<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>As the energy carrier of electric vehicles, how to accurately estimate the remaining power (SOC) of the battery is one of the key technologies in the field of electric vehicle design. Effective estimation of SOC can bring accurate continuous mileage information to the driver, theoretically avoid overcharging and discharging the battery, and also protect the driver's driving safety. In the research of SOC estimation method, constructing a suitable battery model is an important means to improve SOC estimation and to improve the prediction accuracy. In order to obtain a higher response accuracy of the model, this paper proposes an electric vehicle SOC model based on the extended Kalman filter algorithm. Based on the actual data of lithium-ion power battery, SOC estimation research is carried out. The research shows that: when the internal temperature of the battery is the same as the ambient temperature, and both are 25 °C, the model is accurate, the terminal voltage difference is small, and the average voltage difference is 9mV respectively; at room temperature, the extended Kalman filter algorithm has a significant effect on the recovery percentage of SOC voltage. The average is over 73%, and the accuracy is high. The extended Kalman algorithm in this paper we use to estimate the SOC current waveform. The simulation results show that the SOC discharge current is 4A, which has high estimation accuracy and strong applicability.</p>
</abstract>

As the energy carrier of electric vehicles, how to accurately estimate the remaining power (SOC) of the battery is one of the key technologies in the field of electric vehicle design. Effective estimation of SOC can bring accurate continuous mileage information to the driver, theoretically avoid overcharging and discharging the battery, and also protect the driver's driving safety. In the research of SOC estimation method, constructing a suitable battery model is an important means to improve SOC estimation and to improve the prediction accuracy. In order to obtain a higher response accuracy of the model, this paper proposes an electric vehicle SOC model based on the extended Kalman filter algorithm. Based on the actual data of lithium-ion power battery, SOC estimation research is carried out. The research shows that: when the internal temperature of the battery is the same as the ambient temperature, and both are 25 °C, the model is accurate, the terminal voltage difference is small, and the average voltage difference is 9mV respectively; at room temperature, the extended Kalman filter algorithm has a significant effect on the recovery percentage of SOC voltage. The average is over 73%, and the accuracy is high. The extended Kalman algorithm in this paper we use to estimate the SOC current waveform. The simulation results show that the SOC discharge current is 4A, which has high estimation accuracy and strong applicability.

Research on SOC estimation of residual power of lithium-ion batteries for electric vehicles based on extended Kalman filtering algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

As the energy carrier of electric vehicles, how to accurately estimate the remaining power (SOC) of the battery is one of the key technologies in the field of...

Research on SOC estimation of residual power of lithium-ion batteries for electric vehicles based on extended Kalman filtering algorithm

Online veröffentlicht: 10. März 2023

Seitenbereich: 2849 - 2860

Eingereicht: 25. Feb. 2022

Akzeptiert: 04. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00005

Schlüsselwörter
Extended Kalman filtering algorithm, Electric vehicles, Lithium-ion batteries, Remaining power, SOC

© 2023 Haifeng Jiang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on SOC estimation of residual power of lithium-ion batteries for electric vehicles based on extended Kalman filtering algorithm

Online veröffentlicht: 10. März 2023

Seitenbereich: 2849 - 2860

Eingereicht: 25. Feb. 2022

Akzeptiert: 04. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00005

SchlüsselwörterExtended Kalman filtering algorithm, Electric vehicles, Lithium-ion batteries, Remaining power, SOC

© 2023 Haifeng Jiang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Extended Kalman filtering algorithm, Electric vehicles, Lithium-ion batteries, Remaining power, SOC