结合深度学习和观察者来改善流电池的充电状态估计
Alejandro Clemente1, Thomas Puleston2, Andreu Cecilia2
1Polytechnic University of Catalonia (UPC), Avinguda Diagonal, 647, 08020, Barcelona, Spain; Group of Power Systems, Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre, 1, 08930, Sant Adrià del Besòs, Spain.
ISA transactions
|January 9, 2025
概括
这项研究引入了一个深度学习观察器,用于准确估计流电池电荷状态 (SoC). 它使用终端电压和神经网络来提高准确性和简化建模.
科学领域:
- 电化学 电化学 电化学
- 储能系统 储能系统 储能系统
- 机器学习应用 机器学习应用
背景情况:
- 准确的电荷状态 (SoC) 估计对于流电池 (VFB) 的性能和管理至关重要.
- 经典的观察方法通常依赖于复杂的物理模型,在动态条件下可能缺乏准确性.
- 超电位建模是改善SoC估计的关键,但存在重大挑战.
研究的目的:
- 开发和实施一种基于深度学习的新型观察器,用于在VFB中精确估计SoC.
- 直接利用终端电压,并使用机器学习来模拟超电位,以提高准确性.
- 为了减少VFB的传统SoC估计模型的复杂性.
主要方法:
- 通过训练一个神经网络,使用经典观察者的数据来模拟电池的过度潜力.
- 经典观察者使用物理电化学模型和开放电路电压来估计物种度.
- 训练过的超电位模型被整合到观察者中,以改进 SoC 估计.
主要成果:
- 深度学习观察者证明了对VFB的有效SoC估计.
- 通过数值模拟和实验研究的验证证实了该方法的可靠性.
- 拟议的方法在SoC估计中实现了不到5%的相对误差.
结论:
- 基于深度学习的观察器为VFB SoC估计提供了一个高度准确且不那么复杂的解决方案.
- 直接使用终端电压和基于ML的超电位建模是电池管理的一个有希望的方法.
- 经过验证的方法为流电池提供可靠的实时SoC监控.
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