锂离子电池全生命周期内评估参数及评估方法综述

doi:10.19799/j.cnki.2095-4239.2019.0263

摘要/Abstract

摘要：

自锂离子电池得到广泛应用以来，为实现锂离子电池性能的充分应用，从不同角度对其性能展开研究。准确描述电池内部工作原理，评估电池当前工作状态和性能，以及预测电池未来工作能力，是提高储能系统安全性、可靠性和可用性的重要基础。对锂离子电池的研究工作从内部原理出发，归纳整理锂离子电池的建模方法，对不同建模方法的优缺点进行分析对比；汇总整理可以表征电池当前工作状态、性能和未来工作能力的特性参数：荷电状态SOC、健康状态SOH以及剩余寿命RUL，并汇总分析预测该参数的计算思路及相关数学方法，通过分类归纳不同的解决思路和数学方法，分析其优缺点。通过上述工作，总结当前锂离子电池全生命周期内研究评估的工程实用性方法，并指出未来的研究方向和热点。

关键词: 锂离子电池, 建模方法, 荷电状态估计, 健康状态评估, 寿命预测, 全生命周期评估

Abstract:

The final goal is to make full use of lithium battery. In order to realize it, lithium battery performance has been studied from different perspectives since it has been widely used. It is an important foundations for improving the safety, reliability and availability of the energy storage system that include accurately describing internal working principle, evaluating the current operation state and predicting future working ability of battery. In order to achieve the final goal, the research work starts from internal principle study. After summarizing and arranging lithium battery modeling methods, it analyzes and compares the advantages and disadvantages of different modeling methods. Then it summarizes many characteristic parameters that can represent the current operation state, health state and future working ability of the battery, such as state of charge （SOC）, state of health （SOH） and remaining useful life （RUL）. This paper compares the advantages and disadvantages of many SOC traditional estimation methods. Then it point that SOC estimation method based on battery model is common application method. It summarizes the characteristics of mathematical algorithm used in SOC estimation method based on battery model, and point put different characteristic of these algorithm. Then it point out that the latest research hotspot is SOC estimation method based on fusion model, then explain its advantages and risks. For SOH evaluation method, it summarizes three different evaluation parameters and SOH estimating method based on different evaluation parameters. After summarizing and comparing, it is widely application engineering method to estimating SOH through impedance of equivalent circuit of battery model. And the hot research direction in the future is SOH estimation based on fusion model. Then it analyzes advantages and disadvantages of mathematical algorithm involved in different SOH estimation methods. This paper summarizes and classifies RUL estimation methods. After analyzing and comparing, the important methods are RUL based on empirical decay model and RUL based on artificial intelligence algorithm. Then it gives several mature cases based on empirical decay model and sorts out the research cases of RUL based on fusion model as future hotspot research. Through evaluation methods of different application stages in lithium battery life cycle, such as SOC, SOH and RUL, which can engineering practicability realize battery state evaluation, it provides accurate quantitative analysis basis for optimization useful of battery system.

Key words: lithium battery, modeling methods, state of charge, state of health, remaining useful life, full life cycle assessment

中图分类号:

TM 911

卢婷, 杨文强. 锂离子电池全生命周期内评估参数及评估方法综述[J]. 储能科学与技术, 2020, 9(3): 657-669.

LU Ting, YANG Wenqiang. Review of evaluation parameters and methods of lithium batteries throughout its life cycle[J]. Energy Storage Science and Technology, 2020, 9(3): 657-669.

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	电池模型	优点	缺点
电化学模型	准二维模型	贴合电池特性	计算量大，无法获得其解析解
	简化准二维模型	贴合电池特性	计算量大，无法获得其解析解
	单粒子模型	计算量小	大倍率充放电条件下不准确
黑箱模型		避开分析电池内部特性	无法对映射关系进行机理性解释；计算精度依赖训练数据量；
等效电路模型	Rint模型	简单	适用范围较小；偏差大
	RC模型	简单	偏差大
	Thevenin模型；PNGV模型	模型简单，计算量小，具有较好的实用价值	不能表现电池稳态电压变化等特性
	二阶RC模型；GNL 模型	精度高	模型复杂

分类		优点	缺点
离线方法	负载放电法	简单	需要离线测试，多用于实验室；实际系统不适用
离线方法	内阻法	内阻与SOC密切相关	采用离线测试；实际应用较少；
在线方法	安时计量法	算法简单，易于工程实现	误差会逐渐累积；受电流、电池老化、温度等方面的影响；需采用其他辅助手段确定SOC初值；
在线方法	开路电压法	方法简单；能够自动给出 SOC 的初始值	对电压信号的波动很敏感；

方法	优点	缺点
卡尔曼滤波法	较强的初始误差修正能力；抑制噪声信号很强；	在非线性系统线性化的过程中会引入误差
神经网络法	很适合模拟非线性特性	需要大量、全面的样本数据进行训练
观测器方法	精度高稳定性好	估计算法的精度与模型的精度有关；电池模型需要保证SOC的可观性，设计难度大；

评估方法	优点	缺点
电池老化机理的评估	对电池机理分析清晰	电池内部反应精确描述很难实现；适用于电池设计研究；
基于实验测试的评估	实现方法简单	实用性太差，时间成本太高；SOH的算法较为繁琐、测试结论与在线应用的估计误差较大
基于电池模型的评估	模型结构清晰，内部物理含义清晰	电池系统的非线性程度对估计的精度会产生很大的影响
基于数据驱动的评估	不依赖电池模型；大量数据分析可以提高精度；	需要大量数据支撑；

模型	优点	缺点
滑模观测器	对SOH的估算精度比较高	要求构建电池模型可观测，实现过程非常复杂；应用于SOH估计的案例较少
卡尔曼滤波	应用广泛，便于实现；较强的初始误差修正能力；抑制噪声信号很强；	电池非线性问题带来估算误差
粒子滤波	采样集中在高概率区域，具有不同概率分布的采样近似	应用案例较少