锂电池SOC估计的实现方法分析与性能对比

doi:10.19799/j.cnki.2095-4239.2022.0078

摘要/Abstract

摘要：

锂电池荷电状态(state of charge，SOC)估计技术是保证电力储能和电动汽车合理应用的核心技术，也是锂电池系统控制运营、监测维护的基础。在锂电池实际应用中，其表现出非线性、时变性、影响因素复杂性和不确定性的问题，造成了荷电状态估计难度大、精度不高和适应能力不足。为此，众多锂电池荷电状态估计算法及改进策略应运而生。与此同时，部分研究人员针对不同估计方法和改进策略的实现方式和优缺点开展了分析与对比，但相关综述对估计方法的技术特点和适用性方面的论述不足且缺乏系统性总结。本文首先分析了锂电池荷电状态估计的影响因素和测试标准；然后从基于实验计算的传统方法、基于电池模型的滤波类算法、基于数据驱动的机器学习技术以及数模混合估计方法四个方面开展对比分析，归纳总结各类方法的技术特点、实现过程、适用条件、难题痛点以及应用优势，系统全面地论述了现有锂电池荷电状态估计技术的研究重点和应用现状；最后，展望了锂电池荷电状态估计算法的未来研究方向。

关键词: 锂电池荷电状态估计, 实验计算方法, 滤波估计算法, 机器学习技术, 数模驱动

Abstract:

The lithium battery state of charge (SOC) estimation technology is the core technology to ensure the reasonable application of electric energy storage and electric vehicles, as well as the control, operation, monitoring, and maintenance of lithium battery systems. It demonstrates the problems of nonlinearity, time variability, complexity, and uncertainty of influencing factors in the practical application of lithium batteries, thereby resulting in the difficulty, low accuracy, and insufficient adaptability of the state of charge estimation. As a result, numerous lithium battery state of charge estimation algorithms and improvement strategies have emerged. At the same time, some researchers have analyzed and compared the implementation methods, advantages, and disadvantages of various estimation methods, and improvement strategies, but the relevant review lacks a systematic summary and insufficient discussion on the technical characteristics and applicability of estimation methods. To begin, this paper examines the influencing factors and test standards of lithium battery state of charge estimation. Next, the traditional methods based on experimental calculation, filtering algorithms based on battery model, data-driven machine learning technology, and digital-analog hybrid estimation methods are compared and analyzed, as well as technical characteristics, implementation process, applicable conditions, problems, pain points, and application advantages. The research focus and application status of the existing state of charge estimation technology for lithium batteries are systematically and comprehensively discussed. Finally, future research directions for lithium battery state of charge estimation algorithms are proposed.

Key words: SOC estimation of lithium battery, experimental calculation method, filter estimation algorithm, machine learning technology, data-model hybrid drive

中图分类号:

TM 92

黎冲, 王成辉, 王高, 鲁宗虎, 马成智. 锂电池SOC估计的实现方法分析与性能对比[J]. 储能科学与技术, 2022, 11(10): 3328-3344.

Chong LI, Chenhui WANG, Gao WANG, Zonghu LU, Chengzhi MA. Review on implementation method analysis and performance comparison of lithium battery state of charge estimation[J]. Energy Storage Science and Technology, 2022, 11(10): 3328-3344.

