An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

Wu, Aihua; Zhou, Yan; Mao, Jingfeng; Zhang, Xudong; Zheng, Junqiang

doi:10.3390/en16166013

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…Various approaches for SOC estimation have been proposed, including the traditional method, circuit model method, data-driven method, and the artificial intelligence method, among others. Coulomb counting (CC) [14] and open circuit voltage (OCV) [15] are the most common traditional methods. The CC method involves integrating the current over time to estimate the SOC [14].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, this method yields accurate results only when the battery remains idle for extended periods. During charging or discharging operations, the varying current makes it challenging to measure or calculate the OCV value accurately [15].…”

Section: Introductionmentioning

confidence: 99%

“…The circuit model-based method is another candidate to estimate SOC. Typically, this method is combined with Kalman filter [15][16][17] and unscented Kalman filter [18] to determine the model parameters. The accuracy of this method relies heavily on the precision of the established equivalent circuit model.…”

Section: Introductionmentioning

confidence: 99%

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State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm

Cai,

Li,

Yang

et al. 2023

Energies

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The accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the AdaBoost–BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy and poor generalization ability. The proposed model employs a back propagation neural network (BPNN) for the preliminary estimation. Subsequently, an AdaBoost–BPNN model is developed as a strong learner using the AdaBoost integration algorithm. Each BPNN sub-model serves as a weak learner within the AdaBoost framework. The final output of the strong learner is obtained by combining the individual outputs from the weak learners using weighting factors. This adaptive adjustment of weighting factors enhances the accuracy of SOC estimation. The proposed SOC estimation algorithm is evaluated and validated through experimental analysis. Throughout the paper, theoretical analysis is conducted, and the proposed AdaBoost–BPNN model is validated and verified using experimental results. The results demonstrate that the AdaBoost–BPNN model outperforms traditional methods in accurately estimating SOC under various conditions, including constant current-constant voltage (CCCV) charging, dynamical stress testing (DST), US06, a federal urban driving schedule (FUDS), and pulse discharge conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm

Cai,

Li,

Yang

et al. 2023

Energies

View full text Add to dashboard Cite

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A Method for Estimating the State of Charge of Lithium-Ion Batteries Considering Temperature

Gao,

Zhang,

Lyu

2024

J. Electrochem. Soc.

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Ensuring the safe operation of electric vehicles relies heavily on accurately estimating the State of Charge (SOC) of the battery. However, traditional SOC estimation methods often struggle to maintain performance at low temperatures and complex operating conditions. This study proposes a novel temperature-inclusive SOC estimation approach. In this approach, a simplified Multilayer Perceptron (MLP) battery model replaces the Equivalent Circuit Model (ECM), with temperature as the key input variable. Moreover, to enhance the robustness of the model, the training dataset is augmented by introducing noise. Combining the refined battery model with the Unscented Kalman Filter (UKF), the MLP-UKF SOC estimation framework is devised. Performance evaluation, generalizability analysis, and temperature adaptability testing are conducted using independent datasets. The results illustrate that the proposed model rapidly converges with the reference curves at different temperatures and operational conditions, and the average error is less than 3%. Furthermore, compared with the ECM model employing Recursive Least Squares parameter identification (MAE: 0.03228, RMSE: 0.03457), the mean absolute error (MAE) and root mean square error (RMSE) of MLP-UKF method are 0.01273 and 0.01430, respectively, showing better performance. The results of the analysis confirm the applicability of the MLP-UKF SOC estimation method for electric vehicle applications, particularly in challenging conditions such as low temperature and complex operations.

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An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

Cited by 2 publications

References 33 publications

State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm

State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm

A Method for Estimating the State of Charge of Lithium-Ion Batteries Considering Temperature

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