复合储能式混合动力系统全局最优控制策略研究

doi:10.3969/j.issn.1674-8484.2025.06.007

摘要/Abstract

摘要： 为解决工程车辆复合储能混合动力系统在复杂工况下能量管理效率不足的问题，提出一种基于动态规划（DP）的全局优化策略。该策略以负载需求转矩、液压蓄能器荷电状态（SOC）和电池SOC作为状态变量，构建功率分配最优控制模型，并通过逆向递推-正向优化求解控制序列。最后用硬件在环（HIL）测试进一步验证DP策略在实际工程中的应用可行性。结果表明：与基于规则（RB）策略和自适应神经模糊推理系统（ANFIS）策略相比，DP策略使发动机高效区运行时间占比分别提高38.28%和30.27%，综合燃油消耗降低15.17%和11.23%，电池SOC波动幅度减少34.02%和23.97%，制动回收蓄能器SOC均值提升29.46%和23.51%，电池SOC均值提高11.57%和8.62%。上述结果证明，DP策略可有效提升发动机效率，维持其高效区稳定运行，并实现系统整体节能优化；该DP策略为工程机械节能控制提供了理论依据和解决方案。

关键词: 工程车辆, 复合储能式混合动力系统, 动态规划（DP）, 能量管理, 硬件在环（HIL）测试

Abstract:

To address the insufficient energy management efficiency of composite energy storage hybrid systems in engineering vehicles under complex operating conditions, a global optimization strategy based on dynamic programming (DP) was proposed. This strategy constructed an optimal power allocation control model using load demand torque, the state of charge (SOC) of hydraulic accumulator, and battery SOC as state variables, and solved the control sequence through inverse recursive-forward optimization. The feasibility of applying the DP strategy in practical engineering was further validated through hardware-in-the-loop testing. The results show that compared to rule-based (RB) and adaptive neural fuzzy inference system (ANFIS) strategies, the DP strategy increases the proportion of engine operation within the high-efficiency zone by 38.28% and 30.27%, respectively. It reduces the comprehensive fuel consumption by 15.17% and 11.23%, and the battery SOC fluctuation by 34.02% and 23.97%, respectively. The average SOC of the brake energy recovery accumulator are increased by 29.46% and 23.51%, and the average battery SOC are improved by 11.57% and 8.62%, respectively. These results demonstrate that the DP strategy effectively enhances engine efficiency, maintains stable operation within the high-efficiency zone, and achieves overall system energy optimization. The DP strategy provides both theoretical justification and practical solutions for energy-saving control in construction machinery.

Key words: engineering vehicles, composite energy storage hybrid power system, dynamic programming (DP), energy management, hardware-in-the-loop (HIL) testing

中图分类号:

U489

李钊, 隆武强, 田华. 复合储能式混合动力系统全局最优控制策略研究[J]. 汽车安全与节能学报, 2025, 16(6): 877-885.

LI Zhao, LONG Wuqiang, TIAN Hua. Research on global optimal control strategy for hybrid power system with composite energy storage[J]. Journal of Automotive Safety and Energy, 2025, 16(6): 877-885.

