Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network

Wang, Zhe; Yang, Fangfang; Xu, Qiang; Wang, Yongjian; Yan, Hong; Xie, Min

doi:10.1016/j.apenergy.2023.120808

Cited by 34 publications

(6 citation statements)

References 38 publications

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“…Exploring complex augmentation methods, alternative ensemble solutions, and liquid neural networks (LNNs) [76][77][78] could further refine model performance and introduce more efficient, adaptable, and robust approaches to battery health estimation. There is also potential for the use of explaining the LIB RUL using graph neural networks, which have also been shown to significantly reduce parameter count and perform better than their traditional physics-based model counterparts [79,80]. Future research may leverage LNNs or GNNs, known for their dynamic adaptability, to potentially enhance RUL prediction for LIBs.…”

Section: Discussionmentioning

confidence: 99%

Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

Rastegarpanah,

Asif,

Stolkin

2024

Batteries

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With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the convolutional long short-term memory deep neural network (CLDNN) model and the transformer LSTM (temporal transformer) model have emerged as standout remaining useful life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable mean absolute error (MAE) of 84.012 and a mean absolute percentage error (MAPE) of 25.676. Similarly, the temporal transformer model exhibited a notable performance, with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved by applying Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were bench-marked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%.

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Section: Discussionmentioning

confidence: 99%

Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

Rastegarpanah,

Asif,

Stolkin

2024

Batteries

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“…As shown in equation (1) , the objective function consists of two parts, the first part is related to the income from the sale of LS, ES, and OG strategies. In the objective function, aggregators compensate for load reduction by more accurately predicting energy market prices, which is usually possible using numerical techniques such as time series and neural networks [ 20 , 40 ]. The second part of the objective function is related to the costs paid to the customers who participated in load reduction programs.…”

Section: Problem Formulationsmentioning

confidence: 99%

Optimal demand response aggregation in wholesale electricity markets: Comparative analysis of polyhedral; ellipsoidal and box methods for modeling uncertainties

Nojavan,

Hagh,

Taghizad-Tavana

et al. 2024

Heliyon

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“…Both Wang et al [62] and Wei et al [63] propose innovative approaches that utilize advanced machine learning techniques to address key challenges in LIB management, such as accurate capacity estimation, SOH prediction, and RUL prediction. Wang et al [62] leverage a graph neural network (GNN) to estimate LIB capacity by integrating diverse sensor measurements into a graph-like structure. They use neural architecture search to select data aggregation and feature fusion operations, improving model adaptability.…”

Section: Graph Neural Networkmentioning

confidence: 99%

“…Wang et al [62] have made a transformative breakthrough in the realm of LIB capacity estimation with their pioneering approach. Traditional methods for LIB capacity estimation have been hindered by hand-crafted feature engineering or complex data-driven approaches requiring intricate network designs and laborious trial-and-error iterations.…”

Section: Graph Neural Networkmentioning

confidence: 99%

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

Madani,

Ziebert,

Vahdatkhah

et al. 2024

Batteries

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In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring.

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Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network

Cited by 34 publications

References 38 publications

Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

Optimal demand response aggregation in wholesale electricity markets: Comparative analysis of polyhedral; ellipsoidal and box methods for modeling uncertainties

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

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