A critical review of future aspects of digitalization next generation Li-ion batteries manufacturing process

Dammala, Pradeep Kumar; Dermenci, Kamil Burak; Kathribail, Anish Raj; Yadav, Poonam; Van Mierlo, Joeri; Berecibar, Maitane

doi:10.1016/j.est.2023.109209

Cited by 7 publications

(4 citation statements)

References 153 publications

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“…A general schematization of the framework based on these assertions is provided in Figure 1. The role of AI can also be explained in the following shape: Its capability to handle big data quickly allows for cross-implementation among different contexts and topics, integrating them as part of the general system and improving mechanisms for cells [11]. In particular, they are able to connect EMs with PMs, but are only used for low values of currents.…”

Section: Introductionmentioning

confidence: 99%

“…Here, the physical meaning of the arrangement used for the electric circuit is not linked with the real working principles of the device but instead helps provide the relevant information needed as output. The second approach is derived from the already described approach but exploits the advantages of AI [11,[16][17][18]. Applying Machine Learning (ML) techniques enables the simultaneous achievement of two distinct objectives.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, AI advancements can be applied to ESSs [8,9]. In particular, all processes that refer to prediction, communication, and management can benefit from the implementation of AI algorithms that are capable of reducing the processing time of the evaluation process and merging heterogeneous data [10] to set effective strategies aimed at managing the components better and extending the capability of communications in the wake of the Internet of Things (IoT) [11,12].…”

mentioning

confidence: 99%

“…PMs are mainly constituted by electro-chemical relationships, thus disclosing a higher level of detail in the model adopted. A further step is the composition of PMs with SPMs and/or SEIs; these can delve into all problems set on a micro-scale referring to damaging mechanisms for cells [11]. In particular, they are able to connect EMs with PMs, but are only used for low values of currents.…”

mentioning

confidence: 99%

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Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

Miraftabzadeh,

Longo,

Di Martino

et al. 2024

Electronics

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The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

Miraftabzadeh,

Longo,

Di Martino

et al. 2024

Electronics

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Electrolytes for Sodium Ion Batteries: The Current Transition from Liquid to Solid and Hybrid systems

Darjazi,

Falco,

Colò

et al. 2024

Advanced Materials

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Sodium‐ion batteries (NIBs) have recently garnered significant interest in being employed alongside conventional lithium‐ion batteries, particularly in applications where cost and sustainability are particularly relevant. The rapid progress in NIBs will undoubtedly expedite the commercialization process. In this regard, tailoring and designing electrolyte formulation is a top priority, as they profoundly influence the overall electrochemical performance and thermal, mechanical, and dimensional stability. Moreover, electrolytes play a critical role in determining the system's safety level and overall lifespan. This review delves into recent electrolyte advancements from liquid (organic and ionic liquid) to solid and quasi‐solid electrolyte (dry, hybrid, and single ion conducting electrolyte) for NIBs, encompassing comprehensive strategies for electrolyte design across various materials, systems and their functional applications. Our objective is to offer strategic direction for the systematic production of safe electrolytes and to investigate the potential applications of these designs in real‐world scenarios while thoroughly assessing the current obstacles and forthcoming prospects within this rapidly evolving field.This article is protected by copyright. All rights reserved

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2D Discrete Element Simulation of Electrode Structural Evolutions in Li‐Ion Battery During Drying and Calendering

Lyu,

Dong,

Guo

2024

Energy Tech

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Drying and calendering are critical steps in the manufacture of electrodes for lithium‐ion battery that affect their mechanical and electrochemical properties. A 2D representative volume element (RVE) model, including active material and carbon binder domain particles of different shapes and sizes, is developed. The evolution of the RVE structure is simulated using the discrete element method, providing insight into changes in velocity, coordination number, porosity, pore size distribution, tortuosity, and stress. Based on this analysis, a three‐step drying scheme is proposed in accordance with the experimental drying results. In addition, the calendering process significantly improves the mechanical integrity and electronic conductivity of the electrode. Through simulations and experimental observations of changes in surface morphology and porosity, an optimal compression ratio of about 20% is determined for the electrode.

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A critical review of future aspects of digitalization next generation Li-ion batteries manufacturing process

Cited by 7 publications

References 153 publications

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

Electrolytes for Sodium Ion Batteries: The Current Transition from Liquid to Solid and Hybrid systems

2D Discrete Element Simulation of Electrode Structural Evolutions in Li‐Ion Battery During Drying and Calendering

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