SOC estimation of lithium-ion batteries based on DN-AUKF

doi:10.19799/j.cnki.2095-4239.2024.0546

Abstract

Abstract:

Lithium-ion batteries, known for their lack of memory effect, lightweight nature, and environmental friendliness, are widely used as energy sources in electric vehicles, electronic devices, and various scales of energy storage. In a lithium-ion battery management system, the state of charge (SOC) is a critical indicator, and its accurate estimation is essential for efficient energy management and optimal control of the battery system. This paper proposes an SOC estimation method based on the dynamic noise-adaptive unscented Kalman filter (DN-AUKF). The open circuit voltage (OCV) of the battery at different SOCs is first obtained through intermittent discharge experiments and fitted to derive the OCV-SOC curves. The lithium-ion battery is then modeled using a second-order RC equivalent circuit model, with parameter identification conducted via the hybrid pulse power characterization (HPPC) test. Recognizing that the SOC estimation in lithium-ion batteries is a nonlinear process and highly susceptible to operational noise, this study utilizes a traceless transform based on the Kalman filter (KF) to address system nonlinearity, integrates an adaptive factor for noise characteristic estimation, and dynamically adjusts the process noise covariance to enhance algorithm robustness and estimation accuracy. The proposed algorithm is validated under dynamic stress test (DST) and federal urban driving schedule (FUDS) conditions, demonstrating that the DN-AUKF algorithm significantly improves average estimation error, maximum error, and root mean square error compared to the adaptive unscented Kalman filter (AUKF) and unscented Kalman filter (UKF) algorithms under various conditions. The DN-AUKF's average absolute estimation error is less than 0.51%, indicating its superior performance in accurately estimating the SOC of lithium-ion batteries, even under extreme conditions such as low power and high-rate charge and discharge scenarios.

Key words: dynamic noise adaptive unscented Kalman filter, state of charge, second-order RC equivalent circuit model, unscented Kalman filter

CLC Number:

TM 911

Kangyong YIN, Lei SUN, Haomiao LI, Dongliang GUO, Peng XIAO, Kangli WANG, Kai JIANG. SOC estimation of lithium-ion batteries based on DN-AUKF[J]. Energy Storage Science and Technology, 2024, 13(11): 4065-4077.

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算法	平均绝对误差	最大绝对误差	均方根误差
UKF	1.52%	4.15%	1.74%
AUKF	0.48%	1.88%	0.56%
DN-AUKF	0.35%	0.96%	0.43%

算法	平均绝对误差	最大绝对误差	均方根误差
UKF	2.88%	5.99%	3.32%
AUKF	1.16%	1.84%	1.26%
DN-AUKF	0.59%	1.28%	0.71%

算法	平均绝对误差	最大绝对误差	均方根误差
UKF	2.91%	5.99%	3.33%
AUKF	1.11%	1.83%	1.23%
DN-AUKF	0.58%	1.15%	0.70%

算法	平均绝对误差	均方根误差	测试工况	参考文献
EKF	2.17%	1.86%	恒流放电	[28]
EKF	3.12%	3.21%	DST	[35]
UKF	1.12%	0.98%	UDDS	[36]
CKF	1.06%	0.51%	UDDS	[37]
AEKF	1.53%	1.70%	BJDST	[38]
AEKF	1.43%	2.07%	DST	[39]
AUKF	1.01%	1.81%	FUDS	[40]
DN-AUKF	0.59%	0.71%	FUDS	本工作

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