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参考文献 82

1	高铭琨, 徐海亮, 吴明铂. 基于等效电路模型的动力电池SOC估计方法综述[J]. 电气工程学报, 2021, 16(1): 90-102.
	GAO M K, XU H L, WU M B. Review of SOC estimation methods for power battery based on equivalent circuit model[J]. Journal of Electrical Engineering, 2021, 16(1): 90-102.
2	陈海生,俞振华,刘为. 储能产业研究白皮书2021[R]. 中国能源研究会储能专委会/中关村储能产业技术联盟,2021.
	CHEN H H, YU Z H, LIU W. White paper on energy storage industry research 2021[R]. China Energy Research Association Energy Storage Special Committee/Zhongguancun Energy Storage Industry Technology Alliance, 2021.
3	黄凯, 郭永芳, 李志刚. 动力锂离子电池荷电状态估计综述[J]. 电源技术, 2018, 42(9): 1398-1401.
	HUANG K, GUO Y F, LI Z G. Review of state of charge estimation methods for power lithium-ion battery[J]. Chinese Journal of Power Sources, 2018, 42(9): 1398-1401.
4	付诗意, 吕桃林, 闵凡奇, 等. 电动汽车用锂离子电池SOC估算方法综述[J]. 储能科学与技术, 2021, 10(3): 1127-1136.
	FU S Y, LYU T L, MIN F Q, et al. Review of estimation methods on SOC of lithium-ion batteries in electric vehicles[J]. Energy Storage Science and Technology, 2021, 10(3): 1127-1136.
5	苏航, 高怀斌, 李争光, 等. 基于BCRLS-ACKF的锂离子电池荷电状态估计[J]. 储能科学与技术, 2021, 10(6): 2334-2341.
	SU H, GAO H B, LI Z G, et al. State of charge estimation of Li-ion battery based on BCRLS-ACKF[J]. Energy Storage Science and Technology, 2021, 10(6): 2334-2341.
6	封居强, 伍龙, 黄凯峰, 等. 基于FFRLS和AEKF的锂离子电池SOC在线估计研究[J]. 储能科学与技术, 2021, 10(1): 242-249.
	FENG J Q, WU L, HUANG K F, et al. Online SOC estimation of a lithium-ion battery based on FFRLS and AEKF[J]. Energy Storage Science and Technology, 2021, 10(1): 242-249.
7	陈峥, 赵广达, 沈世全, 等. 基于迁移模型的老化锂离子电池SOC估计[J]. 储能科学与技术, 2021, 10(1): 326-334.
	CHEN Z, ZHAO G D, SHEN S Q, et al. SOC estimation of aging lithium-ion battery based on a migration model[J]. Energy Storage Science and Technology, 2021, 10(1): 326-334.
8	李骏, 魏炜阳, 刘霏霏, 等. 基于噪声模型的锂离子电池SOC预测[J]. 电池, 2020, 50(3): 249-253.
	LI J, WEI W Y, LIU F F, et al. SOC prediction for Li-ion battery based on noise model[J]. Battery Bimonthly, 2020, 50(3): 249-253.
9	刘长贺,胡明辉,李兰.基于温变双极化模型的锂离子电池荷电状态估计[J/OL].重庆大学学报.[2022-01-15]. http://qks.cqu.edu.cn/cqdxzrcn/article/abstract/zk-202106056
	LIU C H, HU M H, LI L. State of charge estimation of Li-ion battery based on temperature-variable dual polarization model[J/OL]. Journal of Chongqing University.[2022-01-15]. http://qks.cqu.edu.cn/cqdxzrcn/article/abstract/zk-202106056
10	潘凤文, 弓栋梁, 高莹, 等. 基于鲁棒H_∞滤波的锂离子电池SOC估计[J]. 工程科学学报, 2021, 43(5): 693-701.
	PAN F W, GONG D L, GAO Y, et al. Lithium-ion battery state of charge estimation based on a robust H_∞ filter[J]. Chinese Journal of Engineering, 2021, 43(5): 693-701.
11	刘晓悦, 魏宇册. 优化神经网络的锂电池SOC估算[J]. 机械设计与制造, 2021(11): 83-86.
	LIU X Y, WEI Y C. Optimization of neural network for lithium nattery SOC estimation[J]. Machinery Design ＆ Manufacture, 2021(11): 83-86.
12	陆佳伟, 佘世刚, 魏新尧, 等. 基于布谷鸟搜索优化神经网络的锂电池荷电状态预测[J]. 计算机测量与控制, 2021, 29(8): 47-50, 88.
	LU J W, SHE S G, WEI X Y, et al. Lithium battery charge status prediction based on cuckoo search optimization neural networks[J]. Computer Measurement ＆ Control, 2021, 29(8): 47-50, 88.
13	王语园, 李嘉波, 张福. 基于粒子群算法的最小二乘支持向量机电池状态估计[J]. 储能科学与技术, 2020, 9(4): 1153-1158.
	WANG Y Y, LI J B, ZHANG F. Battery state estimation of least squares support vector machinebased on particle swarm optimization[J]. Energy Storage Science and Technology, 2020, 9(4): 1153-1158.
