AC Impedance Based Online State-of-Charge Estimation for Li-ion Battery

Wu, Shing-Lih; Chen, Hung-Cheng; Tsai, Ming-Yang; Lin, Tong-Chou; Chen, Liang-Ruei

doi:10.1109/icice.2017.8479183

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…We found the significant improvement for the SOC estimation by combining the EKF with ANN, which had the SOC error of less than 1%. Comparisons were made with other SOC estimation methods [37][38][39][40][41][42][43][44][45][46][47][48][49] and the maximum estimation errors are summarized in Table 7. Through this, it was confirmed that the proposed method guarantees the accuracy of SOC estimation similar to or better than other methods.…”

Section: Experiments Results Of the Soc Estimation 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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Section: Experiments Results Of the Soc Estimation 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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“…By using the mapping relationship between battery characteristic and SOC, the battery behavior is characterized by a large number of experiments. The conventional look-up table methods mainly includes open-circuit voltage method [6] and AC impedance method [7]. Still, the estimation accuracy depends on the accuracy of the look-up table established by experiments.…”

Section: Introductionmentioning

confidence: 99%

An Improved Bidirectional Gated Recurrent Unit Method for Accurate State-of-Charge Estimation

et al. 2021

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State-of-Charge (SOC) estimation of lithium-ion batteries have a great significance for ensuring the safety and reliability of battery management systems in electrical vehicle. Deep learning method can hierarchically extract complex feature information from input data by building deep neural networks (DNNs) with multi-layer nonlinear transformations. With the development of graphic processing unit, the training speed of the network is faster than before, and it has been proved to be an effective data-driven method to estimate SOC. In order to further explore the potential of DNNs in SOC estimation, take battery measurements like voltage, current and temperature directly as input and SOC as output, an improved method using the Nesterov Accelerated Gradient (NAG) algorithm based Bidirectional Gated Recurrent Unit (Bi-GRU) network is put forward in this paper. Notably, to address the oscillation problem existing in the traditional gradient descent algorithm, NAG is used to optimize the Bi-GRU. The gradient update direction is corrected by considering the gradient influence of the historical and the current moment, combined with the estimated location of the parameters at the next moment. Compared to state-of-the-art estimation methods, the proposed method enables to capture battery temporal information in both forward and backward directions and get independent context information. Finally, two well-recognized lithium-ion batteries datasets from University of Maryland and McMaster University are applied to verify the validity of the research. Compared with the previous methods, the experimental results demonstrate that the proposed NAG based Bi-GRU method for SOC estimation can improve the precision of the prediction at various ambient temperature.INDEX TERMS State-of-Charge estimation, lithium-ion batteries, Bidirectional Gated Recurrent Unit, Nesterov Accelerated Gradient.

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A Portable Impedance Analyzer for Lithium-ion Batteries

Huang,

Zhu

2024

2024 9th International Conference on Electronic Technology and Information Science (ICETIS)

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AC Impedance Based Online State-of-Charge Estimation for Li-ion Battery

Cited by 9 publications

References 11 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

An Improved Bidirectional Gated Recurrent Unit Method for Accurate State-of-Charge Estimation

A Portable Impedance Analyzer for Lithium-ion Batteries

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