复合储能式装载机能量控制优化与仿真  

Optimization and Simulation of Energy Control for Composite Energy Storage Loaders

作　　者：白宇 林慕义[1,2] 陈勇 刘振博[1] BAI Yu;LIN Mu-yi;CHEN Yong;LIU Zhen-bo(School of Mechanical and Electrical Engineering,Beijing Information Science&Technology University,Beijing 100192;Beijing Laboratory for New Energy Vehicles,Beijing 100192)

机构地区：[1]北京信息科技大学机电工程学院,北京100192 [2]北京电动车辆协同创新中心,北京100192

出　　处：《液压与气动》2024年第2期41-51,共11页Chinese Hydraulics & Pneumatics

基　　金：国家自然科学基金(51275053);科技创新能力服务建设-科研基地建设-新能源汽车北京实验室(市级)(PXM2022_014224_000005)。

摘　　要：针对工程车辆在机群作业下的能量管理问题,以复合储能式装载机为研究对象,利用Recurdyn-Edem联合仿真建立连续作业环境获取工况数据;分别以V型工况及集群作业下装载机单次循环工况油耗最少为目标,设计了基于等效油耗最小策略与规则相结合的控制策略,为了能够进一步提高全局工况的燃油经济性,使用遗传算法对等效油耗最小关键参数进行寻优,最后带入整车模型进行仿真验证。结果表明,V型工况与集群作业工况下,该控制策略相较于自适应神经模糊控制,燃油经济性分别提升了3.23%和4.26%;通过dSPACE进行硬件在环试验,试验与仿真结果基本一致,进而验证了优化结果的有效性。In order to address the energy management problem of engineering vehicles in cluster operation,a study is conducted on the composite energy storage loader.A combined simulation using Recurdyn-Edem is employed to establish a continuous operation environment and acquire working condition data.Two objectives are considered:minimizing fuel consumption during both V cycle working condition and cluster operation of the loader's single-cycle working condition.A control strategy is designed by combining the Equivalent Consumption Minimization Strategy with rules.To further enhance the fuel economy in various working conditions,a Genetic Algorithm is utilized to optimize the key parameters for minimum equivalent fuel consumption.The optimized parameters are then applied to a vehicle model for simulation validation.The results indicate that the proposed control strategy improves fuel economy by 3.23%and 4.26%in V cycle working condition and cluster operation condition,respectively,compared to the Adaptive Neuro-Fuzzy Inference System.Furthermore,hardware-in-the-loop experiments conducted using dSPACE validate the effectiveness of the optimization results.

关 键 词：复合储能 等效油耗最小策略 遗传算法 硬件在环 

分 类 号：TH137[机械工程—机械制造及自动化] TP391.9[自动化与计算机技术—计算机应用技术]