A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter

Liu, Shulin; Dong, Xia; Yu, Xiaodong; Ren, Xiaoqing; Zhang, Jinfeng; Zhu, Rui

doi:10.1016/j.egyr.2022.09.093

Cited by 21 publications

(13 citation statements)

References 16 publications

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“…Table 2 presents the parameters used for modeling the resonant structure on Altium designer and Simulink. For the load we adopt an equivalent RC model of the lithium battery [44][45][46] where Figure 17 shows the equivalent electrical diagram of the model. Table 3 presents the estimated values of the model parameters, all depending on the current and the charging voltage, where the estimated battery capacity is according to Equation (22).…”

Section: Impedance Analysis Of Class-e Invertermentioning

confidence: 99%

Photovoltaic Class‐E Inverter for Resonance Wireless Power Transfer for Electric Vehicle Charging Applications with Optimization Algorithms

et al. 2023

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A wireless power transfer (WPT) station supplied by an array of solar panels is presented, where solar energy comes from an array of panels with 120 V voltage and 3 A current. It is subjected to maximum power point tracking algorithm optimization on a boost converter, where the duty cycle of the converter is adjusted with an interval chosen between [0.2, 0.8], which is used to iteratively compare between the input and output signals in order to have a maximum power at the output. After regulating the panel array signal through the boost converter, the signal is transferred to an additional middle stage whose role is to raise the frequency until it reaches the resonance frequency of the WPT charger. The device responsible for this task is an inverter based on a class‐E inverter architecture and a GaN transistor technology. The inverter is controlled by a DSP card in a hard‐switch mode and delivers the output signal with a frequency of 10 Mhz, which is the resonance frequency of spiral coils of wireless chargers. Herein, the signal nature can be seen at the terminal of a fixed load with signal optimization through the MPPT algorithm and the HF inverter control mode.

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Section: Impedance Analysis Of Class-e Invertermentioning

confidence: 99%

Photovoltaic Class‐E Inverter for Resonance Wireless Power Transfer for Electric Vehicle Charging Applications with Optimization Algorithms

et al. 2023

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“…Nevertheless, the algorithm is overly reliant on the training set, the reliability of the imported data dramatically impacts the reliability of the final trained model, and the training data cannot contain all the situations of the car in the actual work, which will also lead to a substantial decrease in the reliability of the SOC estimated by the model and ultimately lead to the failure of the identification. Fourth, the power cell SOC estimation is implemented using the circuit equivalent model [10][11][12] approach, which is based on the principle of constructing equivalent circuits to replace the actual operating conditions of the cell, using the relevant algorithms to solve the model, and then using the filtering algorithm to obtain an exact prediction of the SOC. The modelling approach is not only highly accurate and robust in estimation, but the parameters in the model have a clear physical meaning and a more solid theoretical basis.…”

Section: Introductionmentioning

confidence: 99%

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

Xing,

Ren,

Luo

et al. 2024

Energy Science & Engineering

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Aiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness.

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“…The estimation of State-Of-Health (SOH) is a core issue in battery management [4][5][6][7]. Accurate estimation of battery SOH can provide essential information for the normal operation, efficient performance, and lifespan prediction of batteries [8][9][10]. Generally, when the SOH of electric vehicle batteries falls below 80%, the batteries may struggle to cope with frequent power demands and become unsuitable for continued use in electric vehicles [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…The method's estimation results are evaluated using three performance metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). RMSE formula is shown in(9), MAE and MAPE formula are shown in(10) and(11) respectively.…”

mentioning

confidence: 99%

Battery State of Health estimation using GA-BP neural network on data feature mining

Geng,

Zhang,

Chen

et al. 2023

IEICE Electron. Express

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The health status estimation of lithium-ion battery is a challenging through measurement. To establish a connection between battery health status and data features, a battery State of Health (SOH) estimation method based on data feature mining is proposed. Four features are extracted from the battery charging curve, and the grey correlation analysis is used to determine the high correlation between features and health status. The method combines a Backpropagation (BP) neural network with Genetic Algorithm (GA) for feature training and learning, enabling the estimation of battery SOH. The feasibility of the proposed method is validated using the NASA battery dataset. The results show that the battery SOH estimation method proposed in this paper outperforms the traditional BP neural network method achieving accurate estimation.

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A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter

Cited by 21 publications

References 16 publications

Photovoltaic Class‐E Inverter for Resonance Wireless Power Transfer for Electric Vehicle Charging Applications with Optimization Algorithms

Photovoltaic Class‐E Inverter for Resonance Wireless Power Transfer for Electric Vehicle Charging Applications with Optimization Algorithms

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

Battery State of Health estimation using GA-BP neural network on data feature mining

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