-
摘要:
装载机能耗高、排放差, 研究装载机新能源技术具有重要意义。结合装载机工况特性提出了燃料电池与超级电容联合驱动的电源系统, 围绕复杂工况下燃料电池和超级电容系统动态模型的实时工况数据进行自适应能量管理策略研究。设计了复合电源拓扑结构与动力传动方案, 建立装载机复杂工况下系统多状态模型, 基于Haar小波理论对整车系统进行功率分流, 提出模糊逻辑能量管理策略动态平衡需求功率中的低频分量, 采用粒子群算法对控制系统进行优化。仿真结果显示:载荷功率经过最优阈值3层Haar小波处理后, 功率变化大幅度减缓, 有效提升燃料电池系统的寿命;模糊逻辑控制器输出的燃料电池功率曲线变化光滑, 超级电容SOC值处于设定区域内, 提高复合电源系统的综合效率;经过粒子群算法优化控制器后, 燃料电池输出平均功率同比下降约5%, 超级电容SOC值在约0.6达到动态平衡状态, 改善了装载机的动态响应和稳定性。
Abstract:It is of great significance to study new energy technologies for loaders, which have high energy consumption and poor emissions. Combined with the operating characteristics of the loader, this paper proposes a power supply system driven by fuel cell and super capacitor. Our research focused on adaptive energy management strategy resulting from dynamic model and real-time data of fuel cell and supercapacitor system under complex working conditions. Firstly, we designed a composite power supply topology and power transmission scheme. Then, a multi-state model of the system was established under complex working conditions of the loader. And, based on the Haar wavelet theory, the power of the vehicle system was split. Subsequently, a fuzzy logic energy management strategy was proposed to dynamically balance the low-frequency components of the demand power. Lastly, the particle swarm optimization algorithm was used to optimize the control system. The simulation results showed that the power change was greatly slowed down because of the optimal threshold three-layer Haar wavelet on the load power, which effectively improved the life of the fuel cell system. The fuel cell power curve output by the fuzzy logic controller also changed smoothly. Meanwhile, the SOC value of the super capacitor was within the set area. Therefore, the overall efficiency of the composite power system was improved. After optimizing the controller by the particle swarm algorithm, the average output power of the fuel cell decreased by about 5% year-on-year, and the SOC value of the super capacitor reaches a dynamic equilibrium state of about 0.6, which improves the dynamic response and stability of the loader.
-
-
[1] LI T Y, HUANG L T, LIU H Y. Energy management and economic analysis for a fuel cell supercapacitor excavator[J]. Energy, 2019, 172: 840-851. doi: 10.1016/j.energy.2019.02.016 [2] DE MIRANDA P E V, CARREIRA E S, ICARDI U A, et al. Brazilian hybrid electric-hydrogen fuel cell bus improved on-board energy management system[J]. International Journal of Hydrogen Energy, 2017, 42(19): 13949-13959. doi: 10.1016/j.ijhydene.2016.12.155 [3] ZHANG X, MI C, 等. 车辆能量管理: 建模、控制与优化[M]. 张希, 米春亭, 译. 北京: 机械工业出版社, 2013.ZHANG X, MI C, et al. Vehicle energy management: Modeling, control and optimization[M]. ZHANG X, MI C T, translated. Beijing: China Machine Press, 2013(in Chinese). [4] LI T Y, LIU H Y, WANG H, et al. Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles[J]. Energy, 2020, 198: 117327. doi: 10.1016/j.energy.2020.117327 [5] MUNOZ P M, CORREA G, GAUDIANO M E, et al. Energy management control design for fuel cell hybrid electric vehicles using neural networks[J]. International Journal of Hydrogen Energy, 2017, 42(48): 28932-28944. doi: 10.1016/j.ijhydene.2017.09.169 [6] SNOUSSI J, BEN ELGHALI S, BENBOUZID M, et al. Auto-adaptive filtering-based energy management strategy for fuel cell hybrid electric vehicles[J]. Energies, 2018, 11(8): 1-20. [7] IBRAHIM M, JEMEI S, WIMMER G, et al. Non cinear autoregressive neural network in an energy management strategy