基于动态平衡优先优化的燃料电池/锂电池能量管理策略

潘玉龙; 张德福; 季长涛; 申振宇

doi:10.19799/j.cnki.2095-4239.2026.0570

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基于动态平衡优先优化的燃料电池/锂电池能量管理策略

储能XXXX | 更新时间：2026-07-27

- 基于动态平衡优先优化的燃料电池/锂电池能量管理策略
- Dynamic-Balance-Prioritized Pareto Parameter Optimization for Energy Management of Fuel-Cell/Lithium-Battery
- 储能科学与技术 2026年页码：1-16
- 作者机构：
  
  1.天津理工大学，海运学院，天津 300384
  2.青岛科而泰环境控制技术有限公司，青岛 266101
- 作者简介：
  
  潘玉龙（1995—），男，硕士研究生，研究方向：燃料电池/锂电池混合电源能量管理，E-mail：1131960518@qq.com；
  申振宇，博士，讲师，研究方向：混合动力与能源管理，船舶绿色动力，E-mail：shenzhenyu610@163.com。
- 基金信息：
  
  无
- DOI：10.19799/j.cnki.2095-4239.2026.0570
  中图分类号： TM 911
- 收稿：2026-07-02，
  
  修回：2026-07-23，
  
  网络首发：2026-07-27，
- 稿件说明：
移动端阅览
潘玉龙, 张德福, 季长涛, 等. 基于动态平衡优先优化的燃料电池/锂电池能量管理策略[J]. 储能科学与技术, XXXX, XX(XX): 1-16.

PAN Yulong, ZHANG Defu, JI Changtao, et al. Dynamic-Balance-Prioritized Pareto Parameter Optimization for Energy Management of Fuel-Cell/Lithium-Battery[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-16.
潘玉龙, 张德福, 季长涛, 等. 基于动态平衡优先优化的燃料电池/锂电池能量管理策略[J]. 储能科学与技术, XXXX, XX(XX): 1-16. DOI： 10.19799/j.cnki.2095-4239.2026.0570.

PAN Yulong, ZHANG Defu, JI Changtao, et al. Dynamic-Balance-Prioritized Pareto Parameter Optimization for Energy Management of Fuel-Cell/Lithium-Battery[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-16. DOI： 10.19799/j.cnki.2095-4239.2026.0570.

摘要

为协调长航时燃料电池/锂电池混合动力无人机有限氢气与电池能量的消耗进度，避免单一能源提前耗尽，提出动态平衡优先的Pareto参数优化能量管理策略，构建“离线参数优化—在线规则执行”框架。建立考虑39 V恒压直流母线和等效内阻损耗的锂电池SOC更新模型，基于1 kW质子交换膜燃料电池性能数据拟合氢耗四阶多项式，其R²、均方根误差和平均相对误差分别为0.9983、157.4 mL·min

¹和2.61%。以SOC利用率、氢气消耗率及其绝对差值表征动态平衡，选取7个规则参数为决策变量，以总氢耗、动态平衡差值和燃料电池相邻时刻功率变化量均方根为目标，采用NSGA-II生成Pareto参数集，并按“先平衡、后经济、再平滑”原则筛选折中解；在线阶段依据实时SOC、剩余氢气比例和负载功率执行功率分配。600 s工况下，折中解的SOC利用率、氢气消耗率、动态平衡差值、总氢耗和功率变化均方根分别为75.66%、72.79%、2.87%、5.807 g和25.363 W，终止SOC为24.34%，高于20%的安全下限；较等效氢耗优先和模糊规则策略，动态平衡差值分别降低62.6%和35.1%。5组随机种子下，总氢耗和动态平衡差值稳定在5.807～5.843 g和2.46%～2.87%，表明重复性良好。进一步对1200 s低波动巡航和600 s多次高功率机动工况重新优化，动态平衡差值分别为0.322%和1.733%，总氢耗分别为6.385 g和6.025 g，终止SOC与剩余氢气均满足约束，且未触发3%阈值放宽。结果表明，该策略能以可接受的氢耗代价协调双源消耗路径，保持负载波动下的协同供能能力，并适配不同时长和负载特征的已知任务剖面。

Abstract

To coordinate the consumption progress of limited onboard hydrogen and battery energy in long-endurance fuel cell/lithium-ion battery hybrid unmanned aerial vehicles and prevent either energy source from being prematurely depleted

a d

ynamic-balance-prioritized Pareto parameter optimization energy management strategy is proposed. An "offline parameter optimization–online rule execution" framework is established. A battery state-of-charge (SOC) update model considering a 39 V constant-voltage DC bus and equivalent internal-resistance losses is developed

while a fourth-order polynomial hydrogen consumption model is fitted using the performance data of a 1 kW proton exchange membrane fuel cell. The coefficient of determination

root mean square error

and mean relative error of the fitted model are 0.9983

157.4 mL·min

and 2.61%

respectively. The SOC utilization ratio

hydrogen consumption ratio

and their absolute difference are defined to characterize dynamic balance. Seven key rule parameters are selected as decision variables

and total hydrogen consumption

dynamic balance difference

and the root mean square of fuel-cell power variations between adjacent time steps are taken as optimization objectives. The non-dominated sorting genetic algorithm II is employed to generate the Pareto parameter set

from which a compromise solution is selected according to the principle of "balance first

economy second

and smoothness third." During online operation

power is allocated according to real-time SOC

remaining hydrogen ratio

and load power. Under the 600 s mission profile

the SOC utilization ratio

hydrogen consumption ratio

dynamic balance difference

total hydrogen consumption

and fuel-cell power variation root mean square are 75.66%

72.79%

2.87%

5.807 g

and 25.363 W

respectively. The terminal SOC remains at 24.34%

above the 20% safety limit. Compared with the equivalent-hydrogen-consumption-priority and fuzzy-rule-based strategies

the dynamic balance difference is reduced by 62.6% and 35.1%

respectively. Across five optimization runs with different random seeds

total hydrogen consumption and dynamic balance difference remain within 5.807–5.843 g and 2.46%-2.87%

demonstrating good repeatability. Further optimizati

on under a 1200 s low-fluctuation cruise profile and a 600 s high-power multi-maneuver profile yields dynamic balance differences of 0.322% and 1.733% and total hydrogen consumptions of 6.385 g and 6.025 g

respectively. The terminal SOC and remaining hydrogen satisfy the safety constraints without triggering relaxation of the 3% threshold. The results indicate that the proposed strategy coordinates the relative depletion paths of hydrogen and battery energy at an acceptable hydrogen cost

preserves dual-source cooperative power-supply capability under subsequent load fluctuations

and exhibits adaptability to known mission profiles with different durations and load characteristics.

关键词

Keywords

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