基于MIAEKF的多温度下锂电池SOC估计

doi:10.19799/j.cnki.2095-4239.2023.0533

摘要/Abstract

摘要：

随着锂电池在电动车辆中的广泛应用，准确估计电池荷电状态（SOC）对于电池的安全性和使用性能至关重要。然而，变化温度环境条件下，传统Kalman估计方法降低电池SOC估计准确性。为解决上述问题，本研究提出了基于多新息自适应扩展卡尔曼滤波（MIAEKF）的SOC估计方法。首先，基于多组不同温度下测试实验获取的电池数据，利用带遗忘因子的最小二乘法（FFRLS）进行参数辨识，获得了多温度下不同SOC阶段的电池参数。其次，利用函数拟合的方法建立了以SOC、温度为自变量，电池参数为因变量的函数模型，用于描述电池参数的动态行为。最后，在此基础上引入了MIAEKF算法，该算法结合了自适应和多新息的思想，通过滑动窗口自适应调整过程噪声与测量噪声协方差，并将窗口的新息均值作为窗口最新时刻后验估计的新息来增加误差新息。选择合适的窗口长度与新息均值系数能够有效提升SOC估计精度。实验数据验证的结果表明，基于MIAEKF的SOC估计方法在相同条件下相较于传统的扩展卡尔曼滤波（EKF）、自适应扩展卡尔曼滤波（AEKF）表现出更高的估计精度和稳定性。在多温度下，通过引入电池参数函数模型，MIAEKF能够自适应多个温度下的SOC估计，并且估计误差均在 $± 1 %$ 以内。

关键词: MIAEKF, 锂电池, SOC, 多温度, 函数拟合

Abstract:

With the widespread application of lithium-ion batteries in electric vehicles, accurate estimation of the state of charge (SOC) of batteries is crucial for ensuring battery safety and optimal performance. However, traditional Kalman estimation methods face challenges maintaining accuracy under variable temperature conditions. To address this issue, this study proposes a SOC estimation method based on multi-innovation adaptive extended Kalman filtering (MIAEKF). This study initially performs parameter identification using battery data from experiments conducted at various temperatures. The forgetting factor recursive least squares method is employed for this purpose, providing battery parameters at different SOC stages under multiple temperature conditions. Second, a function fitting approach is used to establish a model with SOC and temperature as independent variables and battery parameters as dependent variables, describing the dynamic behavior of battery parameters. Finally, the MIAEKF algorithm is introduced, incorporating adaptivity and multi-innovation concepts from the Kalman filtering algorithm. The sliding window is introduced to replace the traditional adaptive factor for adjusting the process noise and measurement noise covariance adaptively. The mean of multi-innovations within the window is used to augment the error innovation for the posterior estimate. By selecting an appropriate window length and innovation mean coefficient, the accuracy of SOC estimation is effectively improved. Verification using experimental data shows that the SOC estimation method based on MIAEKF outperforms traditional extended Kalman filtering and adaptive extended Kalman filtering in terms of estimation accuracy and stability under the same conditions. Under multiple temperature conditions, MIAEKF can adaptively estimate SOC at different temperatures, with estimation errors within ±1%. In summary, this study effectively addresses the complexity of SOC estimation for lithium-ion batteries under variable temperature conditions by proposing the SOC estimation method based on MIAEKF.

Key words: MIAEKF, lithium batteries, SOC, multiple temperatures, function fitting

中图分类号:

TM 912

袁照凯, 范秋华, 王冬青, 孙天民. 基于MIAEKF的多温度下锂电池SOC估计[J]. 储能科学与技术, 2024, 13(2): 680-690.

Zhaokai YUAN, Qiuhua FAN, Dongqing WANG, Tianmin SUN. State of charge estimation for lithium-ion batteries under multiple temperatures based on the MIAEK algorithm[J]. Energy Storage Science and Technology, 2024, 13(2): 680-690.

图/表 13

图1

图2

表1

SOC=100%时HPPC的参数辨识结果"

温度/℃	$R 0$ /Ω	$R 1$ /Ω	$C 1$ /F	$R 2$ /Ω	$C 2$ /F
-10	0.0209	0.00055	19848.738	0.0005	200000
0	0.0104	0.00049	20218.943	0.0005	201614
10	0.0067	0.00050	19871.568	0.0005	200136
25	0.0064	0.00050	20001.977	0.00049	199951
40	0.0017	0.00052	19575.562	0.00052	199164

表1

图3

图4

图5

表2

不同窗口长度下AEKF估计精度"

a	15	25	50	100	200
$R M S E$ /%	0.0601	0.0583	0.0609	0.0650	0.0993
$M a x s o c e r r o r$ /%	0.1049	0.1018	0.1059	0.1127	0.1672

表2

图6

表3

a=25条件下不同λ时MIAEKF SOC估计最大误差"

