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

Eyyup Aslan () and Yusuf Yasa
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Eyyup Aslan: Aurora Flight Sciences, a Boeing Company, Manassas, VA 20110, USA
Yusuf Yasa: Electrical Engineering Department, Istanbul Technical University, 34485 Istanbul, Turkey

Energies, 2024, vol. 17, issue 13, 1-23

Abstract: 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.

Keywords: electric vehicle; equivalent circuit model; lithium-ion battery; state of charge; unscented Kalman filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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