Boosting Rechargeable Batteries R&amp;D by Multiscale Modeling: Myth or Reality?

Franco, Alejandro A.; Rucci, A.; Brandell, Daniel; Frayret, Christine; Gaberšček, Miran; Jankowski, Piotr; Johansson, Patrik

doi:10.1021/acs.chemrev.8b00239

Cited by 251 publications

(289 citation statements)

References 411 publications

(743 reference statements)

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“…In order to face the "bad apples" of these revolutions, such as pollution and climate change, the humankind urgently needs technologies able to transform efficiently renewable energies into electrical one, as well as devices to store it. A possible strategy to address this challenge is through computational simulations, as it was reported by us [5][6][7] and by Thomitzek et al [8] who developed an agent-based process chain model in order to evaluate the impact of the manufacturing process parameters on the battery performance. [1][2][3] The performance of LIBs is strongly correlated to their manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies

Cunha

Lombardo

Primo

et al. 2019

Batteries & Supercaps

Self Cite

120

110

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The number of parameters involved in lithium-ion battery electrode manufacturing and the complexity of the physicochemical interactions throughout the associated processes make highly complex to find interdependencies between the final electrode characteristics and the fabrication parameters. In this work, we have analyzed three different machine-learning algorithms (decision tree, support vector machine, and deep neural network) in order to find the best one to uncover the interdependencies between the slurry manufacturing parame-ters and the final properties of NMC-based cathodes. The results revealed that the support vector machine method shows high accuracy and the possibility to predict the influence of manufacturing parameters on themass loading and porosity of the electrodes in a straightforward graphical way. Furthermore, we report for the first time this new approach and a case study that, by comparing the trends observed experimentally and from the model, demonstrates the validity and the quality of the proposed approach.[a] R.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies

Cunha

Lombardo

Primo

et al. 2019

Batteries & Supercaps

Self Cite

120

110

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“…In general, any scientific approach adopted to try to overcome the aforementioned technical challenges as well as to design and optimize any kind of electrochemical energy storage device, requires transdisciplinary efforts, encompassing at least materials science, chemistry, physics and engineering. Indeed, lithium‐based batteries are made of multiple materials and their operation implicate numerous physicochemical mechanisms occurring simultaneously at multiple spatial scales [11] . To successfully implement the required transdisciplinary approaches and to favor the invention of disruptive energy storage technologies, it is of paramount importance to encourage the emergence of tools that can ease the inspiration by the current and the future generations of battery scientists.…”

Section: Energy Storage and Virtual Realitymentioning

confidence: 99%

“…Instead, the most used traditional way providing virtual experience in the battery field is computational modeling. Since more than 50 years computational models have revealed to be useful simulation tools to understand battery operation principles and to carry out their design and optimization [11,33] . Such models are based on mathematical equations describing physicochemical mechanisms which are solved by using informatics programs.…”

Section: Energy Storage and Virtual Realitymentioning

confidence: 99%

“…Therefore, the models constitute virtual materials or batteries with which one can perform virtual experiments, such as investigating how operation conditions ( e. g ., applied current density) impact outcomes such as the capacity or the aging. Numerous models have been proposed under several geometric representations of the cells and the electrodes within, in one dimension (1D), two dimensions (2D) or three dimensions (3D) [11] . However, the efficient development and use of these models require some programming skills.…”

Section: Energy Storage and Virtual Realitymentioning

confidence: 99%

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Entering the Augmented Era: Immersive and Interactive Virtual Reality for Battery Education and Research**

Franco

Chotard

Loup-Escande

et al. 2020

Batteries & Supercaps

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We present a series of innovative serious games we develop since four years using Virtual Reality (VR) technology to teach battery concepts at the University (from undergraduate to doctorate levels) and also to the general public in the context of science festivals and other events. These serious games allow interacting with battery materials, electrodes and cells in an immersive way. They allow experiencing impossible situations in real life, such as building with hands battery active material crystal structures at the nanometer scale, flying inside battery composite electrodes to calculate their geometrical tortuosities at the micrometer scale, experiencing the electrochemical behavior of different battery types by driving an electric vehicle and interacting with a virtual smart electrical grid impacted by 3D‐printed devices operated from the real world. Such serious games embed mathematical models with different levels of complexity representing the physical processes at different scales. We describe the technical characteristics of our VR serious games and their teaching goals, and we provide some discussion about their impact on the motivation, engagement and learning following four years of experimentation with them. Finally, we discuss why our VR serious games have also the potential to pave the way towards an augmented era in the battery field by supporting the R&D activities carried out by scientists and engineers.

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“…Simulation analysis relies on an advanced continuum Li-ion battery modeling framework [15] featuring sequential multi-scale model linking [16]. This is achieved by thermodynamically consistent upscaling [17] of an advanced and detailed model of LFP particle potential as a function of lithiation determined by a thermodynamically consistent derivation [18].…”

Section: Simulation Modelmentioning

confidence: 99%

Revealing the Thermodynamic Background of the Memory Effect in Phase Separating Cathode Materials

Zelič

Mele

Pačnik

et al. 2019

SV-JME

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Phase separating Li-ion battery cell cathode materials feature a well-known phenomenon called the memory effect. It manifests itself as an abnormal change in working voltage being dependent on cell cycling history. It was only recently that plausible mechanistic reasoning of the memory effect in Li-ion batteries was proposed. However, the existing literature does still not consistently reveal a phenomenological background for the onset or absence of the memory effect. This paper provides strong experimental and theoretical evidence of the memory effect in phase separating Li-ion battery cathode materials. Specifically, the background leading to the onset or absence of the memory effect and the underlying causal chain of phenomena from the collective particle-by-particle intra-electrode phenomena to macroscopic voltage output of the battery are presented and discussed. The results, clearly reveal that no memory effect is observed and predicted for low cut off voltages, whereas the absence of the first rest in memory writing cycle does not result in the absence of the memory effect, as previously believed. In addition, excellent agreement between the simulated and measured results is shown which, on one hand confirms the credibility of the combined analyses and, on the other, provides clear causal relations from macroscopic experimental parameters to simulated phenomena on the particle level.

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Boosting Rechargeable Batteries R&D by Multiscale Modeling: Myth or Reality?

Cited by 251 publications

References 411 publications

Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies

Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies

Entering the Augmented Era: Immersive and Interactive Virtual Reality for Battery Education and Research**

Revealing the Thermodynamic Background of the Memory Effect in Phase Separating Cathode Materials

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