基于ISCSO-ELM算法对氧化流量电池的SOC估计
Dong Xiao1,2, Boyan Li1,2, Jiawei Shan1,2,3
1School of Information Science and Engineering, Northeastern University, 110819 Shenyang, China.
ACS omega
|December 11, 2023
概括
这项研究通过开发电化学模型和改进的ISCSO-ELM算法来计算容量衰减,提高预测准确度,提高了氧还原流电池 (VFB) 的电荷状态 (SOC) 估计.
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
- 电池管理 电池管理
背景情况:
- 氧化还原流电池 (VFBF) 对于电网规模的能源存储至关重要.
- 准确的电荷状态 (SOC) 估计对于VFB的运行效率和寿命至关重要.
- 电池容量衰减对可靠的SOC预测具有重大挑战.
研究的目的:
- 为VFBs开发一个准确的电化学模型.
- 将一个改进的沙猫群优化算法与极端学习机器 (ISCSO-ELM) 集成,以提高SOC估计.
- 为了解决产能下降对VFB SOC预测的影响.
主要方法:
- 建立了一种电化学模型来推导电池的关键参数,包括离子度.
- 一个极端学习机器 (ELM) 与一个改进的沙猫群优化算法 (ISCSO) 结合起来.
- 在SOC估计中应用了ISCSO-ELM模型,考虑到VFB容量下降.
主要成果:
- 电化学模型成功地为SOC估计提供了基本参数.
- ISCSO-ELM方法在预测VFB的SOC方面表现出更好的有效性.
- 综合方法显示,尽管产能下降,SOC估计的准确性提高了.
结论:
- 拟议的电化学模型和ISCSO-ELM方法为VFB SOC估计提供了可靠的解决方案.
- 这种方法有效地缓解了VFB容量下降带来的挑战.
- 这些发现有助于更可靠,更有效地运行氧化还原流电池.
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