$λ$	0.5	1	2	3	4	5
$R M S E$ /%	0.0560	0.0562	0.0448	0.0465	0.0470	0.0485
$M a x s o c e r r o r$ /%	0.0980	0.0984	0.0838	0.0902	0.0878	0.0897

表3

图7

表4

不同SOC估计算法数据对比"

算法	$M a x s o c e r r o r$ /%	$R M S E$ /%
EKF	3.8833	2.1205
AEKF	0.1018	0.0583
MIAEKF	0.0838	0.0448

表4

图8

图9

参考文献 33

1	张贵萍, 闫筱炎, 王兵, 等. 长寿命循环的磷酸铁锂电池及材料、工艺[J]. 储能科学与技术, 2023, 12(7): 2134-2140.
	ZHANG G P, YAN X Y, WANG B, et al. Long life lithium iron phosphate battery and its materials and process[J]. Energy Storage Science and Technology, 2023, 12(7): 2134-2140.
2	易永利, 于冉, 李武, 等. Mo, Al掺杂的Li₇La₃Zr₂O₁₂基复合固态电解质的制备及全固态电池性能研究[J]. 储能科学与技术, 2023, 12(5): 1490-1499.
	YI Y L, YU R, LI W, et al. Preparation of Mo, Al-doped Li₇La₃Zr₂O₁₂-based composite solid electrolyte and performance of all-solid-state batterys[J]. Energy Storage Science and Technology, 2023, 12(5): 1490-1499.
3	黄渭彬, 张彪, 范金成, 等. ZIF-8复合PEO基固态电解质的制备与改性研究[J]. 储能科学与技术, 2023, 12(4): 1083-1092.
	HUANG W B, ZHANG B, FAN J C, et al. Preparation and modification of ZIF-8 composite PEO based solid electrolyte[J]. Energy Storage Science and Technology, 2023, 12(4): 1083-1092.
4	LI J L, FLEETWOOD J, HAWLEY W B, et al. From materials to cell: State-of-the-art and prospective technologies for lithium-ion battery electrode processing[J]. Chemical Reviews, 2022, 122(1): 903-956.
5	黎冲, 王成辉, 王高, 等. 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.
	LI C, WANG C H, WANG G, et al. 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.
6	PENG S, XU L, ZHANG W, et al. Overview of state of power prediction methods for lithium-ion batteries[J]. Journal of Mechanical Engineering, 2022, 58(20): 361.
7	吴盛军, 袁晓冬, 徐青山, 等. 锂电池健康状态评估综述[J]. 电源技术, 2017, 41(12): 1788-1791.
	WU S J, YUAN X D, XU Q S, et al. Review on lithium-ion battery health state assessment[J]. Chinese Journal of Power Sources, 2017, 41(12): 1788-1791.
8	李放, 闵永军, 张涌. 基于大数据的动力锂电池可靠性关键技术研究综述[J]. 储能科学与技术, 2023, 12(6): 1981-1994.
	LI F, MIN Y J, ZHANG Y. Review of key technology research on the reliability of power lithium batteries based on big data[J]. Energy Storage Science and Technology, 2023, 12(6): 1981-1994.
9	谭必蓉, 杜建华, 叶祥虎, 等. 基于模型的锂离子电池SOC估计方法综述[J]. 储能科学与技术, 2023, 12(6): 1995-2010.
	TAN B R, DU J H, YE X H, et al. Overview of SOC estimation methods for lithium-ion batteries based on model[J]. Energy Storage Science and Technology, 2023, 12(6): 1995-2010.
10	孙冬, 陈息坤. 基于离散滑模观测器的锂电池荷电状态估计[J]. 中国电机工程学报, 2015, 35(1): 185-191.
	SUN D, CHEN X K. Charge state estimation of Li-ion batteries based on discrete-time sliding mode observers[J]. Proceedings of the CSEE, 2015, 35(1): 185-191.
11	程泽, 孙幸勉, 程思璐. 一种锂离子电池荷电状态估计与功率预测方法[J]. 电工技术学报, 2017, 32(15): 180-189.
	CHENG Z, SUN X M, CHENG S L. Method for estimation of state of charge and power prediction of lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2017, 32(15): 180-189.
12	武强, 钟勇, 黄志荣, 等. 变温度下EKF和UKF的锂电池SOC估算对比[J]. 福建工程学院学报, 2022, 20(6): 580-586.
	WU Q, ZHONG Y, HUANG Z R, et al. Comparison of SOC estimation of lithium battery by EKF and UKF at variable temperatures[J]. Journal of Fujian University of Technology, 2022, 20(6): 580-586.
13	田茂飞, 安治国, 陈星, 等. 基于在线参数辨识和AEKF的锂电池SOC估计[J]. 储能科学与技术, 2019, 8(4): 745-750.
	TIAN M F, AN Z G, CHEN X, et al. SOC estimation of lithium battery based online parameter identification and AEKF[J]. Energy Storage Science and Technology, 2019, 8(4): 745-750.
14	XING L K, LING L Y, WU X Y. Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction[J]. Connection Science, 2022, 34(1): 2332-2363.
15	卢路. 基于多尺度DEKF的串联锂电池组SOC估计研究[D]. 淮南: 安徽理工大学, 2022.
	LU L. Study on the estimation of charge state of series lithium-ion battery packs based on multi-time scale DEKF[D]. Huainan: Anhui University of Science & Technology, 2022.