14	鲍伟, 葛建军. 基于稀疏采样数据的电动公交车电池SOC预测方法研究[J]. 汽车工程, 2020, 42(3): 367-374.
	BAO W, GE J J. Study on battery SOC prediction method for ElectricBus based on sparsely sampled data[J]. Automotive Engineering, 2020, 42(3): 367-374.
15	田冬冬, 李立伟, 杨玉新. 基于改进BP-EKF算法的SOC估算[J]. 电源技术, 2020, 44(9): 1274-1278.
	TIAN D D, LI L W, YANG Y X. Research on SOC estimation based on improved BP-EKF algorithm[J]. Chinese Journal of Power Sources, 2020, 44(9): 1274-1278.
16	张远进, 吴华伟, 叶从进. 基于AUKF-BP神经网络的锂电池SOC估算[J]. 储能科学与技术, 2021, 10(1): 237-241.
	ZHANG Y J, WU H W, YE C J. Estimation of the SOC of a battery based on the AUKF-BP algorithm[J]. Energy Storage Science and Technology, 2021, 10(1): 237-241.
17	张照娓, 郭天滋, 高明裕, 等. 电动汽车锂离子电池荷电状态估算方法研究综述[J]. 电子与信息学报, 2021, 43(7): 1803-1815.
	ZHANG Z W, GUO T Z, GAO M Y, et al. Review of SOC estimation methods for electric vehicle Li-ion batteries[J]. Journal of Electronics ＆ Information Technology, 2021, 43(7): 1803-1815.
18	国家市场监督管理总局, 国家标准化管理委员会. 电力系统电化学储能系统通用技术条件: GB/T 36558—2018[S]. 北京: 中国标准出版社, 2018.
19	国家市场监督管理总局, 国家标准化管理委员会. 电动汽车用电池管理系统技术条件: GB/T 38661—2020[S]. 北京: 中国标准出版社, 2020.
20	国家质量监督检验检疫总局, 中国国家标准化管理委员会. 电化学储能电站用锂离子电池管理系统技术规范: GB/T 34131—2017[S]. 北京: 中国标准出版社, 2017.
21	韦振汉. 锂离子电池荷电及健康状态预测方法研究[D].广西师范大学,2018.
	WEI Z H. Research on methods for lithium ion battery SOC estimation and soh prediction[D]. Guilin: Guangxi Normal University, 2018.
22	续远.基于安时积分法与开路电压法估测电池SOC[J].新型工业化,2022,12(01):123-124+127.
	XU Y.Estimation of battery SOC based on ampere-hour integration method and open circuit voltage method[J].The Journal of New Industrialization,2022,12(01):123-124+127.
23	郑欣昊. 海洋浮标能量收集器电源管理系统的设计与实现[D]. 哈尔滨: 哈尔滨工业大学, 2019.
	ZHENG X H. Design and implementation of power management system for ocean buoy energy collector[D]. Harbin: Harbin Institute of Technology, 2019.
24	许洁茹, 凌仕刚, 王少飞, 等. 锂电池研究中的电导率测试分析方法[J]. 储能科学与技术, 2018, 7(5): 926-955.
	XU J R, LING S G, WANG S F, et al. Conductivity test and analysis methods for research of lithium batteries[J]. Energy Storage Science and Technology, 2018, 7(5): 926-955.
25	李建林, 屈树慷, 黄孟阳, 等. 锂离子电池建模现状研究综述[J]. 热力发电, 2021, 50(7): 1-7.
	LI J L, QU S K, HUANG M Y, et al. A review of current research on lithium-ion battery modeling[J]. Thermal Power Generation, 2021, 50(7): 1-7.
26	李涛, 程夕明, 胡晨华. 锂离子电池电化学降阶模型性能对比[J]. 物理学报, 2021, 70(13): 429-440.
	LI T, CHENG X M, HU C H. Comparative study of reduced-order electrochemical models of the lithium-ion battery[J]. Acta Physica Sinica, 2021, 70(13): 429-440.
27	李雄, 李英豪, 李晨阳, 等. 基于温度和SOC的退役电池电化学阻抗特性[J]. 电池, 2021, 51(2): 126-130.
	LI X, LI Y H, LI C Y, et al. Electrochemical impedance characteristics of retired battery based on temperature and SOC[J]. Battery Bimonthly, 2021, 51(2): 126-130.
28	李建林, 肖珩. 锂离子电池建模现状综述[J]. 储能科学与技术, 2022, 11(2): 697-703.
	LI J L, XIAO H. Review on modeling of lithium-ion battery[J]. Energy Storage Science and Technology, 2022, 11(2): 697-703.
29	杨杰, 王婷, 杜春雨, 等. 锂离子电池模型研究综述[J]. 储能科学与技术, 2019, 8(1): 58-64.
	YANG J, WANG T, DU C Y, et al. Overview of the modeling of lithium-ion batteries[J]. Energy Storage Science and Technology, 2019, 8(1): 58-64.
30	吴小慧, 张兴敢. 锂电池二阶RC等效电路模型参数辨识[J]. 南京大学学报(自然科学), 2020, 56(5): 754-761.
