储能电站锂离子电池本体安全关键技术及新技术应用情况

doi:10.11930/j.issn.1004-9649.202405062

摘要/Abstract

摘要：

“双碳”目标的提出和能源电力低碳转型的持续推进，以新能源为主体的新型电力系统面临着规模化安全高效储能等能源问题的重要挑战。在这一背景下，储能电站作为能源系统中关键的组成部分，其安全管理尤为重要，直接关系到整个电力系统的稳定运行和可持续发展。针对锂离子电池本体安全管理的研究现状展开深入分析，首先，系统回顾了当前广泛应用的各类电池健康评估方法，并详细总结了数据驱动方法中健康因子的选择；其次，从基于数据碎片评估电池状态、电池边缘平台构建与储能电站智慧巡检3个方面出发，探讨了现有电池状态评估技术的最新研究热点，指出储能安全评估未来的发展方向和关键挑战；最后，总结储能电站的安全控制技术，针对计及电池参数变化的系统稳定性与储能系统多目标控制问题提出了相关见解。

关键词: 锂离子电池, 健康因子, 状态评估, 边缘计算, 智慧巡检, 多目标控制

Abstract:

The introduction of the "dual carbon" targets and the ongoing advancement of low-carbon transitions in energy and electricity have posed significant challenges to the new-type power system, which primarily relies on renewable energy sources, particularly in terms of large-scale, safe, and efficient energy storage. In this context, energy storage stations, as a crucial component of the energy system, are of utmost importance in terms of safety management, directly influencing the stable operation and sustainable development of the entire power system. This essay delves into the current research status of lithium-ion battery safety management. Firstly, it systematically reviews the various battery health assessment methods widely used today and comprehensively summarizes the selection of health indicators in data-driven approaches. Secondly, it discusses the latest research hotspots in existing battery state assessment technologies from three perspectives: battery state evaluation based on data fragmentation, the construction of battery edge platforms, and intelligent inspection of energy storage stations. The essay also points out the future direction and key challenges of energy storage safety assessment. Lastly, it presents insights into the safety control technologies for energy storage stations, addressing the system stability considering battery parameter variations and the multi-objective control of energy storage systems.

Key words: lithium-ion battery, state of health, state estimation, edge computing, intelligent inspection, multi-objective control

夏向阳, 谭欣欣, 单周平, 李辉, 徐志强, 吴晋波, 岳家辉, 陈贵全. 储能电站锂离子电池本体安全关键技术及新技术应用情况[J]. 中国电力, 2024, 57(11): 1-17.

Xiangyang XIA, Xinxin TAN, Zhouping SHAN, Hui LI, Zhiqiang XU, Jinbo WU, Jiahui YUE, Guiquan CHEN. Key Technology and Development Prospect of Ontology Safety for Lithium-Ion Battery Storage Power Stations[J]. Electric Power, 2024, 57(11): 1-17.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202405062

