Energy Storage Science and Technology ›› 2020, Vol. 9 ›› Issue (3): 657-669.doi: 10.19799/j.cnki.2095-4239.2019.0263
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Received:
2019-11-15
Revised:
2020-03-01
Online:
2020-05-05
Published:
2020-05-11
CLC Number:
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.
Table 1
Analysis of advantages and disadvantages of lithium battery model"
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 | |||
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 |
Table 4
Comparison of advantages and disadvantages of lithium battery health assessment methods"
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; |
Table 6
Comparison of advantages and disadvantages of RUL prediction methods"
RUL预测方法 | 优点 | 缺点 | |
---|---|---|---|
基于模型的RUL方法 | 衰退机理模型 | 模型精度高 | 复杂程度高,无法实现在线预测;对测试仪器要求比较高;测试周期长、成本高; |
经验衰退模型 | 工作相对较小量 ;降低成本 | 预测精度对建模精度依赖性较大 | |
等效电路模型 | 实现难度低 | 不能体现部分电化学反应的隐含关系 | |
基于数据驱动的RUL方法 | 方法 基于统计模型 | 数据需求量少,预测方法易于实现,预测精度较高 | 不同电池型号需要建立针对性模型;相同电池型号在不同劣化程度的RUL 预测不同;模型适应性较差; |
人工智能方法 | 预测精度高 | 预测精度依赖数据量和数据来源 |
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