	WU X H, ZHANG X G. Parameters identification of second order RC equivalent circuit model for lithium batteries[J]. Journal of Nanjing University (Natural Science), 2020, 56(5): 754-761.
31	李建林, 梁忠豪, 李雅欣, 等. 锂电池储能系统建模发展现状及其数据驱动建模初步探讨[J]. 油气与新能源, 2021, 33(4): 75-81.
	LI J L, LIANG Z H, LI Y X, et al. Development status in modeling of the lithium battery energy storage system and preliminary exploration of its data-driven modeling[J]. Petroleum and New Energy, 2021, 33(4): 75-81.
32	张少凤, 张清勇, 杨叶森, 等. 基于滑动窗口和LSTM神经网络的锂离子电池建模方法[J]. 储能科学与技术, 2022, 11(1): 228-239.
	ZHANG S F, ZHANG Q Y, YANG Y S, et al. Lithium-ion battery model based on sliding window and long short term memory neural network[J]. Energy Storage Science and Technology, 2022, 11(1): 228-239.
33	吕力行, 刘骅, 徐雷, 等. 基于数据-模型混合驱动的锂电池储能系统状态估计及预警方法[J]. 热力发电, 2021, 50(8): 64-72.
	LYU L X, LIU H, XU L, et al. State estimation and early warning method for lithium battery energy storage system based on data-model hybrid drive[J]. Thermal Power Generation, 2021, 50(8): 64-72.
34	代明杰, 张磊, 江学焕. 基于二阶Thevenin模型的卡尔曼滤波SOC估计[J]. 湖北汽车工业学院学报, 2021, 35(4): 55-58.
	DAI M J, ZHANG L, JIANG X H. Kalman filter SOC estimation based on second order thevenin model[J]. Journal of Hubei University of Automotive Technology, 2021, 35(4): 55-58.
35	郝文美, 张立伟, 彭博, 等. 动车组钛酸锂电池荷电状态估计[J]. 电工技术学报, 2021, 36(S1): 362-371.
	HAO W M, ZHANG L W, PENG B, et al. State of charge estimation of lithium titanate battery for electric multiple units[J]. Transactions of China Electrotechnical Society, 2021, 36(S1): 362-371.
36	曹天佳,李欢欢,韩军,等. 基于扩展卡尔曼滤波的锂电池SOC估计算法[C]//2021中国汽车工程学会年会论文集(2),2021:187-193.
	CAO T J, LI H H, HAN J, et al. Lithium battery SOC estimation algorithm based on extended Kalman filter[C]//Proceedings of the 2021 China Society of Automotive Engineers Annual Conference (2), 2021:187-193.
37	田曜荣, 宋春宁, 莫伟县. 基于无迹卡尔曼滤波单液流锌镍电池SOC估计[J]. 计算机仿真, 2021, 38(11): 73-76, 81.
	TIAN Y R, SONG C N, MO W X. SOC estimation of single flow zinc nickel battery based on unscented Kalman filter[J]. Computer Simulation, 2021, 38(11): 73-76, 81.
38	杨淇, 孙桓五, 张凤博. 锂电SOC改进无迹卡尔曼滤波估算算法研究[J]. 机械设计与制造, 2021(10): 220-224.
	YANG Q, SUN H W, ZHANG F B. Research on SOC improved unscented Kalman filter estimation algorithm for lithium batteries[J]. Machinery Design ＆ Manufacture, 2021(10): 220-224.
39	LUO J Y, PENG J K, HE H W. Lithium-ion battery SOC estimation study based on Cubature Kalman filter[J]. Energy Procedia, 2019, 158: 3421-3426.
40	宋琴, 王顺利, 于春梅. 自适应卡尔曼对储能锂电池充放电状态的估算[J]. 自动化仪表, 2021, 42(5): 63-68.
	SONG Q, WANG S L, YU C M. Estimation of charge and discharge state of energy storage lithium battery by adaptive Kalman[J]. Process Automation Instrumentation, 2021, 42(5): 63-68.
41	黄鹏超, 鄂加强. 基于双自适应卡尔曼滤波的锂电池状态估算[J]. 储能科学与技术, 2022, 11(2): 660-666.
	HUANG P C, E J Q. State estimation of lithium-ion battery based on dual adaptive Kalman filter[J]. Energy Storage Science and Technology, 2022, 11(2): 660-666.
42	王志福, 李仁杰, 李霞. 基于混合AUKF和HIFF的锂离子电池SOC估计[J]. 电池, 2021, 51(4): 380-384.
	WANG Z F, LI R J, LI X. SOC estimation of Li-ion battery based on mixed AUKF and HIFF[J]. Battery Bimonthly, 2021, 51(4): 380-384.
43	杨朝红, 马彬, 黄明浩, 等. 基于OCV分段拟合的电池SOC估计方法研究[J]. 计算机仿真, 2021, 38(11): 82-88, 157.
	YANG Z H, MA B, HUANG M H, et al. Research on state of charge estimation method of lithium battery based on open-circuit voltage piecewise fitting[J]. Computer Simulation, 2021, 38(11): 82-88, 157.
