An Intelligent Preheating Approach Based on High-Gain Control for Lithium-Ion Batteries in Extremely Cold Environment

Shang, Yunlong; Chen, Guicheng; Pan, Qiao; Zhu, Tao; Liu, Kailong

doi:10.1109/tie.2023.3288181

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…Therefore, it is of importance to find a solution to balance the preheating speed and the potential damage to the battery. Based on this idea, Shang et al [7] builds an internal preheating model called high-gain control based preheating model which can automatically change the preheating current according to the real-time battery temperature to realize the balance of temperature rising and battery safety. The results of their experiments show that, compared to the conventional internal preheating model, the proposed model succeeds in preheating the battery in a limited time and applying the maximal safe heating current dynamically through the whole process of preheating.…”

Section: Internal Preheatingmentioning

confidence: 99%

Review on preheating systems for Lithium-ion batteries of electric vehicles under low temperature circumstance

2024

ACE

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Nowadays, energy and environmental protection issues have become the focus of most attention. The development of electric vehicles which depends on lithium ion battery as power source is one of the most applicable way in dealing with these issues. Lithium-ion battery (LIB) is highly favoured for its outstanding features in energy density, cycle life and energy retention. However, its severe sensitivity to working temperatures leads to problems when driving electric vehicles. Therefore, researchers and engineers have explored approaches to guaranteeing a suitable working temperature for LIB, one of which is the battery preheating system. To clarify the advancement of this system, both internal and external preheating methods studied in recent years are summarized, and the discussion for future research is included.

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Section: Internal Preheatingmentioning

confidence: 99%

Review on preheating systems for Lithium-ion batteries of electric vehicles under low temperature circumstance

2024

ACE

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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]. In summary, based on a well-developed machine learning method, an effective management solution can be obtained to improve the operational performance of batteries.…”

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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Alternating current heating techniques for lithium-ion batteries in electric vehicles: Recent advances and perspectives

Huang,

Meng,

Jiang

et al. 2024

Journal of Energy Chemistry

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An Intelligent Preheating Approach Based on High-Gain Control for Lithium-Ion Batteries in Extremely Cold Environment

Abstract: K. ( 2023). An intelligent preheating approach based on high-gain control for lithium-ion batteries in extremely cold environment. IEEE Transactions on Industrial Electronics.

Cited by 4 publications

References 38 publications

Review on preheating systems for Lithium-ion batteries of electric vehicles under low temperature circumstance

Review on preheating systems for Lithium-ion batteries of electric vehicles under low temperature circumstance

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

Alternating current heating techniques for lithium-ion batteries in electric vehicles: Recent advances and perspectives

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