The lithium battery SOC estimation on square root unscented Kalman filter

LIU, Qinghe; Yu, Quanqing

doi:10.1016/j.egyr.2022.05.079

Cited by 27 publications

(9 citation statements)

References 6 publications

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“…However, the accuracy of modeling has become a major challenge, especially for nonlinear systems where traditional Kalman filters may have large errors. In order to solve the nonlinear problem, estimation methods based on extended kalman filtering (EKF) algorithm [6] and unscented kalman filtering (UKF) algorithm [7] have been applied.…”

Section: Introductionmentioning

confidence: 99%

Elman neural network and equivalent circuit model based multi-measurement Kalman filters for SOC estimation

Shen

Ding

Hao

2023

Preprint

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This paper provides an insight into the estimation of state of charge (SOC) of lithium-ion batteries, and multi-measurement Kalman filters are constructed to achieve better estimation accuracy. The filter based estimation scheme consists of two sub Kalman filters, one based on the Thevenin equivalent circuit model and the other based on the Elman neural network model. They share the same state equation but have different measurements, namely from the terminal voltage and the Elman neural network, respectively. The optimal weighted error covariance matrix in Kalman filter is computed to obtain the final estimated SOC with the results from two sub filters. The training set of the experiment is from five mixed charging and discharging cycles and the test set is the LA92 driving cycle. The experimental results show that multi-measurement Kalman filtering has higher accuracy in SOC estimation, both the mean absolute error and the root mean square error are less than 0.3\%.

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Section: Introductionmentioning

confidence: 99%

Elman neural network and equivalent circuit model based multi-measurement Kalman filters for SOC estimation

Shen

Ding

Hao

2023

Preprint

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“…The SOC estimation methods proposed in the previous literatures are classified into three categories: the direct measurement [5][6][7] methods, the estimation methods [8][9][10][11] with a data-driven predictive model, and the estimation methods 7,[12][13][14][15][16] based on the battery models. Based on the directly measurable battery variables, the direct measurement methods mainly include the coulomb counting method, 7 open circuit voltage (OCV) method, 5 impedance spectroscopy method, 6 and internal resistance method.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the battery model, the SOC estimation methods mainly include extended Kalman filter 12,17 (EKF), unscented Kalman filter 14,18 (UKF), particle filter 15 (PF), and cubature Kalman filter 15 (CKF). Based on the electrochemical model and the equivalent circuit model (ECM), Khaki and Das 7 proposed a model for SOC estimation of VRFB.…”

Section: Introductionmentioning

confidence: 99%

State of charge estimation for the vanadium redox flow battery based on the Sage–Husa adaptive extended Kalman filter

Zixuan

Fan

et al. 2023

Circuit Theory & Apps

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SummaryFocusing on improving the accuracy of vanadium redox flow battery (VRFB) state of charge (SOC) estimation, this paper will combine the extended Kalman filter (EKF) estimator with the Sage–Husa adaptive method, referred to as the SAEKF estimator. Firstly, a second‐order Equivalent Circuit Model (ECM) is established to provide the correct parameterization and degrees of freedom. Next, under a hybrid pulse power characterization (HPPC) text, the parameter estimator in MATLAB/Simulink is utilized to identify the ECM parameters by using nonlinear least squares method with Trust‐Region‐Reflective (TRR) iterative algorithm. Then, during the HPPC test, the estimators implemented on the ARM‐Cortex STM32F103 estimate the VRFB SOC under both the 0°C and 5°C conditions. Finally, the mean absolute error (MAE) of the IEKF estimator are 4.66% at the 0°C and 0.895% at the 5°C. In the meantime, the MAE of the SAEKF estimator are 4.15% at the 0°C and 0.703% at the 5°C. The evaluation factors of the 0°C and 5°C experiments in the SAEKF estimator are smaller than those of the IEKF and EKF estimators, indicating that the SAEKF estimator outperforms the IEKF estimator in VRFB SOC estimation.

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“…Furthermore, the BMS manages the energy flow between the PV system, battery, grid, and load (Okay et al, 2021). Liu and Yu (2022) use the square root unscented Kalman filter (SR-UKF) method for SOC estimation on lithium cobalt oxide batteries. They also construct a MATLAB/Simulink model for evaluating experimental results with less than 25 mV error.…”

Section: Introductionmentioning

confidence: 99%

“…They also construct a MATLAB/Simulink model for evaluating experimental results with less than 25 mV error. The experimental results show that more successful results were obtained with the SR_UKF model than with the traditional Kalman filter model (Liu and Yu, 2022). Jin et al (2021) develop a second-order RC equivalent circuit model for lithium-ion batteries which considers the influence of discharge rate.…”

Section: Introductionmentioning

confidence: 99%

Kurşun Asit Bataryalar için SOC Tahmini Yaklaşımını İçeren Bir Batarya Yönetim Sistemi Tasarımı

Çinar

2022

Journal of Materials and Mechatronics: A

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Storage is one of the most important issues of the last decades. In particular, storage systems are needed in order to benefit more effectively from renewable energy systems where production cannot be controlled. One of the most important problems in storage is that as the amount of energy desired to be stored increases, the need for space also increases. Therefore, it is of great importance to manage energy effectively in such systems. In this study, a battery management system (BMS) that can be used for lead acid batteries has been designed. This BMS has a measurement and control system based on STM 32 microcontroller and is controlled via an interface prepared in the MATLAB Simulink environment and the test data is imported into the MATLAB Workspace environment. The designed system can also perform battery charge-discharge experiments in accordance with the battery characteristics. Charge-discharge experiments were carried out using the designed system, and a model was developed to determine the state of charge (SOC) of the battery using the data collected during these experiments. With the model developed based on Elman Neural Networks, the SOC of battery could be estimated at an error level of less than 1%.

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The lithium battery SOC estimation on square root unscented Kalman filter

Cited by 27 publications

References 6 publications

Elman neural network and equivalent circuit model based multi-measurement Kalman filters for SOC estimation

Elman neural network and equivalent circuit model based multi-measurement Kalman filters for SOC estimation

State of charge estimation for the vanadium redox flow battery based on the Sage–Husa adaptive extended Kalman filter

Kurşun Asit Bataryalar için SOC Tahmini Yaklaşımını İçeren Bir Batarya Yönetim Sistemi Tasarımı

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