44	李练兵, 孙坤, 季亮, 等. 基于双卡尔曼滤波的电池SOC估算[J]. 计算机工程与设计, 2021, 42(11): 3218-3224.
	LI L B, SUN K, JI L, et al. Battery SOC estimation based on double Kalman filter[J]. Computer Engineering and Design, 2021, 42(11): 3218-3224.
45	刘习奎. 基于双扩展卡尔曼滤波的锂电池荷电状态估算方法[J]. 电子制作, 2021(21): 93-95.
	LIU X K. Estimation method of lithium battery state of charge based on double extended Kalman filter[J]. Practical Electronics, 2021(21): 93-95.
46	陈剑, 肖振锋, 刘顺成, 等. 基于EKF-SVSF的锂离子电池SOC和SOH准确估计[J]. 电源技术, 2020, 44(10): 1483-1487.
	CHEN J, XIAO Z F, LIU S C, et al. Accurate estimation of SOC and SOH of Li-ion battery based on EKF-SVSF[J]. Chinese Journal of Power Sources, 2020, 44(10): 1483-1487.
47	朱磊, 刘子博, 李路路, 等. 基于RLS-DLUKF算法的锂电池SOC预测方法研究[J]. 储能科学与技术, 2021, 10(3): 1137-1144.
	ZHU L, LIU Z B, LI L L, et al. Research on a battery SOC prediction method based on the RLS-DLUKF algorithm[J]. Energy Storage Science and Technology, 2021, 10(3): 1137-1144.
48	朱奕楠, 吕桃林, 赵芝芸, 等. 基于并行卡尔曼滤波器的锂离子电池荷电状态估计[J]. 储能科学与技术, 2021, 10(6): 2352-2362.
	ZHU Y N, LÜ T L, ZHAO Z Y, et al. State of charge estimation of lithium ion battery based on parallel Kalman filter[J]. Energy Storage Science and Technology, 2021, 10(6): 2352-2362.
49	单成鑫, 李立伟, 杨玉新. 基于IACO-PF的锂电池SOC估算[J]. 储能科学与技术, 2021, 10(3): 1145-1152.
	SHAN C X, LI L W, YANG Y X. SOC of estimation of lithium battery based on IACO-PF[J]. Energy Storage Science and Technology, 2021, 10(3): 1145-1152.
50	刘淑杰, 郝昆昆, 王永, 等. 基于改进粒子滤波算法的动力锂离子电池荷电状态估计[J]. 大连理工大学学报, 2020, 60(4): 392-401.
	LIU S J, HAO K K, WANG Y, et al. State of charge estimation of power lithium-ion battery based on improved particle filter algorithms[J]. Journal of Dalian University of Technology, 2020, 60(4): 392-401.
51	彭方想, 南金瑞, 孙立清. 基于权值选择粒子滤波算法的锂离子电池SOC估计[J]. 太原理工大学学报, 2020, 51(5): 750-755.
	PENG F X, NAN J R, SUN L Q. SOC estimation of lithium-ion battery based on weight selection particle filter algorithm[J]. Journal of Taiyuan University of Technology, 2020, 51(5): 750-755.
52	袁建华, 刘雅萍, 赵子玮, 等. 基于IGWO-PF算法的无人机锂电池SOC估计[J]. 储能科学与技术, 2022, 11(5): 1601-1607.
	YUAN J H, LIU Y P, ZHAO Z W, et al. SOC estimation of UAV lithium battery based on IGWO-PF algorithm[J]. Energy Storage Science and Technology, 2022, 11(5): 1601-1607.
53	熊巍, 梅华平, 徐刚, 等. 基于改进H无穷滤波的锂离子电池SOC估计[J]. 电源技术, 2020, 44(10): 1488-1491, 1528.
	XIONG W, MEI H P, XU G, et al. Lithium-ion battery SOC estimation based on improved H-infinity filter[J]. Chinese Journal of Power Sources, 2020, 44(10): 1488-1491, 1528.
54	YU Q Q, XIONG R, YANG R X, et al. Online capacity estimation for lithium-ion batteries through joint estimation method[J]. Applied Energy, 2019, 255:doi: 10.1016/j.apenergy.2019.113817.
55	丁洁, 姚建鑫, 万佑红, 等. 基于加权多新息H_∞滤波的锂离子电池SOC估计[J]. 电池, 2020, 50(5): 432-435.
	DING J, YAO J X, WAN Y H, et al. SOC estimation for Li-ion battery based on weighted innovation H_∞ filter[J]. Battery Bimonthly, 2020, 50(5): 432-435.
56	于仲安, 卢健, 王先敏. 基于GA-BP神经网络的锂离子电池SOC估计[J]. 电源技术, 2020, 44(3): 337-340, 421.
	YU Z A, LU J, WANG X M. SOC estimation of Li-ion battery based on GA-BP neural network[J]. Chinese Journal of Power Sources, 2020, 44(3): 337-340, 421.
57	王桥, 魏孟, 叶敏, 等. 基于灰狼算法优化极限学习机的锂离子电池SOC估计[J]. 储能科学与技术, 2021, 10(2): 744-751.