16	LI W Q, YANG Y, WANG D Q, et al. The multi-innovation extended Kalman filter algorithm for battery SOC estimation[J]. Ionics, 2020, 26(12): 6145-6156.
17	GU T Y, SHENG J, FAN Q H, et al. The modified multi-innovation adaptive EKF algorithm for identifying battery SOC[J]. Ionics, 2022, 28(8): 3877-3891.
18	雷克兵, 陈自强. 基于改进多新息扩展卡尔曼滤波的电池SOC估计[J]. 浙江大学学报(工学版), 2021, 55(10): 1978-1985, 2001.
	LEI K B, CHEN Z Q. Estimation of state of charge of battery based on improved multi-innovation extended Kalman filter[J]. Journal of Zhejiang University (Engineering Science), 2021, 55(10): 1978-1985, 2001.
19	孙洁, 刘梦, 刘晓悦, 等. MIAEKF算法对锂电池荷电状态估算的研究[J]. 现代电子技术, 2022, 45(16): 115-120.
	SUN J, LIU M, LIU X Y, et al. Research on MIAEKF algorithm for estimating lithium battery SOC[J]. Modern Electronics Technique, 2022, 45(16): 115-120.
20	YANG F F, LI W H, LI C, et al. State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network[J]. Energy, 2019, 175: 66-75.
21	CHEN J L, LU C L, CHEN C, et al. An improved gated recurrent unit neural network for state-of-charge estimation of lithium-ion battery[J]. Applied Sciences, 2022, 12(5): 2305.
22	王一全, 黄碧雄, 严晓, 等. 基于LSTM-DaNN的动力电池SOC估算方法[J]. 储能科学与技术, 2020, 9(6): 1969-1975.
	WANG Y Q, HUANG B X, YAN X, et al. SOC estimation method of power battery based on LSTM-DaNN[J]. Energy Storage Science and Technology, 2020, 9(6): 1969-1975.
23	SHU X, CHEN Z, SHEN J W, et al. State of charge estimation for lithium-ion battery based on hybrid compensation modeling and adaptive H-infinity filter[J]. IEEE Transactions on Transportation Electrification, 2023, 9(1): 945-957.
24	SUN D M, YU X L, WANG C M, et al. State of charge estimation for lithium-ion battery based on an intelligent adaptive extended Kalman filter with improved noise estimator[J]. Energy, 2021, 214: 119025.
25	XU Y H, ZHANG H G, YANG F B, et al. State of charge estimation of supercapacitors based on multi-innovation unscented Kalman filter under a wide temperature range[J]. International Journal of Energy Research, 2022, 46(12): 16716-16735.
26	WANG D Q, YANG Y, GU T Y. A hierarchical adaptive extended Kalman filter algorithm for lithium-ion battery state of charge estimation[J]. Journal of Energy Storage, 2023, 62: 106831.
27	ZENG Y, LI Y, YANG T. State of charge estimation for lithium-ion battery based on unscented Kalman filter and long short-term memory neural network[J]. Batteries, 2023, 9(7): 358.
28	HOU E G, WANG Z, ZHANG X P, et al. Combined state of charge and state of energy estimation for echelon-use lithium-ion battery based on adaptive extended Kalman filter[J]. Batteries, 2023, 9(7): 362.
29	YUAN H Y, LIU J G, ZHOU Y, et al. State of charge estimation of lithium battery based on integrated Kalman filter framework and machine learning algorithm[J]. Energies, 2023, 16(5): 2155.
30	REN X Q, LIU S L, YU X D, et al. A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM[J]. Energy, 2021, 234: 121236.
31	贾明, 李立祥, 李书国, 等. 动力锂离子电池电极结构对其极化特性影响的仿真研究[J]. 中国有色金属学报, 2020, 30(3): 620-628.
	JIA M, LI L X, LI S G, et al. Simulation research of effect of electrode structure on polarization characteristics of power lithium ion battery[J]. The Chinese Journal of Nonferrous Metals, 2020, 30(3): 620-628.
32	KOLLMEYER P, SKELLS M. Turnigy graphene 5000mAh 65C Li-ion battery data[R]. Mendeley Data, 2020
33	AKHLAGHI S, ZHOU N, HUANG Z Y. Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation[C]//2017 IEEE Power & Energy Society General Meeting. Chicago, IL, USA. IEEE, 2017: 1-5.

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