State-of-Health Estimation With Anomalous Aging Indicator Detection of Lithium-Ion Batteries Using Regression Generative Adversarial Network

Zhao, Guangcai; Zhang, Chenghui; Duan, Bin; Zhu, Rui

doi:10.1109/tie.2022.3170630

Cited by 22 publications

(11 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generative adversarial network (GAN) can be applied in SOH estimation for data processing, anomaly detection, and feature engineering, thus enhancing the performance of SOH estimation models. [158,159] Zhao et al [160] developed a GAN capable of simultaneously detecting battery aging indicators and estimating SOH. In the network, the generator is utilized to produce auxiliary training samples that closely resemble the distribution of real data, while the discriminator monitors the distribution of real samples for detecting aging indicators.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

Yang,

Zhang,

Wang

et al. 2024

Carbon Neutralization

View full text Add to dashboard Cite

The issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries.

show abstract

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

Yang,

Zhang,

Wang

et al. 2024

Carbon Neutralization

View full text Add to dashboard Cite

show abstract

“…This approach accurately captured intricate changes and seasonal variations in photovoltaic power plants, thus augmenting the historical photovoltaic dataset. Zhao et al [28] introduced a health status estimation method for precise measurement of the health status of lithium-ion batteries employing GANs. This method devised a generator to automatically generate supplementary training samples that resembled real samples for data augmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Zhao et al. [28] introduced a health status estimation method for precise measurement of the health status of lithium‐ion batteries employing GANs. This method devised a generator to automatically generate supplementary training samples that resembled real samples for data augmentation.…”

Section: Introductionmentioning

confidence: 99%

Short‐term power forecasting method for 5G photovoltaic base stations on non‐sunny days based on SDN‐integrated INGO‐BP and RGAN

Huang,

Guo,

Wei

et al. 2024

IET Renewable Power Gen

View full text Add to dashboard Cite

In response to the suboptimal efficiency observed in the network configuration and administration of 5G photovoltaic base stations (PVBSs), as well as the inherent limitations in accurately forecasting photovoltaic power during inclement weather conditions, this research article introduces a concise and effective method for short‐term power prediction of PVBSs when subjected to non‐sunny weather conditions, leveraging the paradigm of software defined networking. This method ingeniously amalgamates the improved Northern Goshawk optimized back propagation neural network (INGO‐BP) and recurrent generative adversarial networks (RGAN). The investigation commences by delineating a comprehensive PVBS system framework underpinned by SDN principles. Subsequently, it employs the Pearson correlation coefficient technique and principal component analysis for the judicious selection of pertinent features from the PVBS photovoltaic dataset, thereby ameliorating the issue of multicollinearity within the predictive model. To culminate the research endeavour, the INGO algorithm is enlisted to optimize the weightings and bias parameters of the BP neural network, while RGAN is harnessed for the acquisition of distinctive distribution patterns in photovoltaic data across diverse meteorological conditions. This augmentation enhances the model's capacity to discern the intricate long‐term mappings between photovoltaic power and meteorological data. The empirical findings substantiate the elevated accuracy of the INGO‐BP prediction model across sunny, cloudy, and rainy weather conditions, with a discernible enhancement in predictive accuracy for PVBS performance under non‐sunny weather conditions facilitated by data augmentation via RGAN.

show abstract

“…In [18], both the internal capacitance and resistance of the batteries are used to characterize the SoH based on a nonlinear autoregressive exogenous architecture. An alternative solution to obtain the general model of batterie is based on a new regression generative adversarial network (RGAN) [19]. The optimization process of the RGAN and the way of using RGAN to estimate the SoH under abnormal conditions are also presented.…”

Section: Introductionmentioning

confidence: 99%

An Ultrafast Variable Forgetting Factor Recursive Least Square Method for Determining the State-of-Health of Li-Ion Batteries

Mao,

Bao,

Zhang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Fast and accurate detections of state-of-health (SoH) are urgently required by various industrial sectors to facilitate reuse and recycling of Li-ion batteries. However, existing SoH identification methods rarely reconcile the monitoring speed and accuracy, which results in redundant carbon footprints and electronic wastes. To address this critical issue, an ultrafast and accurate SoH monitoring is proposed in this paper. The proposed variable forgetting factor Recursive Least Squares (VFFRLS) method is designed based on a least square method with forgetting factors to determine the electrical parameters of a simple electrical model. The established electrical model can be easily implemented using cost-effective micro-controller units (MCUs). The weighting factors of the objective function is automatically determined based on the Genetic Algorithm (GA). Experimental results validate the superior performance of the proposed strategy over conventional methods in detecting the SoH of one new and three aged batteries. For the new battery, the relative error of the estimated SoH is almost 0%. For the aged batteries, the relative error of the estimated SoH is less than 2.78%. State-of-health (SoH), Li-ion battery, electrical model, variable forgetting factor Recursive Least Squares (VFFRLS) method, Genetic Algorithm (GA). INDEX TERMS

show abstract

State-of-Health Estimation With Anomalous Aging Indicator Detection of Lithium-Ion Batteries Using Regression Generative Adversarial Network

Cited by 22 publications

References 32 publications

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

Short‐term power forecasting method for 5G photovoltaic base stations on non‐sunny days based on SDN‐integrated INGO‐BP and RGAN

An Ultrafast Variable Forgetting Factor Recursive Least Square Method for Determining the State-of-Health of Li-Ion Batteries

Contact Info

Product

Resources

About