The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety

Finegan, Donal P.; Zhu, Juner; Feng, Xuning; Keyser, Matt; Ulmefors, Marcus; Li, Wei; Bazant, Martin Z.; Cooper, Samuel J.

doi:10.1016/j.joule.2020.11.018

Cited by 166 publications

(63 citation statements)

References 62 publications

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“…53 There are also data-driven approaches that directly predict the safety envelope of battery packs in electric vehicles. 54 The coupling of these safety envelopes with performance envelopes would be an important contribution in battery performance and safety modeling. Current battery pack and cell state estimation literature is focused on state-of-health modeling, however, in the context of functional safety of electric aircraft, predicting the power capability and the risk of kneepoint or rapid performance loss is also equally important.…”

Section: Functionalmentioning

confidence: 99%

A review of safety considerations for batteries in aircraft with electric propulsion

2021

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Modern aircraft designs for “more electric” and “fully electric” aircraft have large battery packs ranging from tens of kWh for urban aviation to hundreds or thousands of kWh for commercial aviation. Such large battery packs require careful consideration of the safety concerns unique to aviation. The most pertinent safety concerns related to batteries can be categorized into two broad areas: exothermic heat related events (thermal issues) and partial or complete loss of safety–critical power supply (functional issues). Degradation during operation of a battery can contribute to capacity fade, increased internal resistance, power fade, and internal short circuits, which lead to the loss of or decrease in propulsive power. When batteries are the primary source of onboard power and energy, it is crucial to be able to estimate their state-of-health in terms of capacity and power capability. Internal short circuits and other sources of excessive heat generation can lead to high temperatures within the cells of a battery pack leading to safety concerns and thermal events. One of the biggest risk factors for batteries used in aviation is the potential for thermal runaway where temperatures reach the flashpoint of one of the cell components, eventually cascading over multiple cells leading to system-wide battery pack failure and a fire hazard. This article reviews the current understanding of the safety concerns related to batteries in the context of urban and regional electric aviation.

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Section: Functionalmentioning

confidence: 99%

A review of safety considerations for batteries in aircraft with electric propulsion

2021

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“…At present, the fault diagnosis methods of battery energy storage systems are mainly divided into battery model methods and data-driven methods. 7 , 8 The method based on battery model achieves fault diagnosis by comparing the predicted value of the model with the actual measured value of the battery. The premise is to establish a reliable and accurate battery model.…”

Section: Introductionmentioning

confidence: 99%

Data-model alliance network for the online multi-step thermal warning of energy storage system based on surface temperature diffusion

Dong

et al. 2022

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As an important type of energy storage, battery energy storage systems have been widely used. However, there are frequent cases of battery explosion due to high temperature. To address this issue, researches have been carried out either in the model-driven or the data-driven aspects to predict the temperature of the battery. In this paper, a two-node electrothermal model and a multi-scale long short-term memory network are established formulating a data-model alliance network (DMAN) for surface temperature diffusion. An improved adaptive boosting algorithm is then employed to enhance the bridge of the two models. Integrating a data-model alliance module (DMAM) and multi-step-ahead thermal warning network (MATWN), this DMAN provides an advanced online multi-step-ahead thermal warning structure to achieve early warning of temperature crossing. Experimental results verify the progressiveness of the proposed technique.

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“…For examples, fast charging scheme could be designed for different aging conditions to balance the charging speed and degradation rate. 8 , 9 , 10 Likewise, users can get early warnings of the battery failure, 11 , 12 determine whether a battery should be replaced, 13 , 14 and if the retired batteries could be adopted for the less-demanding applications to allow cascade utilization. 15 , 16 , 17 Most of these data-based applications, by nature, require a large amount of available battery aging data.…”

Section: Introductionmentioning

confidence: 99%

Recovering large-scale battery aging dataset with machine learning

Tang

Liu

et al. 2021

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The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety

Cited by 166 publications

References 62 publications

A review of safety considerations for batteries in aircraft with electric propulsion

A review of safety considerations for batteries in aircraft with electric propulsion

Data-model alliance network for the online multi-step thermal warning of energy storage system based on surface temperature diffusion

Recovering large-scale battery aging dataset with machine learning

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