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）是现代电气化驱动链的关键组成部分。除了安全功能之外，有效的电池管理系统还可以完美地使用电池本身，并显著延长车辆电池的使用寿命，从而降低电动车辆的整体成本。对于有效的BMS，电池模型必须基于（1）在线可用和（2）模型的物理激励参数。这是本项目的目的耦合参数识别方法在线电缆与非参数方法产生物理的理解。要开发的在线方法识别基于电池的分数模型，由于其特定的结构，其参数通过新的识别程序识别，因此可以进行物理解释。该过程的基础上的想法“分布的弛豫时间”，已经成功地应用于燃料电池。然而，它需要通过一种新的方法来计算微分容量的分布进行扩展。IRS在控制工程领域的能力和IWE在阻抗谱领域的能力在这方面相互补充。

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

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

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

Bias-free Parameter Identification for Non-Commensurable Fractional Systems

• DOI: 10.23919/ecc51009.2020.9143681
• 发表时间: 2020-05
• 期刊: 2020 European Control Conference (ECC)
• 作者: Oliver Stark;M. Pfeifer;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