锂离子电池电流效能预测方法研究  

Lithium-Ion Battery Current Efficiency Prediction Method Research

作　　者：程志辉[1,2] 黄宇[1,2] CHENG Zhi - hui HUANG Yu(The School of Mechanical Engineering of Hubei University of Technology, Wuhan Hubei 430068, China The Library Center of Hubei University of Technology, Wuhan Hubei 430068, China)

机构地区：[1]湖北工业大学机械工程学院,湖北武汉430068 [2]湖北工业大学图书管理中心,湖北武汉430068

出　　处：《计算机仿真》2017年第4期129-132,297,共5页Computer Simulation

基　　金：国家自然科学基金项目(51179197)

摘　　要：锂离子电池电流效能预测为电池健康管理提供了重要手段。现有的预测方法忽略了欧姆内阻的温度变化效应,预测精度较低。针对上述问题,提出了一种双模型相关滤波电池电流效能预测方法。首先,对锂电池和内阻抗进行分开建模,在电池Thevenin模型的基础上构建了内阻抗预测模型,实时修正模型参量;接着,在粒子滤波的框架内嵌入噪声相关处理方法,降低测量噪声对估计精度的扰动,增强内阻抗变化对状态估计的修正作用。实验结果表明,同传统方法相比,所用方法较好地提升了电流效应预测精度和抗干扰能力。Lithium - ion battery current efficiency prediction is an important means of battery health management, but the existing prediction methods have low prediction accuracy because of ignoring the ohm internal resistance temperature change effect. In order to solve this problem, this paper proposes a new lithium - ion battery current efficiency prediction method based on double model correlation filtering. First of all, lithium - ion battery and internal impedance are modeled separately. The battery internal resistance prediction model is established based on the Thevenin model, and the model parameters can be corrected in real - time. Second, noise relative processing method is embedded in the framework of particle filter, which reduces the disturbance of measurement noise, and strengthens the state estimation correction effect by the internal impedance varying. The experimental results show that, compared with traditional methods, this method improves the prediction precision and anti -interference ability.

关 键 词：锂离子电池 荷电状态 健康状态 相关滤波 

分 类 号：TP912[自动化与计算机技术]