Identification of lithium-ion-battery-cells based on physically interpretable models for battery state estimation during vehicle operation

基于物理可解释模型的锂离子电池单元识别，用于车辆运行期间的电池状态估计

• 批准号: 270869123
• 负责人: Professor Dr.-Ing. Sören Hohmann
• 金额: --
• 依托单位: Institut für Regelungs- und Steuerungssysteme (IRS)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/270869123?language=en
• 关键词: Identification; lithium; ion; battery; cells

A battery management system (BMS) is a key component of a modern electrified drive chain. In addition to safety functions an effective battery management system yields a perfect usage auf the battery itself and contributes significantly to a long lifetime of the vehicle’s battery and hence reduces the overall costs of an electric vehicle. For an effective BMS it is necessary that battery models base on (1) online available and (2) physically motivated parameters of the model. It is the aim of this project to couple parametric identification methods being online cable with non-parametric methods yielding a physical understanding. The online method identification to be developed bases on a fractional model of the battery, which allows a physical interpretation due to its specific structure, the parameters of which are identified by a new identification procedure. The procedure bases on the idea of “distribution of relaxation times” which has already been successfully applied to fuel cells. However it needs to be extended by a new method calculating the distribution of differential capacities. The competences of the IRS in the field of control engineering and the competences of the IWE in the field of impedance spectroscopy complement each other perfectly in this respect.

电池管理系统（BMS）是现代电气驱动链的关键组成部分。除了安全功能外，有效的电池管理系统还可以使电池本身产生完美的用法，并为车辆电池的长寿命做出了重大贡献，从而降低了电动汽车的整体成本。对于有效的BMS，必须将电池模型基于（1）在线可用和（2）模型的物理动机参数。该项目的目的是将参数识别方法与非参数方法进行在线电缆，从而产生物理理解。在线方法标识是要在电池的分数模型上开发的基础，该模型允许其特定的结构引起物理解释，其参数通过新的识别过程识别。该程序基于“放松时间分布”的想法，该概念已经成功地应用于燃料电池。但是，需要通过一种计算差异能力分布的新方法来扩展它。在这方面，美国国税局在控制工程领域的能力以及IWE在阻抗光谱领域的能力完美完成。

项目成果

Controllability and Energy-Optimal Control of Time-Variant Fractional Systems

• DOI: 10.1109/cdc.2018.8619785
• 发表时间: 2018-12
• 期刊: 2018 IEEE Conference on Decision and Control (CDC)
• 作者: Marius Eckert;K. Nagatou;Felix Rey;Oliver Stark;S. Hohmann

Computation of Orders of a Commensurable Fractional Order Model

• DOI: 10.1109/cdc40024.2019.9029742
• 发表时间: 2019-12
• 期刊: 2019 IEEE 58th Conference on Decision and Control (CDC)
• 作者: Oliver Stark;Martin Kupper;Stefan Krebs;S. Hohmann

Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs

• DOI: 10.1109/ccdc.2018.8408010
• 发表时间: 2018-06
• 期刊: 2018 Chinese Control And Decision Conference (CCDC)
• 作者: Martin Kupper;J. Brenneisen;Oliver Stark;Stefan Krebs;S. Hohmann

Current and State of Charge Estimation of Lithium-Ion Battery Packs Using Distributed Fractional Extended Kalman Filters

• DOI: 10.1109/ccta.2019.8920558
• 发表时间: 2019-08
• 期刊: 2019 IEEE Conference on Control Technology and Applications (CCTA)
• 作者: Martin Kupper;A. Creutz;Oliver Stark;Stefan Krebs;S. Hohmann

Unknown Input Kalman Filtering for Linear Discrete-Time Fractional Order Systems With Direct Feedthrough

• DOI: 10.23919/ecc.2019.8795817
• 发表时间: 2019-06
• 期刊: 2019 18th European Control Conference (ECC)
• 作者: Martin Kupper;M. Pfeifer;Stefan Krebs;S. Hohmann