	WANG Q, WEI M, YE M, et al. Estimation of lithium-ion battery SOC based on GWO-optimized extreme learning machine[J]. Energy Storage Science and Technology, 2021, 10(2): 744-751.
58	赵超, 王延峰, 林立. 基于改进灰狼算法优化核极限学习机的锂电池动力电池荷电状态估计[J]. 信息与控制, 2021, 50(6): 731-739.
	ZHAO C, WANG Y F, LIN L. State of charge estimation for lithium battery based on kernel extreme learning machine optimized by improved grey wolf algorithm[J]. Information and Control, 2021, 50(6): 731-739.
59	王帅, 马鸿雁, 窦嘉铭, 等. 基于UGOA-BP的锂电池SOC估算[J]. 储能科学与技术, 2022, 11(1): 258-264.
	WANG S, MA H Y, DOU J M, et al. Estimation of lithium-ion battery state of charge based on UGOA-BP[J]. Energy Storage Science and Technology, 2022, 11(1): 258-264.
60	张小辉, 许傲然, 王秀平. 回溯搜索算法改进RBF算法的锂离子电池SOC估算研究[J]. 电测与仪表, 2020, 57(18): 146-152.
	ZHANG X H, XU A R, WANG X P. Research on lithium-ion battery SOC estimation based on backtracking search algorithm and improved RBF algorithm[J]. Electrical Measurement ＆ Instrumentation, 2020, 57(18): 146-152.
61	陈德海, 丁博文, 潘韦驰. 基于LFOA-GRNN模型的矿用锂电池SOC预测[J]. 现代电子技术, 2020, 43(6): 115-118.
	CHEN D H, DING B W, PAN W C. Mining lithium battery SOC prediction based on LFOA-GRNN model[J]. Modern Electronics Technique, 2020, 43(6): 115-118.
62	钱建文, 杜翀, 田欣, 等. 一种改进T-S模糊神经网络估计锂电池SOC的方法[J]. 电源技术, 2020, 44(9): 1270-1273.
	QIAN J W, DU C, TIAN X, et al. An improved fuzzy neural network method based on T-S model to estimate state of charge of lithium batteries[J]. Chinese Journal of Power Sources, 2020, 44(9): 1270-1273.
63	曹新宇, 彭飞, 李立伟, 等. 基于IBAS-NARX神经网络的锂电池荷电状态估计[J]. 储能科学与技术, 2021, 10(6): 2342-2351.
	CAO X Y, PENG F, LI L W, et al. SOC estimation of lithium battery based on IBAS-NARX neural network model[J]. Energy Storage Science and Technology, 2021, 10(6): 2342-2351.
64	明彤彤, 赵晶, 王晓磊, 等. 基于改进LSTM的脉冲大倍率工况下锂电池SOC估计[J]. 电力系统保护与控制, 2021, 49(8): 144-150.
	MING T T, ZHAO J, WANG X L, et al. SOC estimation of a lithium battery under high pulse rate condition based on improved LSTM[J]. Power System Protection and Control, 2021, 49(8): 144-150.
65	朱元富, 贺文武, 李建兴, 等. 基于Bi-LSTM/Bi-GRU循环神经网络的锂电池SOC估计[J]. 储能科学与技术, 2021, 10(3): 1163-1176.
	ZHU Y F, HE W W, LI J X, et al. SOC estimation for Li-ion batteries based on Bi-LSTM and Bi-GRU[J]. Energy Storage Science and Technology, 2021, 10(3): 1163-1176.
66	李超然, 肖飞, 樊亚翔, 等. 基于门控循环单元神经网络和Huber-M估计鲁棒卡尔曼滤波融合方法的锂离子电池荷电状态估算方法[J]. 电工技术学报, 2020, 35(9): 2051-2062.
	LI C R, XIAO F, FAN Y X, et al. A hybrid approach to lithium-ion battery SOC estimation based on recurrent neural network with gated recurrent unit and Huber-M robust Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2051-2062.
67	王仲旭, 张圣渠, 刘强. 基于GA和PSO的电动客车锂离子电池SOC估计[J]. 电池, 2021, 51(3): 221-224.
	WANG Z X, ZHANG S Q, LIU Q. SOC estimation of Li-ion battery in electric bus based on GA and PSO[J]. Battery Bimonthly, 2021, 51(3): 221-224.
68	成文晶, 潘庭龙. 基于分布估计算法LSSVM的锂电池SOC预测[J]. 储能科学与技术, 2020, 9(6): 1948-1953.
	CHENG W J, PAN T L. Prediction for SOC of lithium-ion batteries by estimating the distribution algorithm with LSSVM[J]. Energy Storage Science and Technology, 2020, 9(6): 1948-1953.
69	舒星,刘永刚,申江卫,陈峥.基于改进最小二乘支持向量机与Box-Cox变换的锂离子电池容量预测[J].机械工程学报,2021,57(14):118-128.
	SHU X, LIU Y G, SHEN J W, et al.Capacity prediction of lithium-ion batteries based on improved least squares support vector machine and Box-Cox transform[J].Chinese Journal of Mechanical Engineering,2021,57(14):118-128.
