Lithium Battery SOC Estimation Based on Extended Kalman Filtering Algorithm

Li, Haoran; Liang-Dong, LI; Xiaoyin, Zheng; Mingxuan, Sun

doi:10.1109/ccsse.2018.8724766

Cited by 17 publications

(5 citation statements)

References 3 publications

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“…It is suitable for the SOC estimation of EVs in which the battery current is unstable [24]. However, it has high demands for the battery modelling and computational capability [25,26].…”

Section: Kalman Filter-based Methodsmentioning

confidence: 99%

Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network

et al. 2021

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Currently, Lithium-ion batteries (LiB) are widely applied in energy storage devices in smart grids and electric vehicles. The state of charge (SOC) is an indication of the available battery capacity, and is one of the most important factors that should be monitored to optimize LiB’s performance and improve its lifetime. However, because the SOC relies on many nonlinear factors, it is difficult to estimate accurately. This paper presented the design of an effective SOC estimation method for a LiB pack Battery Management System (BMS) based on Kalman Filter (KF) and Artificial Neural Network (ANN). First, considering the configuration and specifications of the BMS and LiB pack, an ANN was constructed for the SOC estimation, and then the ANN was trained and tested using the Google TensorFlow open-source library. An SOC estimation model based on the extended KF (EKF) and a Thevenin battery model was developed. Then, we proposed a combined mode EKF-ANN that integrates the estimation of the EKF into the ANN. Both methods were evaluated through experiments conducted on a real LiB pack. As a result, the ANN and KF methods showed maximum errors of 2.6% and 2.8%, but the EKF-ANN method showed better performance with less than 1% error.

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“…It is suitable for the SOC estimation of EVs in which the battery current is unstable [24]. However, it has high demands for the battery modelling and computational capability [25,26].…”

Section: Kalman Filter-based Methodsmentioning

confidence: 99%

Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network

et al. 2021

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“…In this mode, the control objective aims to regulate the output current of the boost converter to help discharging the battery without causing any damage to Again, using the backstepping control approach, the control law will be carried out in one step. First, let 𝑧 denotes the boost converter output current tracking error: (51) The dynamics of the previous error is expressed as: (52) of the error 𝑧 , to be the Lyapunov candidate along the error 𝑧 gives:…”

Section: Controllers Design For Battery Energy Storage Systemmentioning

confidence: 99%

“…This type of battery is pollution-free and has a low self-discharge rate and a long life. Several research works have been carried out on batteries, which dealt with giving the mathematical shape of the battery, controlling the battery charge, estimating the battery parameters, and protecting itagainst risks using neural networks [51], the extended Kalman filter (EKF) [52], fuzzy logic [53], and the unscented Kalman filter [54]. Using these strategies requires a large amount of data experimentation, long-term calculations, and a learning algorithm to achieve a reliable estimation of the battery terminals voltage.…”

Section: Introductionmentioning

confidence: 99%

Robust Control of DFIG-Based WECS Integrating an Energy Storage System With Intelligent MPPT Under a Real Wind Profile

Chojaa,

Derouich,

Zamzoum

et al. 2023

IEEE Access

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This research work addresses the challenge of controlling a wind energy conversion system (WECS) connected to the grid, which incorporates a battery energy storage system and operates under varying real wind speed profiles. The system comprises a Doubly-Fed Induction Generator (DFIG) connected to the grid through an AC/DC/AC converter, along with a Li-ion battery pack storage unit linked to the Back-to-Back converter DC via a DC/DC converter. The study highlights that internal parametric variations and the non-linear behavior of the wind turbine negatively affect the energy quality produced, as well as the battery charging performance and its overall lifespan. To overcome these issues, the research proposes a robust control strategy based on Integral action Sliding Mode Control (ISMC) to independently manage the powers of the WECS, particularly the DFIG, to enhance dynamic performance. Furthermore, effective battery charging and discharging controllers are crucial for efficiently distributing the generated power to both the grid and the storage unit, ensuring proper energy extraction and battery charging/discharging. Depending on the battery's State of Charge (SOC), the available extracted energy, and the power injected into the grid, two current regulation modes are employed: buck charging mode and boost discharging mode. A storage system energy management algorithm is then utilized to determine the appropriate charging mode, ensuring battery safety. To validate the system's performance, a 1.5 MW wind power conversion system is tested using Matlab/Simulink simulations. The results demonstrate the robustness and efficiency of the proposed control approach in enhancing the overall system's performance.

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“…Energy and environmental issues are fundamental issues related to human development, and oil, as one of the cores of today's energy structure, is facing an increasingly depleted status quo, and if the energy structure is not changed, oil resources will be depleted within this century. [1][2][3][4][5]. In 2021, the sources of urban air pollution in China are shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

2023

Applied Mathematics and Nonlinear Sciences

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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.

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Lithium Battery SOC Estimation Based on Extended Kalman Filtering Algorithm

Cited by 17 publications

References 3 publications

Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network

Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network

Robust Control of DFIG-Based WECS Integrating an Energy Storage System With Intelligent MPPT Under a Real Wind Profile

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

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