Data selection framework for battery state of health related parameter estimation under system uncertainties

Fogelquist, Jackson; Lin, Xinfan

doi:10.1016/j.etran.2023.100283

eTransportation

2023

DOI: 10.1016/j.etran.2023.100283

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Data selection framework for battery state of health related parameter estimation under system uncertainties

Jackson Fogelquist,

Xinfan Lin

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2024

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Cited by 2 publications

References 52 publications

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Joint estimation of State of Charge (SOC) and State of Health (SOH) for lithium ion batteries using Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Sort Term Memory Network (LSTM) models

Zheng,

Luo

2024

International Journal of Electrochemical Science

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Joint estimation of State of Charge (SOC) and State of Health (SOH) for lithium ion batteries using Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Sort Term Memory Network (LSTM) models

Zheng,

Luo

2024

International Journal of Electrochemical Science

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C-Rate- and Temperature-Dependent State-of-Charge Estimation Method for Li-Ion Batteries in Electric Vehicles

Aslan,

Yasa

2024

Energies

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Li-ion batteries determine the lifespan of an electric vehicle. High power and energy density and extensive service time are crucial parameters in EV batteries. In terms of safe and effective usage, a precise cell model and SoC estimation algorithm are indispensable. To provide an accurate SoC estimation, a current- and temperature-dependent SoC estimation algorithm is proposed in this paper. The proposed SoC estimation algorithm and equivalent circuit model (ECM) of the cells include current and temperature effects to reflect real battery behavior and provide an accurate SoC estimation. For including current and temperature effects in the cell model, lookup tables have been used for each parameter of the model. Based on the proposed ECM, the unscented Kalman filter (UKF) approach is utilized for estimating SoC since this approach is satisfactory for nonlinear systems such as lithium-ion batteries. The experimental results reveal that the proposed approach provides superior accuracy when compared to conventional methods and it is promising in terms of meeting electric vehicle requirements.

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Data selection framework for battery state of health related parameter estimation under system uncertainties

Cited by 2 publications

References 52 publications

Joint estimation of State of Charge (SOC) and State of Health (SOH) for lithium ion batteries using Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Sort Term Memory Network (LSTM) models

Joint estimation of State of Charge (SOC) and State of Health (SOH) for lithium ion batteries using Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Sort Term Memory Network (LSTM) models

C-Rate- and Temperature-Dependent State-of-Charge Estimation Method for Li-Ion Batteries in Electric Vehicles

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