锂离子动力电池状态与参数自适应联合估计理论研究

项目名称： 锂离子动力电池状态与参数自适应联合估计理论研究

项目编号： No.51477009

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 电工技术

项目作者： 张彩萍

作者单位： 北京交通大学

项目金额： 80万元

中文摘要： 锂离子电池荷电状态是对电池充放电控制的基本依据，与电池温度、工作电流以及可用容量紧密相关，其影响因素复杂且具有耦合特点，荷电状态估计问题已成为制约电池安全、可靠及长寿命使用的瓶颈。 如何在全寿命周期工况使用条件下准确获得电池参数，是实现荷电状态高精度估计的核心问题。项目通过开展电池充放电过程化学反应机理及外特性研究，结合时域和频域特征分析，建立电池动态充放电模型，提出具有电化学意义的模型参数确定方法；开展电池模型参数对荷电状态估计敏感度定量分析方法研究，提出影响荷电状态估计的关键参数；开展不同使用路径的电池寿命衰退机制研究，深入揭示外部特征参数随电池温度、电流及老化的变化规律，采用数据驱动方法，提出全寿命周期模型参数高精度估计方法；开展电池数据量测噪声特性估计方法研究，分析量测噪声特性及其相关性对电池荷电状态估计的影响，形成一种含量测噪声特性的电池荷电状态与参数的自适应联合估计新方法。

中文关键词： 锂离子动力电池；动态模型；荷电状态；自适应；联合估计

英文摘要： The state of charge of lithium ion battery is the basic foundation for batteries charging and discharging control，it is also closely related with temperature, working current and available capacity, and the influence factors of SOC is complex and coupling with each other. The SOC estimation problem has already become the bottleneck which restricts the safe，reliable and long lifetime usage of batteries. How to accurately acquire batteries parameters under the condition of whole life cycle is the core issue to achieve accurate SOC estimation. From conducting research on the chemical reaction mechanism and external characteristic of the charging and discharging process, combined with the time domain and frequency domain analysis, this project establishes the dynamic charging and discharging model of lithium ion battery, develops the method for determining the model parameters with electrochemical significance. Conducting the research on quantitative analysis method of batteries models parameter on the SOC estimation, the essential parameters influencing the SOC estimation are determined. The degradation mechanism of lithium ion batteries based on different usage paths is studied, deeply revealing the changing rules of external characteristic parameters varying with the battery temperature, current and aging. Using the data driven method, the high accurate estimation method for the model parameters of whole life cycle is proposed. The measurement noise characteristics estimation method is studied, and the influence of noise characteristics and their correlations on the SOC estimation is investigated. A novel adaptive joint estimation method between the batteries and parameters and SOC with measurement noise characteristics is developed.

英文关键词： Traction lithium-ion batteries;dynamic model;state of charge;adaptive;integrated estimation