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A model for state-of-health estimation of lithium ion batteries based on charging profiles

机译:基于充电曲线的锂离子电池健康状态估计模型

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摘要

Using an equivalent circuit model to characterize the constant-current part of a charging/discharging profile, a model is developed to estimate the state-of-health of lithium ion batteries. The model is an incremental capacity analysis-based model, which applies a capacity model to define the dependence of the state of charge on the open circuit voltage as the battery ages. It can be learning-free, with the parameters subject to certain constraints, and is able to give efficient and reliable estimates of the state-of-health for various lithium ion batteries at any aging status. When applied to a fresh LiFePO4 cell, the state-of-health estimated by this model (learning-unrequired or learning-required) shows a close correspondence to the available measured data, with an absolute difference of 0.31% or 0.12% at most, even for significant temperature fluctuation. In addition, NASA battery datasets are employed to demonstrate the versatility and applicability of the model to different chemistries and cell designs. (C) 2019 Elsevier Ltd. All rights reserved.
机译:使用等效电路模型来表征充电/放电曲线的恒定电流部分,从而开发出一种模型来估算锂离子电池的健康状态。该模型是一个基于增量容量分析的模型,该模型应用一个容量模型来定义随着电池老化,充电状态对开路电压的依赖性。它可以免于学习,其参数受某些约束,并且能够在各种老化状态下对各种锂离子电池进行有效而可靠的健康状态估算。当应用于新鲜的LiFePO4电池时,此模型估算的健康状态(无需学习或需要学习)显示与可用的测量数据非常接近,绝对差最大为0.31%或0.12%,即使温度波动很大。此外,NASA电池数据集用于证明该模型的多功能性和适用于不同化学和电池设计。 (C)2019 Elsevier Ltd.保留所有权利。

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