70	李超然, 肖飞, 樊亚翔, 等. 基于高斯过程回归的锂电池SOC估算方法[J]. 海军工程大学学报, 2021, 33(1): 55-59.
	LI C R, XIAO F, FAN Y X, et al. State of charge estimation method for lithium battery based on Gaussian process regression[J]. Journal of Naval University of Engineering, 2021, 33(1): 55-59.
71	魏孟, 李嘉波, 叶敏, 等. 基于高斯混合回归的锂离子电池SOC估计[J]. 储能科学与技术, 2020, 9(3): 958-963.
	WEI M, LI J B, YE M, et al. SOC estimation of Li-ion battery based on Gaussian mixture regression[J]. Energy Storage Science and Technology, 2020, 9(3): 958-963.
72	孙爱芬, 赤娜. 基于改进的高斯过程回归的SOC估计算法[J]. 储能科学与技术, 2022, 11(1): 253-257.
	SUN A F, CHI N. SOC estimation algorithm based on improved Gaussian process regression[J]. Energy Storage Science and Technology, 2022, 11(1): 253-257.
73	马向平, 靳皓晴, 朱奇先, 等. 锂离子电池荷电状态的在线融合估计方法[J]. 兰州理工大学学报, 2020, 46(5): 78-84.
	MA X P, JIN H Q, ZHU Q X, et al. An online fusion estimation method for state of charge of lithium ion batteries[J]. Journal of Lanzhou University of Technology, 2020, 46(5): 78-84.
74	刘兴涛, 李坤, 武骥, 等. 基于EKF-SVM算法的动力电池SOC估计[J]. 汽车工程, 2020, 42(11): 1522-1528, 1544.
	LIU X T, LI K, WU J, et al. State of charge estimation for traction battery based on EKF-SVM algorithm[J]. Automotive Engineering, 2020, 42(11): 1522-1528, 1544.
75	丁羿茗, 吕瑞强, 蒋超. 基于EKF算法的神经网络估算锂电池SOC[J]. 电源技术, 2021, 45(10): 1260-1263.
	DING Y M, LV R Q, JIANG C. Estimation of lithium battery SOC based on EKF neural network[J]. Chinese Journal of Power Sources, 2021, 45(10): 1260-1263.
76	辛浩东, 赵欣, 周昊, 等. 基于LSTM-UKF的全电船用锂电池SOC容量估计[J]. 船舶工程, 2021, 43(11): 111-117.
	XIN H D, ZHAO X, ZHOU H, et al. SOC capacity estimation method of lithium-ion batteries in all-electric ships based on LSTM-UKF[J]. Ship Engineering, 2021, 43(11): 111-117.
77	寇发荣, 王思俊, 王甜甜, 等. VCM模型下的IBAS-EKF锂电池荷电状态估计[J]. 机械科学与技术, 2021, 40(12): 1929-1938.
	KOU F R, WANG S J, WANG T T, et al. IBAS-EKF estimation of lithium battery state of charge under VCM[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(12): 1929-1938.
78	李军, 张俊, 张世义. 基于ABP-EKF算法的锂电池SOC估计[J]. 重庆交通大学学报(自然科学版), 2021, 40(3): 135-140.
	LI J, ZHANG J, ZHANG S Y. Lithium battery SOC estimation based on ABP-EKF algorithm[J]. Journal of Chongqing Jiaotong University (Natural Science), 2021, 40(3): 135-140.
79	张吉昂, 王萍, 程泽. 基于充电电压片段和融合方法的锂离子电池SOC-SOH-RUL联合估计[J]. 电网技术, 2022, 46(3): 1063-1072.
	ZHANG J, WANG P, CHENG Z. A joint estimation framework of SOC-SOH-RUL for lithium batteries based on charging voltage segment and hybrid method[J]. Power System Technology, 2022, 46(3): 1063-1072.
80	李名莉, 邱兵涛, 贾琳鹏. 锂电池组剩余电量SOC估算方法的分析与研究[J]. 自动化仪表, 2019, 40(4): 56-59.
	LI M L, QIU B T, JIA L P. Analysis and research on SOC estimation method for residual power of lithium battery packs[J]. Process Automation Instrumentation, 2019, 40(4): 56-59.
81	贾海峰, 李聪. 基于BP神经网络的锂电池组SOC估算[J]. 农业装备与车辆工程, 2020, 58(1): 105-107, 112.
	JIA H F, LI C. SOC estimation of lithium battery pack based on BP neural network[J]. Agricultural Equipment ＆ Vehicle Engineering, 2020, 58(1): 105-107, 112.
82	何锋, 王文亮, 蒋雪生, 等. 双扩展卡尔曼滤波法估计锂电池组SOC与SOH[J]. 农业装备与车辆工程, 2021, 59(7): 37-40, 61.
	HE F, WANG W L, JIANG X S, et al. Estimation of SOC and SOH of lithium battery pack by double extended Kalman filter[J]. Agricultural Equipment ＆ Vehicle Engineering, 2021, 59(7): 37-40, 61.

