State-of-Charge Estimation of the Lithium-Ion Battery Using Neural Network Based on an Improved Thevenin Circuit Model

Zhang, Haoliang; Na, Woonki; Kim, Jong-Hoon

doi:10.1109/itec.2018.8450162

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…As can be seen from the analysis in Section 4.1 and Figure 10, in the SoC interval of 0.45-0.75, the difference in the different fitting curves is not large, and the relationship between dQ/dV and SoH under different SoCs is quite similar, that is, the accuracy of the SoC estimation in this interval has relatively inconspicuous influence on SoH estimation, while in the SoC region at both ends there are a few obvious fluctuation points, that is, when the error of the SoC is amplified to 2%, the estimation accuracy of the SoH estimation method has begun to be affected by the SoC error in the region of the SoC from 0.3-0.45. Fortunately, all the commonly used SoC estimation methods limit the SoC error to 1% or less [21][22][23]. Therefore, in this paper, the selection of the SoC estimation methods has not been discussed.…”

Section: Influence Of Soc Estimation Error On Soh Estimation Resultsmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.

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Section: Influence Of Soc Estimation Error On Soh Estimation Resultsmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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“…Finally, the optimized neural network is used to estimate the battery SOC to solve the difficult problem of neural network modeling. In order to solve the problem of inconsistent estimated values caused by the instability of the initial value of the neural network and network parameter settings, the use of dual neural networks for real-time battery parameter identification and battery SOC estimation can improve the estimation accuracy while reducing the calculation pressure [78]. The neural network algorithm is suitable for various power batteries, but requires a lot of data for training.…”

Section: Soc Estimation Methods Based On the Black Box Battery Modelmentioning

confidence: 99%

Review on the Battery Model and SOC Estimation Method

et al. 2021

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The accuracy of the power battery model and SOC estimation directly affects the vehicle energy management control strategy and the performance of the electric vehicle, which is of great significance to the efficient management of the battery and the improvement of the reliability of the vehicle. Based on the research of domestic and foreign battery models and the previous results of SOC estimation, this paper classifies power battery models into electrochemical mechanism models, equivalent circuit models and data-driven models. This paper analyzes the advantages and disadvantages of various battery models and current research progress. According to the choice of battery model, the previous research results of the power battery SOC estimation method are divided into three categories: the direct measurement method not based on battery model, the estimation method using black box battery model, and the battery model SOC estimation method based on state space. This paper will summarize and analyze the principles, applicable scenarios and research progress of the three categories of estimation algorithms aiming to provide references for future in-depth research. Finally, in view of the shortcomings of the battery model and estimation algorithm of the existing method, the future improvement direction is proposed.

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“…Unlike image classification, which assigns a single class label to the entire image, semantic segmentation is a more granular task, amounting to pixel-level classification [1] . Over the past few years, the computer vision community has heavily relied on effective deep neural networks (DNNs) designed for semantic segmentation, as evidenced by recent research [2][3][4][5][6][7][8][9][10][11] . These efficient DNNs are characterized by their low computational demands and quick inference times [12] , and their widespread adoption has significantly influenced applications in various fields such as autonomous driving [13,14] , semantic segmentation enables precise scene understanding, allowing the vehicle to identify and differentiate between various objects on the road, such as pedestrians, vehicles, traffic signs, and obstacles.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Semantic Segmentation through Reinforced Active Learning: Combating Dataset Imbalances and Bolstering Annotation Efficiency

Han,

Huong,

Cheng

2023

j. of electron. & inf. syst.

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This research addresses the challenges of training large semantic segmentation models for image analysis,focusing on expediting the annotation process and mitigating imbalanced datasets. In the context of imbalanceddatasets, biases related to age and gender in clinical contexts and skewed representation in natural images can affectmodel performance. Strategies to mitigate these biases are explored to enhance efficiency and accuracy in semanticsegmentation analysis. An in-depth exploration of various reinforced active learning methodologies for imagesegmentation is conducted, optimizing precision and efficiency across diverse domains. The proposed frameworkintegrates Dueling Deep Q-Networks (DQN), Prioritized Experience Replay, Noisy Networks, and EmphasizingRecent Experience. Extensive experimentation and evaluation of diverse datasets reveal both improvements andlimitations associated with various approaches in terms of overall accuracy and efficiency. This research contributesto the expansion of reinforced active learning methodologies for image segmentation, paving the way for moresophisticated and precise segmentation algorithms across diverse domains. The findings emphasize the need fora careful balance between exploration and exploitation strategies in reinforcement learning for effective imagesegmentation.

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State-of-Charge Estimation of the Lithium-Ion Battery Using Neural Network Based on an Improved Thevenin Circuit Model

Cited by 8 publications

References 22 publications

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Review on the Battery Model and SOC Estimation Method

Enhancing Semantic Segmentation through Reinforced Active Learning: Combating Dataset Imbalances and Bolstering Annotation Efficiency

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