Energy Management Strategies for Fuel Cell Vehicle Considering Air Conditioning Systems

doi:10.12141/j.issn.1000-565X.240396

Abstract

Abstract:

In the actual operation of fuel cell hybrid electric vehicles, the air conditioning system provides a comfortable environment for drivers and passengers. However, the performance of the air conditioning system interacts with the vehicle’s energy distribution during operation. Therefore, it is necessary to integrate the air conditioning system into the energy management strategy, and design an energy management strategy that ensures the cabin temperature comfort requirements while also considering the overall hydrogen consumption efficiency of the vehicle. Firstly, based on the vehicle dynamics model, the heat balance equation was used to establish the heat pump air-conditioning system model and heat load model. Then, the dual delay depth deterministic strategy gradient (TD3-PER) algorithm combining the double Q network and the depth deterministic strategy gradient was used to establish the energy management strategy considering the energy consumption of the air conditioning system and the vehicle operation demand. Simulation under the typical NEDC driving cycle shows that with the TD3-PER energy management strategy, the air conditioning system can rapidly bring the cabin temperature to and maintain it within the comfortable range of 22 ℃ to 26 ℃ in 100 seconds, ensuring cooling/heating performance to maintain cabin comfort. This validates the feasibility of the TD3-PER energy management strategy when considering the air conditioning system. During cooling/heating operation, compared to the traditional Deep Deterministic Policy Gradient (DDPG) algorithm, the power distribution strategy based on the TD3-PER algorithm can extend the lifespan of both the fuel cell and the battery. Additionally, in terms of hydrogen consumption, the TD3-PER-based strategy can improve fuel economy by 2.59 percentage points during cooling and 3.58 percentage points during heating. This demonstrates that the TD3-PER algorithm-based energy management strategy offers significant advantages over traditional algorithms in terms of reducing hydrogen consumption and improving overall vehicle efficiency.

Key words: dual-energy fuel cell vehicle, air conditioning system, energy management strategy, TD3-PER, DDPG

CLC Number:

U469.72

ZHAO Youqun, XU Zhou, YU Zhihao, LIN Fen, HE Kunpeng, YOU Qingshen. Energy Management Strategies for Fuel Cell Vehicle Considering Air Conditioning Systems[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(6): 56-65.

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参数	取值
整车质量/kg	1 760
滚动半径/m	0.28
滚动阻力系数	0.019
空气阻力系数	0.34
迎风面积/m²	2.5
汽车旋转质量换算系数	1.1
驱动电机峰值功率/kW	160
驱动电机最大转速/（r∙min^-1）	12 000
主减速器传动比	5
氢燃料电池额定功率/kW	50
氢燃料电池峰值功率/kW	65
蓄电池容量/（A·h）	37
蓄电池总线额定电压/V	350
蓄电池最大电流/A	100
蓄电池持续电流/A	72
蓄电池荷电状态允许范围	5%~95%

参数	取值
顶板面积/m²	2
前挡风玻璃面积/m²	1.8
后窗面积/m²	0.29
侧窗面积/m²	0.4
前挡风板渗透率	0.452
后窗渗透率	0.321
侧窗渗透率	0.475
前挡风玻璃入射角/（°）	60
后窗入射角/（°）	55
侧窗入射角/（°）	20

参数	取值
Actor网络学习率	0.000 8（制冷），0.001（制热）
折扣因子	0.9
样本学习数	64
软更新率	0.001
Critic网络学习率	0.000 8（制冷），0.001（制热）
经验池容量	10 000
训练步数	2 000
延迟更新参数	2

策略	SOC终值	氢耗量/g	相对氢耗/%
DDPG	0.7	79.50
DDPG+制冷	0.7	85.37	7.38
DDPG+制热	0.7	87.63	10.23
TD3-PER+制冷	0.7	83.31	4.79
TD3-PER+制热	0.7	84.79	6.65

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