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SOC估计的碳电池基于GA-MIUKF算法

Lu Wang¹, Feng Wang², Liju Xu¹

¹School of Machinery and Transportation, Southwest Forestry University, Kunming, 650224, China.

Scientific reports

|February 9, 2024

概括

本研究引入了一种新方法,用于估计使用基因算法多创新无气味卡尔曼波器 (GA-MIUKF) 的碳电池的电荷状态 (SOC). GA-MIUKF算法显著提高了这些电池的SOC估计准确性.

科学领域:

电池技术电池技术
电化学工程电化学工程
计算智能是一种计算智能.

背景情况:

准确的电荷状态 (SOC) 估计对于碳电池的性能和安全至关重要.
现有的估计方法可能缺乏精度,特别是在动态操作条件下.
优化电池模型参数和过器噪声对于提高SOC估计准确性至关重要.

研究的目的:

通过GA-MIUKF算法为碳电池提出并验证一种新的SOC估计方法.
通过优化电池模型和噪声参数来提高SOC估计的精度.
将拟议的GA-MIUKF方法与传统的UKF和MI-UKF算法的性能进行比较.

主要方法:

为碳电池开发同等电路模型.
基因算法 (GA) 的应用,用于电池模型参数和MI-UKF噪声差异参数的全球优化.
实施和对多创新无气味卡尔曼过器 (MI-UKF) 和无气味卡尔曼过器 (UKF) 算法的比较分析.

主要成果:

在碳电池的SOC估计中,GA-MIUKF算法表现出卓越的性能.
拟议的方法实现了2.0%的平均估计误差,超过了标准UKF和MI-UKF算法.
使用GA优化参数显著促进了MI-UKF算法的精度提高.

结论:

GA-MIUKF算法为碳电池 SOC 估计提供了一个高度准确和有效的方法.
整合GA用于参数优化,提高了MI-UKF波器的稳定性和精度.
这项研究为改善碳电池应用的电池管理系统提供了宝贵的贡献.

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