Multilevel Data-Driven Battery Management: From Internal Sensing to Big Data Utilization

Wei, Zhongbao; Liu, Kailong; Liu, Xinghua; Li, Yang; Du, Liang; Gao, Fei

doi:10.1109/tte.2023.3301990

Cited by 14 publications

(5 citation statements)

References 182 publications

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“…With the rapid development of artificial intelligence, machine learning and computational platforms [8], machine-learning-based methods have become a powerful solution to management issues in batteries [9,10]. Numerous machine-learning-based solutions have been developed to estimate batteries' internal states [11][12][13][14], forecast batteries' future ageing dynamics [15][16][17] and remaining useful life (RUL) [18,19], diagnose battery faults [20][21][22] and optimize battery charging [23][24][25][26] and energy management [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery

Dai,

Xiang,

Zhou

et al. 2024

WEVJ

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Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by the manufacturing process, particularly in the production of electrodes. However, efficiently analysing the effects of key manufacturing features and predicting the mass loading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effectively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key features during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machine-learning-based regression methods, including decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in the mixing and coating stages. The comparative results show that the developed machine-learning-based approach is able to provide a satisfactory prediction of the electrode mass loading of a solid-state battery with 0.995 R2 while successfully quantifying the importance of four key features in the early manufacturing stages. Due to the advantages of its data-driven nature, the developed machine-learning-based approach can efficiently assist engineers in monitoring/predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batteries for further energy storage applications.

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Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery

Dai,

Xiang,

Zhou

et al. 2024

WEVJ

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show abstract

“…The experimental campaigns are generally expensive. Therefore, different approaches, such as laboratory Energies 2023, 16, 6887 2 of 26 protocols, have been tried to gather data instead of doing a test directly on the BESS. Many studies have tried to extend the cell-level aging models to the BESS level, but this can give rise to many inaccuracies and faults in the models.…”

Section: Introductionmentioning

confidence: 99%

“…Extracting extensive features from limited cycle data is pivotal for enhancing predictive models. However, as batteries age, they often exhibit a nonlinear degradation phenomenon known as the "knee point" [16,17]. Accurately predicting this critical transition poses a significant challenge for AI methods with limited monitoring cycles.…”

Section: Introductionmentioning

confidence: 99%

“…To circumvent these limitations, researchers have advocated the integration of AI methods during the manufacturing phase. By doing so, AI can encompass the intricacies of Energies 2023, 16, 6887 3 of 26 battery production that impact its longevity, offering a comprehensive approach to address these complexities [16].…”

Section: Introductionmentioning

confidence: 99%

“…Energies 2023, 16,6887 Therefore, each study focused on some of them. Generally, it is possible aging processes to one of the following modes [24].…”

mentioning

confidence: 99%

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A Review on Testing of Electrochemical Cells for Aging Models in BESS

Pakjoo,

Piegari,

Rancilio

et al. 2023

Energies

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The use of electrochemical cells is becoming more widespread, especially in the energy industry and battery energy storage systems (BESSs). As we continue to deploy BESSs, it becomes increasingly important for us to understand how these systems age and accurately predict their performance over time. This knowledge is essential for ensuring that the systems operate optimally and can be properly maintained. Since the structure of a BESS is different from a single electrochemical cell, the existing models at the cell level cannot predict and estimate the life of the BESS with suitable accuracy. Furthermore, the test protocols available at the cell level mostly cannot be executed at the BESS level for many reasons. Therefore, in this paper, a review of test protocols for building aging models for BESSs has been performed. After reviewing the protocols for a single electrochemical cell and addressing the differences between BESSs and cells, a review of the works performed on a larger scale has been carried out, and the possible ways for testing the BESS for aging models were investigated.

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Benchmarking core temperature forecasting for lithium-ion battery using typical recurrent neural networks

Zhang,

Xiang,

Xiong

et al. 2024

Applied Thermal Engineering

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Multilevel Data-Driven Battery Management: From Internal Sensing to Big Data Utilization

Cited by 14 publications

References 182 publications

A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery

A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery

A Review on Testing of Electrochemical Cells for Aging Models in BESS

Benchmarking core temperature forecasting for lithium-ion battery using typical recurrent neural networks

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