A Hierarchical State of Charge Estimation Method for Lithium-ion Batteries via XGBoost and Kalman Filter

Song, Shiyu; Zhang, Xiaoyong; Gao, Dianzhu; Fu, Jiang; Wu, Yue; Huang, Jiahao; Ya-dong, Gong; Liu, Bowen; Huang, Zhiwu

doi:10.1109/smc42975.2020.9283051

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…In SOC estimation, as a nonlinear system, its performance is affected by many factors [23][24][25][26]. The three impact factors of current, voltage, and temperature are used in this paper.…”

Section: Experimental Datamentioning

confidence: 99%

State of Charge Centralized Estimation of Road Condition Information Based on Fuzzy Sunday Algorithm

Lin

Wang

et al. 2022

Energies

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Accurate estimation of the state of charge (SOC) is critical for battery management systems. A backpropagation neural network (BPNN) based on a modified fuzzy Sunday algorithm is proposed to improve the accuracy of SOC predictions of lithium-ion batteries (LIBs). The road condition information relating to the data is obtained using the fuzzy Sunday algorithm, and the acquired feature information is used to estimate SOC using BPNN based on the Levenberg–Marquardt (L–M) training process. The change from exact character matching to fuzzy number matching is an improvement to the Sunday algorithm. The quantification of the road condition is innovatively integrated into the neural network. At present, this kind of feature is new to the estimation process, and our experiment proved that the effect is good. To quickly estimate the SOC under different driving conditions, the same network was used to predict the data of different road conditions. In addition, a strategy is proposed for SOC estimation under unknown road conditions, which improves the estimation accuracy. Studies have shown that the model used in the experiment is more accurate than other machine learning models. This model assures prediction accuracy, reliability, and timeliness.

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Section: Experimental Datamentioning

confidence: 99%

State of Charge Centralized Estimation of Road Condition Information Based on Fuzzy Sunday Algorithm

Lin

Wang

et al. 2022

Energies

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Machine Learning Applied to Lithium‐Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development

Xiao,

Shi,

et al. 2024

Energy Storage

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Lithium‐ion batteries (LIBs) are extensively utilized in electric vehicles due to their high energy density and cost‐effectiveness. LIBs exhibit dynamic and nonlinear characteristics, which raise significant safety concerns for electric vehicles. Accurate and real‐time battery state estimation can enhance safety performance and prolong battery lifespan. With the rapid advancement of big data, machine learning (ML) holds substantial promise for state estimation. This paper systematically reviews several common ML algorithms, detailing the basic principles of each and illustrating their structures with flowcharts. We compare the advantages and disadvantages of various methods. Subsequently, we discuss feature extraction techniques employed in recent studies for estimating state of charge (SOC), state of health (SOH), state of power (SOP), and remaining useful life (RUL), as well as the application of these ML methods in state estimation. Finally, we discuss the challenges associated with using ML methods for state estimation and outline future development trends.

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Electric vehicle battery pack state of charge estimation using parallel artificial neural networks

Manoharan

Sooriamoorthy

Begam

et al. 2023

Journal of Energy Storage

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A Hierarchical State of Charge Estimation Method for Lithium-ion Batteries via XGBoost and Kalman Filter

Cited by 3 publications

References 15 publications

State of Charge Centralized Estimation of Road Condition Information Based on Fuzzy Sunday Algorithm

State of Charge Centralized Estimation of Road Condition Information Based on Fuzzy Sunday Algorithm

Machine Learning Applied to Lithium‐Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development

Electric vehicle battery pack state of charge estimation using parallel artificial neural networks

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