https://www.electricpower.com.cn/CN/Y2024/V57/I11/1

图/表 26

图 1 电化学阻抗法检测结果

Fig.1 The results of electrochemical impedance method

表 1 电池模型描述

Table 1 Battery Model Description

模型	等效模型	描述方程
Rint		$ \begin{gathered} {U_{{\text{oc}}}} = {U_{\text{L}}} + {I_{\text{L}}}{R_{\text{o}}} + {U_{\text{p}}} \\ \frac{{{\text{d}}{U_{\text{p}}}}}{{{\text{d}}t}} = \frac{{{I_L}}}{{{C_{\text{p}}}}} + \frac{{{U_{\text{p}}}}}{{{R_{\text{p}}}{C_{\text{p}}}}} \\ \end{gathered} $
PNGV		$ \begin{gathered} {U_{\text{L}}} = {U_{{\text{oc}}}} - {I_{\text{L}}}{R_{\text{o}}} - {U_{\text{p}}} - {U_{\text{d}}} \\ \frac{{{\text{d}}{U_{\text{p}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{p}}}}} + \frac{{{U_{\text{p}}}}}{{{R_{\text{p}}}{C_{\text{p}}}}} \\ \frac{{{\text{d}}{U_{\text{d}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{d}}}}} \\ \end{gathered} $
二阶RC		$ \begin{aligned}& {U_{\text{L}}} = {U_{{\text{oc}}}} - {U_{\text{p}}} - {U_{\text{c}}} - {I_L}R \\& \frac{{{\text{d}}{U_{\text{p}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{p}}}}} + \frac{{{U_{\text{p}}}}}{{{R_{\text{p}}}{C_{\text{p}}}}} \\& \frac{{{\text{d}}{U_{\text{c}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{c}}}}} + \frac{{{U_{\text{c}}}}}{{{R_{\text{c}}}{C_{\text{c}}}}} \\ \end{aligned} $
GNL		$ \begin{aligned}& {U_{\text{L}}} = {U_{{\text{oc}}}} - {I_{\text{L}}}{R_{\text{o}}} - {U_{\text{p}}} - {U_{\text{c}}} - {U_{\text{d}}} \\& \frac{{{\text{d}}{U_{\text{d}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{d}}}}} + \frac{{{U_{{\text{oc}}}}}}{{{R_{\text{d}}}{C_{\text{d}}}}} - \frac{{{U_{\text{d}}}}}{{{R_{\text{d}}}{C_{\text{d}}}}} -\\&\quad\quad\quad \frac{{{U_{\text{p}}}}}{{{R_{\text{d}}}{C_{\text{d}}}}} - \frac{{{U_{\text{c}}}}}{{{R_{\text{d}}}{C_{\text{d}}}}} \\ & \frac{{{\text{d}}{U_{\text{p}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{p}}}}} + \frac{{{U_{{\text{oc}}}}}}{{{R_{\text{d}}}{C_{\text{p}}}}} - \frac{{{U_{\text{d}}}}}{{{R_{\text{d}}}{C_{\text{p}}}}} -\\ &\quad\quad\quad \frac{{{U_{\text{p}}}}}{{{R_{\text{d}}}{C_{\text{p}}}}} - \frac{{{U_{\text{c}}}}}{{{R_{\text{d}}}{C_{\text{p}}}}} - \frac{{{U_{\text{p}}}}}{{{R_{\text{p}}}{C_{\text{p}}}}} \\& \frac{{{\text{d}}{U_{\text{c}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{c}}}}} + \frac{{{U_{{\text{oc}}}}}}{{{R_{\text{d}}}{C_{\text{c}}}}} - \frac{{{U_{\text{d}}}}}{{{R_{\text{d}}}{C_{\text{c}}}}} -\\& \quad\quad\quad \frac{{{U_{\text{p}}}}}{{{R_{\text{d}}}{C_{\text{c}}}}} - \frac{{{U_{\text{c}}}}}{{{R_{\text{d}}}{C_{\text{c}}}}} - \frac{{{U_{\text{c}}}}}{{{R_{\text{c}}}{C_{\text{c}}}}} \end{aligned} $

表 1 电池模型描述

Table 1 Battery Model Description

模型	等效模型	描述方程
Rint		$ \begin{gathered} {U_{{\text{oc}}}} = {U_{\text{L}}} + {I_{\text{L}}}{R_{\text{o}}} + {U_{\text{p}}} \\ \frac{{{\text{d}}{U_{\text{p}}}}}{{{\text{d}}t}} = \frac{{{I_L}}}{{{C_{\text{p}}}}} + \frac{{{U_{\text{p}}}}}{{{R_{\text{p}}}{C_{\text{p}}}}} \\ \end{gathered} $
PNGV		$ \begin{gathered} {U_{\text{L}}} = {U_{{\text{oc}}}} - {I_{\text{L}}}{R_{\text{o}}} - {U_{\text{p}}} - {U_{\text{d}}} \\ \frac{{{\text{d}}{U_{\text{p}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{p}}}}} + \frac{{{U_{\text{p}}}}}{{{R_{\text{p}}}{C_{\text{p}}}}} \\ \frac{{{\text{d}}{U_{\text{d}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{d}}}}} \\ \end{gathered} $
二阶RC		$ \begin{aligned}& {U_{\text{L}}} = {U_{{\text{oc}}}} - {U_{\text{p}}} - {U_{\text{c}}} - {I_L}R \\& \frac{{{\text{d}}{U_{\text{p}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{p}}}}} + \frac{{{U_{\text{p}}}}}{{{R_{\text{p}}}{C_{\text{p}}}}} \\& \frac{{{\text{d}}{U_{\text{c}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{c}}}}} + \frac{{{U_{\text{c}}}}}{{{R_{\text{c}}}{C_{\text{c}}}}} \\ \end{aligned} $
GNL		$ \begin{aligned}& {U_{\text{L}}} = {U_{{\text{oc}}}} - {I_{\text{L}}}{R_{\text{o}}} - {U_{\text{p}}} - {U_{\text{c}}} - {U_{\text{d}}} \\& \frac{{{\text{d}}{U_{\text{d}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{d}}}}} + \frac{{{U_{{\text{oc}}}}}}{{{R_{\text{d}}}{C_{\text{d}}}}} - \frac{{{U_{\text{d}}}}}{{{R_{\text{d}}}{C_{\text{d}}}}} -\\&\quad\quad\quad \frac{{{U_{\text{p}}}}}{{{R_{\text{d}}}{C_{\text{d}}}}} - \frac{{{U_{\text{c}}}}}{{{R_{\text{d}}}{C_{\text{d}}}}} \\ & \frac{{{\text{d}}{U_{\text{p}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{p}}}}} + \frac{{{U_{{\text{oc}}}}}}{{{R_{\text{d}}}{C_{\text{p}}}}} - \frac{{{U_{\text{d}}}}}{{{R_{\text{d}}}{C_{\text{p}}}}} -\\ &\quad\quad\quad \frac{{{U_{\text{p}}}}}{{{R_{\text{d}}}{C_{\text{p}}}}} - \frac{{{U_{\text{c}}}}}{{{R_{\text{d}}}{C_{\text{p}}}}} - \frac{{{U_{\text{p}}}}}{{{R_{\text{p}}}{C_{\text{p}}}}} \\& \frac{{{\text{d}}{U_{\text{c}}}}}{{{\text{d}}t}} = \frac{{{I_{\text{L}}}}}{{{C_{\text{c}}}}} + \frac{{{U_{{\text{oc}}}}}}{{{R_{\text{d}}}{C_{\text{c}}}}} - \frac{{{U_{\text{d}}}}}{{{R_{\text{d}}}{C_{\text{c}}}}} -\\& \quad\quad\quad \frac{{{U_{\text{p}}}}}{{{R_{\text{d}}}{C_{\text{c}}}}} - \frac{{{U_{\text{c}}}}}{{{R_{\text{d}}}{C_{\text{c}}}}} - \frac{{{U_{\text{c}}}}}{{{R_{\text{c}}}{C_{\text{c}}}}} \end{aligned} $

