退役电池特征指标提取和处理方法综述

doi:10.19799/j.cnki.2095-4239.2025.0149

摘要/Abstract

摘要：

动力电池在老化过程中出现容量衰减、内阻增大和一致性下降等一系列特征变化，虽然这些特征变化给退役电池梯次利用安全性带来巨大挑战，但也成为电池状态评估和筛选的重要依据。首先，分析国内外政策法规在推动和规范梯次利用发展中的作用，并结合工程实例剖析安全隐患，提出特征指标提取与处理方法在效率和精度方面的要求。其次，围绕“模型-测试-算法”框架，创新性地将特征指标提取与处理分为“侧重于效率”和“侧重于精度”两大类方法，探讨在特征指标提取和处理过程中，测试手段和智能算法如何提高效率；介绍多维特征指标提取和分阶段的特征指标应用的过程中，模型与算法如何提高精度。最后，结合政策文献要求，对各类方法进行总结和对比，为迎接2030年即将来临的“退役电池浪潮”提供理论依据。

关键词: 退役电池, 状态评估, 梯次利用, 特征指标, 电池筛选

Abstract:

During the aging process of power batteries, characteristic changes such as capacity degradation, increased internal resistance, and decreased consistency occur. Although these changes present significant challenges to the safety of retired battery (RB) cascade utilization, they also form an important basis for battery state assessment and screening. This paper first analyzes the role of domestic and international policies and regulations in promoting and regulating the development of cascade utilization and examines safety hazards using engineering examples. The requirements for the extraction and processing methods of characteristic indicators (CI) in terms of efficiency and accuracy are proposed. Focusing on the "model-testing-algorithm" framework, the extraction and processing of CI are innovatively divided into two major categories: "emphasizing efficiency" and "emphasizing accuracy." The paper discusses how testing methods and intelligent algorithms can improve efficiency in CI extraction and processing, and it introduces how models and algorithms can enhance accuracy in multi-dimensional CI extraction and phased application. Finally, in combination with policy document requirements, various methods are summarized and compared to provide a theoretical basis for the forthcoming "retired battery wave" expected around 2030.

Key words: retired batteries (RB), state assessment, cascade utilization, characteristic indicators (CI), battery screening

中图分类号:

TM 911

谢红伟, 时玮, 陈诗荣, 施洪生, 李华伟, 焦学文. 退役电池特征指标提取和处理方法综述[J]. 储能科学与技术, 2025, 14(9): 3581-3595.

Hongwei XIE, Wei SHI, Shirong CHEN, Hongsheng SHI, Huawei LI, Xuewen JIAO. A Review of feature extraction and processing methods for retired batteries[J]. Energy Storage Science and Technology, 2025, 14(9): 3581-3595.

图/表 11

图1

表1

图2

表2

图3

图4

表3

微分曲线方法示意"

方法	计算公式	曲线		峰值所表征的电池状态
I C A	$d Q d V = I × d t d V = I × d t d V$	原曲线	微分曲线
I C A	$d Q d V = I × d t d V = I × d t d V$			电池老化的容量变化

D V A	$d V d Q = d V I × d t = 1 I × d V d t$			电池老化的电压变化

表3

表4

图5

表5

图6

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实施国家及主体	时间	应用领域	成果与规模
中国中国铁塔	2015年	通信基站	使用梯次利用的动力电池在30个不同省份建成约50万个基站，使用退役电池储能容量约3 GWh
意大利Enel X	2020年	工业储能	Enel X公司与日产合作，在梅利利亚开展梯次利用项目，最大储能容量达1.7 MWh
德国博世集团、宝马	2021年	工业储能	博世集团、宝马和瓦滕福公司利用退役电池，建造了2 MW/2 MWh的大型光伏电站储能系统
国网江苏电力	2022年	电网储能	利用退役电池建设南京江北储能电站，这是国内首个梯次利用的电网侧储能电站，总容量达7.5万 kWh，其中退役磷酸铁锂电池约为4万 kWh
国网鄂州市鄂城区供电公司	2024年	直流微电网	将光伏发电、退役电池和充电桩组成直流微电网系统，其中电池储能量可达384 kWh

维度	具体要求	技术指标	法规/标准依据	核心技术要求
精度维度要求	核心参数检测精度控制	容量衰减率误差±1.5%内阻离散度≤0.8 mΩ	GB/T 42231—2022	0.5级精度电化学工作站全温域热电校准系统
	全周期数据追溯精度	特征参数采样频≥10 Hz 数据完整度≥99.8%	欧盟电池法规(2023)	128位高速AD转换器分布式时间戳同步机制
	空间分辨能力	热特性参数分辨率≤5 mm³ 膨胀力梯度检测精度0.1 N/mm²	IEC 63338：2024	激光干涉测量系统多物理场耦合误差补偿算法

效率维度要求	单体检测时效性	特征提取时间≤120 s/cell 重组合格率≥95%	梯次利用管理办法	多线程并行计算架构机器视觉引导定位技术
	批量处理能力	产线吞吐量≥2000 只/h 数据处理延迟≤50 ms	UL 1974	六轴机器人分拣系统5G-MEC边缘计算平台
	算法计算效率	寿命预测时效提升40% 误判率压缩至≤0.5%	T/CESA 1063—2022	LightGBM-MOGA混合算法张量压缩感知技术

曲线类型	参考文献	特征指标	特点
EIS曲线	文献[18]	阻抗	只提取EIS低频部分
	文献[19]		利用外部设备实现离线快速测量
	文献[20]		只提取阻抗谱中低频部分
	文献[21]		不同阻抗类型分别提取
	文献[22]		同时测量多块电池
	文献[23]		不同SOC下曲线重构至同一SOC测量

充放电曲线	文献[25]	任意片段间隔点连线与到该垂线的距离参数	提取更加直观的几何特征
	文献[26]	电压-容量衰减方差特征	只需通过任意片段的20个点提取特征
	文献[28]	电压	可通过曲线平移提取未测试电池的指标
	文献[31] 文献[32]	电压	部分曲线特征指标预测全体
	文献[33] 文献[34]	电压	将电压指标进行可视化处理

脉冲电压曲线	文献[37] 文献[38]	电压	利用短时脉冲进行特征快速提取
脉冲电压曲线	文献[39]	电压曲线拐点	少量指标结合机器学习进行预测

微分曲线	文献[41] 文献[45]	电压区间	提取出特征指标的最优片段
	文献[43] 文献[44]	容量、内阻	能够高倍率充放电，提高效率
	文献[46] 文献[47]	电压、内阻	微分曲线结合其他测试方法

方法思想	提取指标	优点	缺陷
多维静态指标结合	电压、电流、容量等静态参数	直观、方便、效率高	受环境干扰大、对设备精度要求高
循环曲线偏移测试	梯度、方差等数理统计指标	揭示特性变化情况	测试量大、难以解释本质
曲线分块与切片	各子集电压、电流等局部参数	针对性强且具体	选取标主观、忽视潜在细节
曲线转移与转化	热、光、声等其他维度指标	实现物理特性转化	建模复杂、难以揭示电学特性
静、动态指标结合	SOH、SOC、SOP等动态参数	利于全面评估状态、精度高	依赖模型和算法，数据处理难度大