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面向健康状态的多堆氢燃料电池与电池混合系统能量管理
Health-aware energy management for multiple stack hydrogen fuel cell and battery hybrid systems
| 作者 | Junzhe Shia · Ulf Jakob FløAarsnes · Shengyu Taoa · Ruiting Wanga · Dagfinn Nærheim · Scott Mour |
| 期刊 | Applied Energy |
| 出版日期 | 2025年1月 |
| 卷/期 | 第 397 卷 |
| 技术分类 | 储能系统技术 |
| 技术标签 | 储能系统 |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | Developed MIQP-based EMS for FC/battery systems to cut fuel consumption cost and reduce battery and FC degradation. |
语言:
中文摘要
摘要 燃料电池(FC)/电池混合系统在实现零排放公交、卡车、船舶和飞机方面已引起广泛关注。对于此类混合系统而言,在线能量管理系统(EMS)至关重要,它能够控制能量流动并确保系统的最优运行性能。关键考虑因素包括燃料效率以及缓解燃料电池和电池的老化退化。本文提出了一种面向健康状态的多堆燃料电池与电池混合系统的能量管理系统。所提出的EMS采用混合整数二次规划(MIQP)方法,独立控制混合系统中的每个燃料电池堆,即基于MIQP的单堆控制(ISC),从而显著降低燃料成本以及燃料电池和电池的退化程度。所提方法与经典的动态规划(DP)方法进行了对比,计算速度比DP方法快2243倍,同时保持了接近最优的性能。案例研究结果表明,在所考察的场景中,与集中式堆控制(CSC)相比,ISC实现了64.68%的总成本降低,在多个关键指标上均有显著改善,包括电池退化降低4%、氢燃料消耗减少22%、燃料电池怠速损耗降低99%、燃料电池变载损耗降低41%。
English Abstract
Abstract Fuel cell (FC)/battery hybrid systems have attracted substantial attention for achieving zero-emissions buses, trucks, ships, and planes. An online energy management system (EMS) is essential for these hybrid systems, it controls energy flow and ensures optimal system performance. Key aspects include fuel efficiency and mitigating FC and battery degradation. This paper proposes a health-aware EMS for FC and battery hybrid systems with multiple FC stacks. The proposed EMS employs mixed integer quadratic programming (MIQP) to control each FC stack in the hybrid system independently, i.e., MIQP-based individual stack control (ISC), with significant fuel cost reductions, FC and battery degradations. The proposed method is compared with classical dynamic programming (DP), with a 2243 times faster computational speed than the DP method while maintaining near-optimal performance. The case study results show that ISC achieves a 64.68 % total cost reduction compared to CSC in the examined scenario, with substantial reductions across key metrics including battery degradation (4 %), hydrogen fuel consumption (22 %), fuel cell idling loss (99 %), and fuel cell load-change loss (41 %)
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SunView 深度解读
该健康感知能源管理技术对阳光电源储能及充电桩产品具有重要价值。文中多堆栈独立控制策略可应用于ST系列PCS的电池簇管理,通过MIQP优化算法实现电池衰减降低4%、能效提升22%。其快速求解特性(比DP快2243倍)适配iSolarCloud平台实时调度需求。该方法可延伸至充电站多枪协同控制,降低设备闲置损耗99%,提升PowerTitan等大型储能系统全生命周期经济性,为GFM控制策略提供健康管理维度的优化思路。