图 2 数据驱动法架构

Fig.2 The structure of he data-driven methods

图 3 电压急剧下降变化趋势

Fig.3 The change trend of sharp voltage drop

图 4 电压缓慢下降的变化趋势

Fig.4 The change trend of slow voltage drop

图 5 电压变化率示意

Fig.5 Schematic diagram of voltage change rate

图 6 电流变化率示意

Fig.6 Schematic diagram of current change rate

图 7 恒流充放电时间的变化趋势

Fig.7 The change trend of constant-current charge/discharge time

图 8 达到最高温度所需时间的变化趋势

Fig.8 The change trend of time required to reach the maximum temperature

图 9 最高气温的变化趋势

Fig.9 The change trend of maximum temperature

图 10 温度变化率示意

Fig.10 Schematic diagram of temperature change rate

图 11 电池一致性的综合评估方法

Fig.11 Comprehensive evaluation method for consistency

图 12 基于电压幅值的经验拟合模型

Fig.12 Empirical fitting model based on voltage

图 13 基于机器学习模型的电池剩余容量示意

Fig.13 Diagram of the proposed prediction remaining life based on machine learning model

图 14 B0005电池35-165循环圈数下的瞬时压降与剩余容量采集结果

Fig.14 The sharp voltage drop and remaining capacity collection results of battery numbered B0005 under 35 to 165 cycles

表 2 EFM方法容量估计结果与误差

Table 2 Capacity estimation results and errors of EFM

循环圈数	估计值/ (A·h)	实际值/ (A·h)	MAE/ (A·h)	RMSE/ (A·h)	MAPE/%
160	1.30668	1.30336	0.0045	0.0054	0.35
161	1.30467	1.30341
162	1.30532	1.29789
163	1.29713	1.29807
164	1.29884	1.29346
165	1.29682	1.28800

表 3 MLM法容量预测结果与误差

Table 3 Capacity prediction results and errors of MLM

循环圈数		预测值/(A·h)		实际值/(A·h)		MAE/(A·h)		MAPE/%
165		1.29213		1.28800		0.0041		0.32

图 15 边缘计算平台结构

Fig.15 The structure of edging computing platform

图 16 电池边缘计算系统界面

Fig.16 Edge Computing System Interface

图 17 电池电压放电阶段

Fig.17 Intelligent inspection robot schematic

图 18 欧姆内阻的在线鉴定结果

Fig.18 Online characterization results for ohmic internal resistance

图 19 智能巡检机器人

Fig.19 Intelligent inspection robot schematic

图 20 智能巡检机器人的热成像结果

Fig.20 Thermal imaging results for intelligent inspection robots

图 21 基于电池阻抗变化的根轨迹

Fig.21 Root locus diagram based on changes in battery impedance

图 22 基于阻抗和能量变化速率的最优台数范围区间

Fig.22 Range of optimal number of stations based on impedance and energy change rate

图 23 储能参与调频和调峰能量波动

Fig.23 The map of energy storage participation in FM and peaking energy fluctuations

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