A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation

Zhang, Shuzhi; Chen, Zhang; Jiang, Shiyong; Zhang, Xiongwen

doi:10.1016/j.energy.2022.123423

Cited by 42 publications

(7 citation statements)

References 44 publications

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“…Among all types of fuel cells, the LIB is one of the most promising energy sources, because of its high energy capacity and Coulombic performance [ 3 , 4 , 5 , 6 , 7 ], with great potential in EV applications. LIBs are a complex nonlinear time-varying system with multiple real-time changing quantities [ 8 , 9 , 10 , 11 , 12 , 13 ], including state of charge, state of health, state of power, and state of energy. State estimations are all dependent on accurate battery models.…”

Section: Introductionmentioning

confidence: 99%

Improved Parameter Identification for Lithium-Ion Batteries Based on Complex-Order Beetle Swarm Optimization Algorithm

Zhang

et al. 2023

Micromachines

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With the aim of increasing the model accuracy of lithium-ion batteries (LIBs), this paper presents a complex-order beetle swarm optimization (CBSO) method, which employs complex-order (CO) operator concepts and mutation into the traditional beetle swarm optimization (BSO). Firstly, a fractional-order equivalent circuit model of LIBs is established based on electrochemical impedance spectroscopy (EIS). Secondly, the CBSO is used for model parameters’ identification, and the model accuracy is verified by simulation experiments. The root-mean-square error (RMSE) and maximum absolute error (MAE) optimization metrics show that the model accuracy with CBSO is superior when compared with the fractional-order BSO.

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Section: Introductionmentioning

confidence: 99%

Improved Parameter Identification for Lithium-Ion Batteries Based on Complex-Order Beetle Swarm Optimization Algorithm

Zhang

et al. 2023

Micromachines

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“…Studies have shown that the UKF has higher accuracy than EKF for SOC estimation with the Thevenin model [31]. Following that, adaptive EKF (AEKF) [21], [32] and adaptive UKF (AUKF) [33], [34] are introduced to handle unknown noise variance. Besides, dual EKF (DEKF) is proposed to estimate both SOC and model parameters simultaneously [35], [36].…”

Section: Introductionmentioning

confidence: 99%

A Self-Calibration SOC Estimation Method for Lithium-Ion Battery

2023

IEEE Access

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Accurate state of charge (SOC) estimation is essential for the battery management system (BMS). In engineering, inappropriate selection of equivalent circuit model (ECM) and model parameters is common for lithium-ion batteries. It can result in systematic errors (i.e., modeling errors) in the state-space equation, thus affecting the SOC estimation accuracy. Aiming at that, this paper proposes a self-calibration method to enhance SOC estimation. In the method, a novel state-space equation containing an unknown systematic error term is developed based on the Thevenin model. A self-calibration unscented Kalman filter (SC-UKF) algorithm is then introduced for recursive SOC estimation. The algorithm can automatically recognize and calibrate the unknown systematic error in the state equation, while also reducing the random noise effect through data fusion with the measurement equation. Test results demonstrate that the method can effectively correct the Thevenin modeling error and improve SOC estimation accuracy. Furthermore, the proposed method is computationally simple and convenient for engineering applications without increasing model complexity.INDEX TERMS lithium-ion battery, state of charge, equivalent circuit model, systematic error, selfcalibration, unscented Kalman filter.

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“…Filters cater for sensor reading restrictions and uncertainties to enhance system state assessment accuracy and reliability [5][6][7][8][9][10][11][12][13][14]. Filters minimize sensor data noise, enhancing estimated information and issue and diagnostic discoveries [15][16][17][18][19][20][21][22][23][24][25]. The Kalman filter (KF) estimates a system's state using poor or noisy data.…”

Section: Introductionmentioning

confidence: 99%

Sliding innovation filter for micorgrid application

Al-Shabi

Obaideen

El-Nadi

et al. 2023

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

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Currently, microgrids are frequently used and various control algorithms have been applied to improve their performance in both grid-connected and islanded modes. However, research has shown that incorporating a filtering technique into the controller can lead to even better performance. As a result, a simple controller with a filter can perform just as well as a complex controller that operates alone. This study focuses on the performance of a microgrid using a new filter called the sliding innovation filter, which is known for its robustness and stability. The filter is an excellent option for estimating performance under various conditions, such as load and injected powers. To demonstrate the filter's advantages, modeling uncertainties are introduced into the system while the filter is estimating states. The filter's performance is evaluated using a MATLAB© simulation environment.

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A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation

Cited by 42 publications

References 44 publications

Improved Parameter Identification for Lithium-Ion Batteries Based on Complex-Order Beetle Swarm Optimization Algorithm

Improved Parameter Identification for Lithium-Ion Batteries Based on Complex-Order Beetle Swarm Optimization Algorithm

A Self-Calibration SOC Estimation Method for Lithium-Ion Battery

Sliding innovation filter for micorgrid application

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