图/表 11

参考文献 16

[1]	LIU Huiying, YAO Yongming, LI Tianyu, et al. Dyna algorithm-based reinforcement learning energy management for fuel cell hybrid engineering vehicles[J]. J Energ Stor, 2024, 94: 112526-112540.
[2]	郭琦. 工程车辆并联式液压混合动力系统研究[D]. 杭州: 浙江大学, 2017.
	GUO Qi. Research on parallel hydraulic hybrid power system for engineering vehicles[D]. Hangzhou: Zhejiang University, 2017. (in Chinese)
[3]	ZHANG Wei, WANG Jixin, XU Zhenyu, et al. A generalized energy management framework for hybrid construction vehicles via model-based reinforcement learning[J]. Energy, 2022, 260: 124849-124863. doi: 10.1016/j.energy.2022.124849 URL
[4]	LIU Huiying, XING Xiaoxue, SHANG Weiwei, et al. NSGA-II optimized multi-objective predictive energy management for fuel cell/battery/supercapacitor hybrid construction vehicles[J]. Int’l J Elect Sci, 2021, 16(4): 21046-21062.
[5]	LAN Ban. An optimization approach for energy management of hybrid vehicles based on dynamic programming and cuckoo search[J]. Tehnički Vjesnik, 2024, 31(6): 2146-2155.
[6]	YAO Dongwei, ZENG Fanlong, LU Xinwei, et al. Traffic prediction based battery state-of-charge planning for connected plug-in hybrid electric vehicle[J]. Int’l J Autom Tech, 2025, (prepublish): 1-20.
[7]	CHEN Jinzhou, HE Hongwen, WANG Yaxiong, et al. Research on energy management strategy for fuel cell hybrid electric vehicles based on improved dynamic programming and air supply optimization[J]. Energy, 2024, 300: 131567-131580. doi: 10.1016/j.energy.2024.131567 URL
[8]	Hoffmann H. Optimization of power-split hybrid drives and of their operating strategy using dynamic programming[J]. Fors Im Ingen, 2025, 89(1): 29-47.
[9]	ZHAI Xumao, LI Yujuan, JIANG Guangzong, et al. Improved mode switching and gear shift control strategy of parallel hybrid electric vehicle based on dynamic programming[J]. SAE Int’l J Comm Vehi, 2025, 18(02-18-02-0008).
[10]	AN Siqi, GAN Yuantao, PENG Xu, et al. Charging optimization with an improved dynamic programming for electro-gasoline hybrid powered compound-wing unmanned aerial vehicle[J]. Energies, 2024, 18(1): 30-30.
[11]	刘振博, 林慕义, 陈勇, 等. 复合储能式装载机动态规划能量管理策略仿真[J]. 重庆理工大学学报(自然科学), 2024, 38(11): 213-220.
	LIU Zhenbo, LIN Muyi, CHEN Yong, et al. Simulation of dynamic programming energy management strategy for hybrid energy storage loader[J]. J Chongqing Univ Tech (Nat Sci), 2024, 38(11): 213-220. (in Chinese)
[12]	Tyni T, Vepsäläinen J. Hydraulic hybrid system coupling traction motor and working hydraulics of electric wheel loader[J]. Energy, 2025, 332: 137217-137230. doi: 10.1016/j.energy.2025.137217 URL
[13]	ZHANG Haoxiang, WANG Feng, WU Jiaming, et al. A cycle-adaptive control strategy to minimize electricity and battery aging costs of electric-hydraulic hybrid wheel loaders[J]. Energy, 2025, 319: 134655-134669. doi: 10.1016/j.energy.2025.134655 URL
[14]	Allam M, Linjama M. Optimisation of series electric hybrid wheel loader energy management strategies using dynamic programming[J]. Int’l J Heav Vehi Syst, 2025, 32(7): 1-29.
[15]	QIU Weijin, Ashta S, Shaver G M, et al. System configuration, control development, and in-field validation of a hybrid electric wheel loader featuring electrically-boosted engine[J]. Cont Engi Prac, 2024, 150: 105989-106004.
[16]	LIU Jichao, LIANG Yanyan, CHEN Zheng, et al. Energy management strategies for hybrid loaders: Classification, comparison and prospect[J]. Energies, 2023, 16(7): 3018-3041.

柴油发动机	额定功率	180 kW
	最大扭矩	1 050 Nm
	额定转速	2 200 r/min
	怠速转速	800 r/min
	峰值功率(驱动/发电)	60/45 kW
永磁同步电机	峰值扭矩	350 Nm
	最高工作转速	3 000 r/min
动力电池 (磷酸铁锂)	额定容量	50 Ah
	额定电压	360 V
	SOC工作范围	0.3~0.8
液压蓄能器 (皮囊式)	总容积	25 L
	预充气压力	12 MPa
	最高工作压力	25 MPa
	SOC工作范围	0.2~0.9
整车基本参数	整机操作重量	16.5 t
	额定载荷	5.0 t
	液力变矩器最大变矩系数	2.5

柴油发动机	额定功率	180 kW
	最大扭矩	1 050 Nm
	额定转速	2 200 r/min
	怠速转速	800 r/min
	峰值功率(驱动/发电)	60/45 kW
永磁同步电机	峰值扭矩	350 Nm
	最高工作转速	3 000 r/min
动力电池 (磷酸铁锂)	额定容量	50 Ah
	额定电压	360 V
	SOC工作范围	0.3~0.8
液压蓄能器 (皮囊式)	总容积	25 L
	预充气压力	12 MPa
	最高工作压力	25 MPa
	SOC工作范围	0.2~0.9
整车基本参数	整机操作重量	16.5 t
	额定载荷	5.0 t
	液力变矩器最大变矩系数	2.5

指标	电流RMS值 A	峰值电流 A	相比RMS 降低/ %	相比峰值降低/ %
DP策略	24.61	34.04	-	-
ANFIS策略	30.48	43.72	19.3	22.1
RB策略	34.41	54.49	28.5	37.5

指标	电流RMS值 A	峰值电流 A	相比RMS 降低/ %	相比峰值降低/ %
DP策略	24.61	34.04	-	-
ANFIS策略	30.48	43.72	19.3	22.1
RB策略	34.41	54.49	28.5	37.5