Enhanced Identification of Battery Models for Real-Time Battery Management
Enhanced Identification of Battery Models for Real-Time Battery Management
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DOI：
10.1109/tste.2011.2116813
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发表时间：
2011-02
影响因子：
8.8
通讯作者：
Mark Sitterly;L. Wang;G. Yin;Caisheng Wang
中科院分区：
文献类型：
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作者：
Mark Sitterly;L. Wang;G. Yin;Caisheng Wang
可再生能源发电、汽车电气化和智能电网严重依赖储能设备来提高运行、可靠性和效率。电池系统由许多电池单元组成，即使是新的电池单元也具有不同的特性，并且由于老化、操作条件和化学性质变化等各种因素而随着时间和操作条件的变化而变化。他们的有效管理需要高保真的模型。本文的目的是开发识别算法，捕捉每个电池单元的个性化特征，并实时生成更新的模型。结果表明，典型的电池模型可能无法识别，独特的电池模型特征需要修改输入/输出表达式，标准的最小二乘方法会遇到辨识偏差。本文设计了改进的模型结构和辨识算法来解决这些问题。严格建立了系统可辨识性、算法收敛、辨识偏差和偏差校正机制。以典型的电池模型结构为例说明了该方法的实用性。
Renewable energy generation, vehicle electrification, and smart grids rely critically on energy storage devices for enhancement of operations, reliability, and efficiency. Battery systems consist of many battery cells, which have different characteristics even when they are new, and change with time and operating conditions due to a variety of factors such as aging, operational conditions, and chemical property variations. Their effective management requires high fidelity models. This paper aims to develop identification algorithms that capture individualized characteristics of each battery cell and produce updated models in real time. It is shown that typical battery models may not be identifiable, unique battery model features require modified input/output expressions, and standard least-squares methods will encounter identification bias. This paper devises modified model structures and identification algorithms to resolve these issues. System identifiability, algorithm convergence, identification bias, and bias correction mechanisms are rigorously established. A typical battery model structure is used to illustrate utilities of the methods.