.31%。
