Data-Driven Safety Envelope of Lithium-Ion Batteries for Electric Vehicles

Li, Wei; Zhu, Juner; Xia, Yong; Gorji, Maysam B.; Wierzbicki, Tomasz

doi:10.1016/j.joule.2019.07.026

Cited by 166 publications

(65 citation statements)

References 38 publications

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“…A reliable model can generate a safety envelope virtually, setting a safety boundary for battery systems. 74 TR models are currently used to guide the safety design of LIBs at both the cell 9 and system levels. 39 The research direction for the TR models include (1) improving the model accuracy and (2) reducing the computational load.…”

Section: Model-based Safety Design Of Battery Systemsmentioning

confidence: 99%

Mitigating Thermal Runaway of Lithium-Ion Batteries

Feng

Ren

et al. 2020

Joule

952

377

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This paper summarizes the mitigation strategies for the thermal runaway of lithium-ion batteries. The mitigation strategies function at the material level, cell level, and system level. A time-sequence map with states and flows that describe the evolution of the physical and/or chemical processes has been proposed to interpret the mechanisms, both at the cell level and at the system level. At the cell level, the time-sequence map helps clarify the relationship between thermal runaway and fire. At the system level, the time-sequence map depicts the relationship between the expected thermal runaway propagation and the undesired fire pathway. Mitigation strategies are fulfilled by cutting off a specific transformation flow between the states in the time sequence map. The abuse conditions that may trigger thermal runaway are also summarized for the complete protection of lithium-ion batteries. This perspective provides directions for guaranteeing the safety of lithium-ion batteries for electrical energy storage applications in the future.

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Section: Model-based Safety Design Of Battery Systemsmentioning

confidence: 99%

Mitigating Thermal Runaway of Lithium-Ion Batteries

Feng

Ren

et al. 2020

Joule

952

377

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show abstract

“…As we look forward, the pursuit of high energy density batteries will push the requirement of advanced binders to a new level. In this regard, the binder design should consider the following: i) reducing the binder content further to 3 wt% or even less of the total electrode mass without losing the mechanical strength and adhesion; ii) simplifying the synthesis procedure with aqueous binders being preferable toward to low cost and sustainability; iii) exploring multifunctional polymer binders, ideally to integrate all the necessary functionalities into one polymer; iv) revealing deep insights into polymer dispersion and degradation mechanism which are needed to better guide binder development, and v) benefiting from the fast-developing AI and machine learning technology, [192][193][194][195][196] which have already demonstrated their application in the battery community. It is expected to realize a fast screening of feasible polymeric binders based on calculation results.…”

Section: Conclusion and Future Perspectivementioning

confidence: 99%

A Review of the Design of Advanced Binders for High‐Performance Batteries

Zou

Manthiram

2020

Advanced Energy Materials

284

177

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Polymer binders as a critical component in rechargeable batteries provide the electrodes with interconnected structures and mechanical strength to maintain the electronic/ionic transfer during battery cycling. The conventional binders, such as polyvinylidene fluoride (PVDF), are not ideal candidates due to their relatively low adhesiveness, weak mechanical strength, and poor functionality for high‐energy‐density batteries with a thick electrode and/or a high‐capacity electrode. Nevertheless, the binder has not yet received the attention that reflects its importance in batteries. The requirements of high energy density batteries make essential the exploration of advanced polymeric binders, which possess particular functions. In this review, state‐of‐the‐art binder design strategies are sorted in terms of the challenges of various electrodes (anodes and cathodes for lithium ion batteries (LIBs) and sulfur cathodes for Li–S batteries), which have been commercialized or have the potential for practical cells. A deep insight into how the polymeric binders improve the cell performance and the design principle of new binders is also provided. Finally, a perspective on the direction of future binder development for high‐energy‐density batteries with long cycle life is presented.

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“…The machine learning methodology in this paper is not limited to fluid mechanics and can be easily transferred to other areas, e.g., in experimental solid mechanics, where a large number of specimens are required to quantify the modulus of elasticity, the yield stress, and the onset of fracture. Hence, combined with advanced manufacturing technologies (58)(59)(60) that are capable of generating versatile prototypes in a short amount of time, we foresee great potential for automatic sequential experimentation to map material and structural properties (61) to obtain understanding that may lead to new advances, such as developing the next generation of morphing wings for aviation (62). Similarly, this methodology is readily applicable to nondestructive evaluation of materials, where uncertainty quantification and automation will accelerate considerably such testing.…”

Section: Discussionmentioning

confidence: 99%

A robotic Intelligent Towing Tank for learning complex fluid-structure dynamics

et al. 2019

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We describe the development of the Intelligent Towing Tank, an automated experimental facility guided by active learning to conduct a sequence of vortex-induced vibration (VIV) experiments, wherein the parameters of each next experiment are selected by minimizing suitable acquisition functions of quantified uncertainties. This constitutes a potential paradigm shift in conducting experimental research, where robots, computers, and humans collaborate to accelerate discovery and to search expeditiously and effectively large parametric spaces that are impracticable with the traditional approach of sequential hypothesis testing and subsequent train-and-error execution. We describe how our research parallels efforts in other fields, providing an orders-of-magnitude reduction in the number of experiments required to explore and map the complex hydrodynamic mechanisms governing the fluid-elastic instabilities and resulting nonlinear VIV responses. We show the effectiveness of the methodology of “explore-and-exploit” in parametric spaces of high dimensions, which are intractable with traditional approaches of systematic parametric variation in experimentation. We envision that this active learning approach to experimental research can be used across disciplines and potentially lead to physical insights and a new generation of models in multi-input/multi-output nonlinear systems.

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Data-Driven Safety Envelope of Lithium-Ion Batteries for Electric Vehicles

Cited by 166 publications

References 38 publications

Mitigating Thermal Runaway of Lithium-Ion Batteries

Mitigating Thermal Runaway of Lithium-Ion Batteries

A Review of the Design of Advanced Binders for High‐Performance Batteries

A robotic Intelligent Towing Tank for learning complex fluid-structure dynamics

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