A light-weight feature extractor for lithium-ion battery health prognosis

Zhou, Danhua; Wang, Bin; Zhu, Chao; Zhou, Fang; Wang, Hong

doi:10.1016/j.ress.2023.109352

Cited by 10 publications

(7 citation statements)

References 39 publications

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“…The state of charge (SOC) range, which refers to the range of charge levels maintained during the charging and discharging processes, has a significant impact on the health status of a power battery.Both shallow state of charge (high SOC) and deep state of charge (low SOC) can have negative effects on battery health.In shallow state of charge, the battery is prone to corrosion and oxidation reactions, especially in high-temperature environments [4] . This can result in capacity loss and increased internal resistance, thereby reducing battery performance and lifespan.In deep state of charge, the chemical reactions within the battery become less stable, leading to polarization and delamination.…”

Section: State Of Charge Rangementioning

confidence: 99%

Analysis of the relationship between operating parameters and health state of power batteries

Pi,

Liu

2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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The health state of power batteries is influenced by various operating parameters, including charge-discharge rate, state of charge, and operating temperature. In this study, experiments were conducted to test the variations in the health state of power batteries under different charge-discharge rates, state of charge, and operating temperatures. The impact of these operating parameters on the health state of power batteries was analyzed. This research not only contributes to a more accurate assessment of the health state of power batteries but also provides labeled values for different operating parameters, which can be utilized in machine learning algorithms for determining the health state of power batteries. This, in turn, promotes the development of algorithms for assessing the health state of power batteries.

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Section: State Of Charge Rangementioning

confidence: 99%

Analysis of the relationship between operating parameters and health state of power batteries

Pi,

Liu

2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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“…The normalization factor for Equation ( 5) is this likelihood function. The normalization factor may be calculated using Equation (11) as follows.…”

Section: Gwo-brnn-based Soh Estimation Incorporating Hfs and Correlat...mentioning

confidence: 99%

“…The normalization factor for Equation ( 5) is this likelih tion. The normalization factor may be calculated using Equation (11) as follows 𝐻(𝐺|ϐ, ϭ, 𝐶) = 𝐻(𝐺|𝑟, ϐ, 𝐶)𝐻(𝑟|ϐ, 𝐶) 𝐻(𝑟|𝐺, ϐ, ϭ, 𝐶)…”

Section: 𝑟 = 𝑟 − 𝐿 𝐿 + 𝛳𝐼 𝐿 𝑒mentioning

confidence: 99%

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Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management

Waseem,

Huang,

Wong

et al. 2023

Mathematics

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Due to the complexity of the aging process, maintaining the state of health (SOH) of lithium-ion batteries is a significant challenge that must be overcome. This study presents a new SOH estimation approach based on hybrid Grey Wolf Optimization (GWO) with Bayesian Regularized Neural Networks (BRNN). The approach utilizes health features (HFs) extracted from the battery charging-discharging process. Selected external voltage and current characteristics from the charging-discharging process serve as HFs to explain the aging mechanism of the batteries. The Pearson correlation coefficient, the Kendall rank correlation coefficient, and the Spearman rank correlation coefficient are then employed to select HFs that have a high degree of association with battery capacity. In this paper, GWO is introduced as a method for optimizing and selecting appropriate hyper-p parameters for BRNN. GWO-BRNN updates the population through mutation, crossover, and screening operations to obtain the globally optimal solution and improve the ability to conduct global searches. The validity of the proposed technique was assessed by examining the NASA battery dataset. Based on the simulation results, the presented approach demonstrates a higher level of accuracy. The proposed GWO-BRNN-based SOH estimation achieves estimate assessment indicators of less than 1%, significantly lower than the estimated results obtained by existing approaches. The proposed framework helps develop electric vehicle battery prognostics and health management for the widespread use of eco-friendly and reliable electric transportation.

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“…The low-cost linear operation is crafted to be learnable and inputdependent, allowing it to be optimized using backpropagation in the backward pass. The effectiveness of this network as a feature extractor [128][129][130] is proved to ideally fit the Orientation-Dimensions Estimator, and the Bird's Eye View Center Estimator, which results in overall only 14 million parameters for both models while competing with the state-of-the-art monocular 3D object detection models.…”

Section: Ghostnet As An Off-the-shelf Lightweight Feature Extractormentioning

confidence: 99%

MonoGhost: Lightweight Monocular GhostNet 3D Object Properties Estimation for Autonomous Driving

El-Dawy,

El-Zawawi,

El-Habrouk

2023

Robotics

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Effective environmental perception is critical for autonomous driving; thus, the perception system requires collecting 3D information of the surrounding objects, such as their dimensions, locations, and orientation in space. Recently, deep learning has been widely used in perception systems that convert image features from a camera into semantic information. This paper presents the MonoGhost network, a lightweight Monocular GhostNet deep learning technique for full 3D object properties estimation from a single frame monocular image. Unlike other techniques, the proposed MonoGhost network first estimates relatively reliable 3D object properties depending on efficient feature extractor. The proposed MonoGhost network estimates the orientation of the 3D object as well as the 3D dimensions of that object, resulting in reasonably small errors in the dimensions estimations versus other networks. These estimations, combined with the translation projection constraints imposed by the 2D detection coordinates, allow for the prediction of a robust and dependable Bird’s Eye View bounding box. The experimental outcomes prove that the proposed MonoGhost network performs better than other state-of-the-art networks in the Bird’s Eye View of the KITTI dataset benchmark by scoring 16.73% on the moderate class and 15.01% on the hard class while preserving real-time requirements.

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A light-weight feature extractor for lithium-ion battery health prognosis

Cited by 10 publications

References 39 publications

Analysis of the relationship between operating parameters and health state of power batteries

Analysis of the relationship between operating parameters and health state of power batteries

Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management

MonoGhost: Lightweight Monocular GhostNet 3D Object Properties Estimation for Autonomous Driving

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