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电动汽车应用中的离子电池基于EKF的SOC估计的硬件实现

G Nishanth¹, M Murali Krishnan¹, Balamurugan Parandhaman²

¹School of Electrical Engineering, Vellore Institute of Technology, Chennai, India.

Scientific reports

|May 3, 2025

概括

精确监测离子电池的充电状态 (SOC) 是一个挑战. 这项研究展示了一种扩展卡尔曼过 (EKF) 方法,可以达到超过98%的准确性,以可靠地估计老化电池中的SOC.

科学领域:

电池技术电池技术
电气工程电气工程
材料科学材料科学材料科学

背景情况:

离子电池在电动汽车,无人机和便携式电子设备中至关重要.
精确的充电状态 (SOC) 监控是这些电池面临的重大挑战.
像库伦计数这样的现有估计技术存在局限性.

研究的目的:

开发和演示用于离子电池的准确SOC估计方法.
提高SOC监控的可靠性,特别是在老化的电池中.
通过软件和硬件实现来验证拟议的方法.

主要方法:

扩展卡尔曼过 (EKF) 方法的实施.
使用静态容量和混合脉冲功率表征测试.
采用自适应模型方法,对参考SOC点 (90%和10%) 进行优化,并使用终端电压导数更新电池模型.

主要成果:

该EKF方法在测量和估计的SOC值之间实现了2%的误差.
与传统方法相比,SOC预测准确度超过98%.
在EKF方程中的协差矩阵有助于可靠的SOC预测.

结论:

开发的基于EKF的自适应模型为离子电池中SOC估计提供了高度准确和可靠的方法.
这种方法对老化的电池特别有效,解决了电池管理系统的关键需求.
展示的软件和硬件验证证实了拟议技术的实际适用性.

关键词:

电动汽车电动汽车是什么相当的电路模型的电路模型.扩展的卡尔曼波器过器离子电池是一种离子电池.收费状态收费状态

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