信息与控制2017，Vol.46Issue(5)：519-524,6.DOI:10.13976/j.cnki.xk.2017.0519
基于交互式多模型卡尔曼滤波的电池荷电状态估计
Battery State-of-charge Estimation Using Interactive Multiple-model Kalman Filter
摘要
Abstract
A novel state-of-charge ( SOC) estimator is presented based on a combination of the interactive multi-model algorithm and the extended Kalman filter. The estimator is used in SOC estimation for nonlinear sys-tems of lithium-ion batteries. First, the dynamic characteristics of the lithium-ion battery are described by two Thevenin circuit models, which have different parameters. Then, interactive multi-model extended Kalman filter and conventional extended Kalman filter are applied in numerical simulations to estimate the SOC in ca-ses of hybrid pulse power characterization and urban dynamometer driving schedule, and then in a hardware experiment in case of constant current discharge. An analysis of results shows the effectiveness of interactive multi-model extended Kalman filter and its advantage over conventional methods with respect to estimation errors. The added computational cost of the new estimator is reasonable.关键词
荷电状态/交互式多模型/扩展卡尔曼滤波/锂离子电池Key words
state-of-charge/interactive multiple-model/extended-Kalman-filter/Lithium-ion batteries分类
信息技术与安全科学引用本文复制引用
夏小虎,刘明..基于交互式多模型卡尔曼滤波的电池荷电状态估计[J].信息与控制,2017,46(5):519-524,6.基金项目
安徽省自然科学基金资助项目(1408085MF134) （1408085MF134）
安徽省高校优秀青年骨干人才国内外访学研修重点项目(gxfxZD2016224) （gxfxZD2016224）