for battery lultra-capacitor hybrid electrical vehicles[J]. Electric Power Systems Research, 2016, 136: 262-269. doi: 10.1016/j.epsr.2016.03.005 [8] ZHANG R D, TAO J L, ZHOU H Y. Fuzzy optimal energy management for fuel cell and super capacitor systems using neural network based driving pattern recognition[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(1): 45-57. doi: 10.1109/TFUZZ.2018.2856086 [9] HE D, SHI Y, SONG X. Weight-free multi-objective predictive cruise control of autonomous vehicles in integrated perturbation analysis and sequential quadratic programming optimization framework[J]. Dynamic System Measurement Control, 141, 9: 91015. [10] 杜文杰. 基于燃料电池复合双电源装载机系统功率控制研究[D]. 太原: 中北大学, 2020.DU W J. Research on power control of fuel cell compound dual energy source system[D]. Taiyuan: North University of China, 2020(in Chinese). [11] 吕沁阳, 滕腾, 张宝迪, 等. 增程式燃料电池车经济性与耐久性优化控制策略[J]. 哈尔滨工业大学学报, 2021, 53(7): 126-133. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX202107015.htmLV Q Y, TENG T, ZHANG B D, et al. Optimal control strategy for economy and durability of extended range fuel cell vehicle[J]. Journal of Harbin Institute of Technology, 2021, 53(7): 126-133(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX202107015.htm [12] 胡尊严. 车用燃料电池系统耐久性建模与状态估计研究[D]. 北京: 清华大学, 2019: 3-7.HU Z Y. Durability modeling and state estimation for vehicular fuel cell system[D]. Beijing: Tsinghua University, 2019: 3-7(in Chinese). [13] SULAIMAN N, HANNAN M A, MOHAMED A, et al. Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations[J]. Applied Energy, 2018, 228: 2061-2079. doi: 10.1016/j.apenergy.2018.07.087 [14] 崔宁, 秦四成, 赵丁选. 液压挖掘机并联混合节能动力系统多目标优化控制策略[J]. 西安交通大学学报, 2016, 50(6): 116-121. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT201606018.htmCUI N, QIN S C, ZHAO D X. A multi object optimal control strategy for a parallel hybrid power system in hydraulic excavators[J]. Journal of Xi'an Jiaotong University, 2016, 50(6): 116-121(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT201606018.htm [15] SONG K, DING Y H, HU X, et al. Degradation adaptive energy management strategy using fuel cell state-of-health for fuel economy improvement of hybrid electric vehicle[J]. Applied Energy, 2021, 285: 1-12. [16] LI T Y, LIU H Y, WANG H, et al. Multiobjective optimal predictive energy management for fuel cell/battery hybrid construction vehicles[J]. IEEE Access, 2020, 8: 25927-25937. doi: 10.1109/ACCESS.2020.2969494 [17] FAISAL M, HANNAN M A, KER P J, et al. Particle swarm optimised fuzzy controller for charging-discharging and scheduling of battery energy storage system in MG applications[J]. Energy Reports, 2020, 6: 215-228. [18] AFZAL A, RAMIS M K. Multi-objective optimization of thermal performance in battery system using genetic and particle swarm algorithm combined with fuzzy logics[J]. Journal of Energy Storage, 2020, 32: 101815. [19] LI J, ZHOU Q, WILLIAMS H, et al. Back-to-back competitive learning mechanism for fuzzy logic based supervisory control system of hybrid electric vehicles[J]. IEEE Transactions on Industrial Electronics, 2020, 67(10): 8900-8909. [20] 吕柏权, 张静静, 李占培, 等. 基于变换函数与填充函数的模糊粒子群优化算法[J]. 自动化学报, 2018, 44(1): 74-86. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201801007.htmLV B Q, ZHANG J J, LI Z P, et al. Fuzzy partical swarm optimization based on filled function and transformation function[J]. Acta Automatica Sinica, 2018, 44(1): 74-86(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201801007.htm