A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries

Liu, Donglei; Wang, Shunli; Fan, Yongcun; Xia, Lili; Qiu, Jingsong

doi:10.1002/cta.3386

Cited by 22 publications

(5 citation statements)

References 40 publications

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“…Iteration principle of the EKF algorithm.-Due to the multitime scale effect of lithium-ion batteries, the EKF algorithm is used in this paper to identify the parameters of the slowly changing links in the HC-EEC model. When using the EKF algorithm to estimate the model parameters, 45 the state space equation for constructing the HC-EEC model is shown in Eq. 26.…”

Section: P K mentioning

confidence: 99%

On-Line Multi-Time Scale Adaptive Parameter Identification Based on Improved Lithium-Ion Batteries Hysteresis Characteristic-Electrical Equivalent Circuit Modeling

Wang

Cao

et al. 2023

J. Electrochem. Soc.

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Accurate identification of model parameters is a key aspect of lithium battery state estimation. To accurately identify battery model parameters, this paper establishes hysteresis characteristic-electrical equivalent circuit (HC-EEC) modeling by analyzing the influence of the hysteresis effect on battery state of charge. For the high-precision identification of battery model parameters, an online multi-time scale adaptive parameter identification strategy (OM-TSAPIS) is proposed. According to the different dynamic response links in the HC-EEC model, the strategy performs parameter identification through different time scale links, and uses the adaptive step size as the starting identification condition for the multi-time scale links, thereby improving the parameter identification accuracy of the HC-EEC model. The absolute average error of OM-TSAPIS was 0.0437 and 0.298 mV under the urban dynamometer driving schedule and Beijing bus dynamic street test conditions, respectively. Simulation results show that the identification accuracy of the proposed algorithm is high.

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Section: P K mentioning

confidence: 99%

On-Line Multi-Time Scale Adaptive Parameter Identification Based on Improved Lithium-Ion Batteries Hysteresis Characteristic-Electrical Equivalent Circuit Modeling

Wang

Cao

et al. 2023

J. Electrochem. Soc.

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“…Based on Equations ( 9), (11), and ( 12), the prediction and NCM for the jth particle are expressed as…”

Section: Adaptive Foupfmentioning

confidence: 99%

“…Many academics have employed various methodologies to estimate the SOC of LIBs in recent years. [7][8][9] The ampere-hour integration, 10,11 the opencircuit voltage, 12,13 and the internal resistance techniques 14 are the SOC estimation techniques that are not based on the battery model. These techniques typically have drawbacks, such as the poor noise immunity and the low precision.…”

Section: Introductionmentioning

confidence: 99%

An improved fractional‐order state estimation algorithm based on an unscented particle filter for state of charge estimation of lithium‐ion batteries with adaptive estimations of unknown parameters

Jiao,

Gao,

Chai

et al. 2023

Circuit Theory & Apps

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SummaryAn adaptive fractional‐order unscented particle filter (FOUPF) and an adaptive FOUPF with the estimations of the noise covariance matrices (NCMs) are proposed to estimate the state of charge (SOC) of lithium‐ion batteries (LIBs) in this study. The Grünwald–Letnikov difference is employed to establish the corresponding discrete‐time equation of the fractional‐order system (FOS). The order and SOC of FOS of LIBs are controlled within an appropriate interval by a mapping function. In order to achieve a joint estimation of order and unknown parameters for a FOS, an augmented vector method is applied in this study. Compared with the adaptive fractional‐order unscented Kalman filter and adaptive fractional‐order cubature Kalman filter, the proposed adaptive FOUPF has a higher accuracy for estimating SOC. Besides, an iterative approach that accommodates the NCMs is proposed to improve the estimation accuracy of SOC. Finally, the availability of the proposed algorithms is tested by several experiments.

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“…Compared with the literature 15,39 that only uses LSTM neural network, this algorithm has higher accuracy and better filtering effect. Meanwhile, compared with the correlation algorithm based on the battery equivalent circuit model in the literature, 50,51 it has less prediction time, more accessible model construction, and broader applicability. The main contributions of this paper are as follows.…”

Section: Introductionmentioning

confidence: 99%

An improved long short‐term memory based on global optimization square root extended Kalman smoothing algorithm for collaborative state of charge and state of energy estimation of lithium‐ion batteries

Wang

Cao

et al. 2023

Circuit Theory & Apps

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SummaryState of charge and state of energy are essential performance indicators of the battery management system and the key to reflecting the remaining capacity of batteries. Aiming at the problems of low precision, long time, and strongly nonlinear system estimation of state of charge and state of energy of lithium‐ion batteries based on traditional algorithm under complex working conditions, this paper proposes a hybrid method consisting of the long short‐term memory neural network and square root extended Kalman smoothing. The long short‐term memory neural network can enhance the memory ability of the previous time data. The sliding window technology is introduced into the network to improve the correlation between the last time and the subsequent time estimation. Based on the traditional Kalman filtering algorithm, the square root and reverse smoothing algorithms are introduced to solve the risk of the negative covariance matrix and the problems of slow convergence and significant estimation deviation caused by a strongly nonlinear system. According to experiments, under the hybrid pulse power characterization working condition at 25°C, the maximum absolute errors of state of charge and state of energy are 1.779% and 1.487%, and the mean absolute errors are 0.352% and 0.894%, respectively. Under the Beijing bus dynamic stress test working condition at 25°C, the maximum absolute errors of state of charge and state of energy are 2.703% and 2.369%, and the mean absolute errors are 0.462% and 0.621%, respectively. The experimental results show that this algorithm can obtain reliable state of charge and state of energy under different complex working conditions with high accuracy, convergence, and robustness.

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A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries

Cited by 22 publications

References 40 publications

On-Line Multi-Time Scale Adaptive Parameter Identification Based on Improved Lithium-Ion Batteries Hysteresis Characteristic-Electrical Equivalent Circuit Modeling

On-Line Multi-Time Scale Adaptive Parameter Identification Based on Improved Lithium-Ion Batteries Hysteresis Characteristic-Electrical Equivalent Circuit Modeling

An improved fractional‐order state estimation algorithm based on an unscented particle filter for state of charge estimation of lithium‐ion batteries with adaptive estimations of unknown parameters

An improved long short‐term memory based on global optimization square root extended Kalman smoothing algorithm for collaborative state of charge and state of energy estimation of lithium‐ion batteries

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