Power Generation Technology ›› 2023, Vol. 44 ›› Issue (4): 452-464.DOI: 10.12096/j.2096-4528.pgt.23003
• Key Technologies of Green Hydrogen Preparation, Storage and Multi-scenario Application • Previous Articles Next Articles
Bofei WANG1, Haozhe XIAO1, Guohao LI2, Wenheng XIU3, Yunhao MO2, Mingjie ZHU1, Zhen WU1
Received:
2023-01-10
Published:
2023-08-31
Online:
2023-08-29
Contact:
Zhen WU
Supported by:
CLC Number:
Bofei WANG, Haozhe XIAO, Guohao LI, Wenheng XIU, Yunhao MO, Mingjie ZHU, Zhen WU. A Review of Energy Management Strategy for Hydrogen-Electricity Hybrid Power System Based on Control Target[J]. Power Generation Technology, 2023, 44(4): 452-464.
EMS种类 | 每一步计算耗时/s | 总计算耗时/s |
---|---|---|
基于PMP的EMS | 0.004 880 | 13.943 3 |
传统基于规则的EMS | 0.000 304 | 0.868 8 |
改进的基于规则的EMS | 0.000 539 | 1.539 5 |
Tab. 1 Comparison of calculation time between three EMSs
EMS种类 | 每一步计算耗时/s | 总计算耗时/s |
---|---|---|
基于PMP的EMS | 0.004 880 | 13.943 3 |
传统基于规则的EMS | 0.000 304 | 0.868 8 |
改进的基于规则的EMS | 0.000 539 | 1.539 5 |
具体控制策略 | 优点 | 缺点 | 应用 |
---|---|---|---|
基于确定规则的能量 管理策略 | 实现简单,计算量小,实时性好 | 相关参数会受到工况条件的强烈影响,燃油经济性有待改善,难以保证最佳优化效果 | 广泛应用于商用的燃料电池混合动力系统 |
基于庞特里亚金最小值原理的控制策略 | 与DP相比,计算耗时短,计算效率高 | 依赖于准确的预测模型, 适应性不强 | 要求有较高的全局优化性能,该策略可控制算法计算时间在30 s以内 |
Tab. 2 Comparison of the determination rule-based EMS and PMP-based EMS
具体控制策略 | 优点 | 缺点 | 应用 |
---|---|---|---|
基于确定规则的能量 管理策略 | 实现简单,计算量小,实时性好 | 相关参数会受到工况条件的强烈影响,燃油经济性有待改善,难以保证最佳优化效果 | 广泛应用于商用的燃料电池混合动力系统 |
基于庞特里亚金最小值原理的控制策略 | 与DP相比,计算耗时短,计算效率高 | 依赖于准确的预测模型, 适应性不强 | 要求有较高的全局优化性能,该策略可控制算法计算时间在30 s以内 |
场景 | 电池SOC状态 | 燃料电池功率波动/W |
---|---|---|
燃料电池未退化 | 0.7~0.8 | 250 |
燃料电池退化率50% | 0.7~0.8 | 700 |
失去部分电池,燃料电池退化率20% | ~0.7 | 700 |
Tab. 3 Simulation results of fuzzy logic controller in three scenarios
场景 | 电池SOC状态 | 燃料电池功率波动/W |
---|---|---|
燃料电池未退化 | 0.7~0.8 | 250 |
燃料电池退化率50% | 0.7~0.8 | 700 |
失去部分电池,燃料电池退化率20% | ~0.7 | 700 |
具体控制策略 | 优点 | 缺点 | 应用 |
---|---|---|---|
基于模糊逻辑的EMS策略 | 算法相对简单,不需精确的数学模型,具有较好的鲁棒性与自适应性 | 模糊规则等需要根据经验来制定,无法保证全局最优 | 恰当应用该控制策略,在燃料电池混动汽车运行平稳时,可限制燃料电池功率波动300 W以内,同时,使电池处于一个良好的SOC状态 |
MPC策略 | 鲁棒性强,稳定性好, 采用反馈校正和滚动优化 | 依赖工程经验,适应性不强,计算效率不高 | 适用于实时应用场景,可优化电池SOC波动和燃料电池功率波动 |
Tab. 4 Comparison of the fuzzy logic rule-based EMS and MPC-based EMS
具体控制策略 | 优点 | 缺点 | 应用 |
---|---|---|---|
基于模糊逻辑的EMS策略 | 算法相对简单,不需精确的数学模型,具有较好的鲁棒性与自适应性 | 模糊规则等需要根据经验来制定,无法保证全局最优 | 恰当应用该控制策略,在燃料电池混动汽车运行平稳时,可限制燃料电池功率波动300 W以内,同时,使电池处于一个良好的SOC状态 |
MPC策略 | 鲁棒性强,稳定性好, 采用反馈校正和滚动优化 | 依赖工程经验,适应性不强,计算效率不高 | 适用于实时应用场景,可优化电池SOC波动和燃料电池功率波动 |
控制策略 | 优点 | 缺点 |
---|---|---|
ECMS | 最大限度地减少燃料消耗和维持电池SOC所需的等效消耗[ | 难以保证全局最优 |
逻辑门限控制策略 | 控制过程简单,具有较强的实用性,控制运算效率较高,较好地 提高了燃油的经济性 | 控制逻辑复杂、波动大,对经验数据依赖性较大 |
Tab. 5 Comparison of the logic threshold control strategy and ECMS-based EMS
控制策略 | 优点 | 缺点 |
---|---|---|
ECMS | 最大限度地减少燃料消耗和维持电池SOC所需的等效消耗[ | 难以保证全局最优 |
逻辑门限控制策略 | 控制过程简单,具有较强的实用性,控制运算效率较高,较好地 提高了燃油的经济性 | 控制逻辑复杂、波动大,对经验数据依赖性较大 |
具体控制策略 | 优点 | 缺点 | 应用 |
---|---|---|---|
基于神经网络的能量管理策略 | 有较强的学习和自适应能力[ | 数据挖掘困难、耗时,成功训练神经 网络需要较长的时间 | 具有独特的非线性自适应信息处理能力,可用于解决 复杂多变量问题的最优解[ |
基于Q-learning的能量管理策略 | 无模型,可实时自主学习最优策略[ | 创建数据库需要耗费大量时间,需要 复杂的人工智能知识 | 适用于系统模型位置、不准确或不断变化的应用[ |
Tab.6 Advantages, disadvantages and applications of EMS based on neural network and Q-learning algorithm
具体控制策略 | 优点 | 缺点 | 应用 |
---|---|---|---|
基于神经网络的能量管理策略 | 有较强的学习和自适应能力[ | 数据挖掘困难、耗时,成功训练神经 网络需要较长的时间 | 具有独特的非线性自适应信息处理能力,可用于解决 复杂多变量问题的最优解[ |
基于Q-learning的能量管理策略 | 无模型,可实时自主学习最优策略[ | 创建数据库需要耗费大量时间,需要 复杂的人工智能知识 | 适用于系统模型位置、不准确或不断变化的应用[ |
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