Maximizing Parameter Identifiability of an Equivalent-Circuit Battery Model Using Optimal Periodic Input Shaping
Maximizing Parameter Identifiability of an Equivalent-Circuit Battery Model Using Optimal Periodic Input Shaping
复制标题
使用最佳周期性输入整形最大化等效电路电池模型的参数可辨识性
DOI：
10.1115/dscc2014-6272
复制
发表时间：
2014
影响因子：
9.2
通讯作者：
H. Fathy
中科院分区：
文献类型：
--
作者：
Michael Rothenberger;J. Anstrom;S. Brennan;H. Fathy
本文对锂离子电池的周期性循环进行了整形，以最大限度地提高电池的参数可识别性。本文的动机是需要更快，更准确的锂离子电池诊断，特别是在运输方面。电池参数可识别性差使得诊断具有挑战性。现有文献通过使用Fisher信息量化电池参数可识别性来解决这一挑战，并表明测试轨迹优化可以提高可识别性。一个限制是该文献的重点是从多电池实验室循环测试离线估计电池模型参数。相反，本文的动机是通过对目标电池或电池的在线健康估计。本文探讨了线性和非线性二阶等效电路电池模型的“有针对性的估计”问题。这些模型的简单性导致在线性情况下的分析最优解，提供见解，以指导非线性情况下的优化问题的设置。作为这种优化的结果，参数估计精度显著提高。本文针对多种电气化车辆配置演示了这一改进。版权所有© 2014 by ASME
This paper shapes the periodic cycling of a lithium-ion battery to maximize the battery’s parameter identifiability. The paper is motivated by the need for faster and more accurate lithium-ion battery diagnostics, especially for transportation. Poor battery parameter identifiability makes diagnostics challenging. The existing literature addresses this challenge by using Fisher information to quantify battery parameter identifiability, and showing that test trajectory optimization can improve identifiability. One limitation is this literature’s focus on offline estimation of battery model parameters from multi-cell laboratory cycling tests. This paper is motivated, in contrast, by online health estimation for a target battery or cell. The paper examines this “targeted estimation” problem for both linear and nonlinear second-order equivalent-circuit battery models. The simplicity of these models leads to analytic optimal solutions in the linear case, providing insights to guide the setup of the optimization problem for the nonlinear case. Parameter estimation accuracy improves significantly as a result of this optimization. The paper demonstrates this improvement for multiple electrified vehicle configurations.Copyright © 2014 by ASME