常见因素	影响原因
温度	温度越高，电解液的活性越强，进而锂离子扩散能力增强，受到的阻力减弱，内部化学反应更加激烈，故锂电池的容量随之增大
充放电倍率	充放电流越大，锂电池内部的化学反应越剧烈，故在充电电压相同的前提下，电流越大，锂电池的容量越大
电池自放电率	电池自放电率显示在非工作状态保存电量的能力，该值越大，锂电池的真实容量越大
充放电效率	大电流放电会使得电池内部的锂离子不能完全化学反应，导致电池容量减小

标准编号	标准名称	SOC估计技术要求
GB/T 36558—2018^[18]	电力系统电化学储能系统通用技术条件	能量计算误差不应大于3%，计算更新周期不应大于3 s
GB/T 38661—2020^[19]	电动汽车用电池管理系统技术条件	对于不可外接充电的混合动力电动汽车，锂电池动力电池管理系统SOC估算的累积误差应不大于15%
GB/T 34131—2017^[20]	电化学储能电站用锂离子电池管理系统技术规范	电能量计算误差应不大于3%

混合形式	技术特点	技术优势
安时积分+ELM^[73]	安时积分法估计电池SOC， ELM修正预测误差	ELM算法预测估计误差弥补安时积分法误差较大的问题
EKF+ Elman^[77]	EKF估计电池SOC，Elman神经网络实时修正电池极化电压	使用Elman神经网络模型提高了锂电池等效模型精度，提升EKF的SOC估计精度
EKF+ BP^[78]	EKF估计电池SOC，BP神经网络计算EKF误差并进行修正	BP神经网络具有非线性建模能力，能够建立温度变量与SOC值差值，形成误差修正
UKF+ BP^[23]	UKF估计电池SOC，BP神经网络估计初始电池SOC	初始SOC更为准确，加快UKF的收敛速度
EKF+ ANN^[75]	EKF估计神经网络权值及阈值等参数，利用神经网络预测电池SOC	通过EKF获取更为准确的神经网络权值及阈值，提升神经网络SOC预测准确性
EKF+ LSTM^[76]	UKF以及LSTM共同估计SOC，利用LSTM估计结果校正UKF结果	结合UKF以及LSTM对SOC的估计效果，在多种情况下具有良好SOC估计效果
EKF+ SVM^[74]	EKF估计电池SOC，SVM模型根据电池状态补偿电池参数误差	使用SVM保证电池等效模型参数更为准确
EKF+ LSSVM^[79]	EKF估计电池SOC，LSSVM估计电池SOH，建立SOC与SOH联合估计模型	LSSVM适合短周期SOH估计，从电池健康状态的角度修正电池SOC估计结果

方法类型	估计方法	方法精度	方法复杂度	方法数据量	方法计算量	实时检测性
基于实验测试计算的估计方法	开路电压法^[22]	**	*	*	*	*
	放电法^[21]	**	***	*	*	*
	安时积分法^[4]	***	***	*	**	***
	电导法^[23]	***	**	**	**	*
	交流阻抗法^[24]	****	****	***	**	*
基于模型驱动的估计方法	卡尔曼及其改进滤波^[35-36]	***	***	***	***	****
	粒子滤波^[50-52]	****	***	***	***	****
	H无穷滤波^[53]	***	****	***	***	****
	基于递推最小二乘滤波^[54]	***	***	***	***	****
基于数据驱动的估计方法	神经网络类^[61,62]	****	****	*****	****	****
	支持向量类^[67-69]	***	***	****	***	****
	高斯过程回归^[70]	***	**	****	***	****
基于数模驱动的估计方法	卡尔曼+ 神经网络^[73]	*****	*****	*****	*****	****
基于数模驱动的估计方法	卡尔曼+ 支持向量机^[74]	*****	****	****	****	****

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