Hazardous scenarios identification for Li-ion secondary batteries

Bubbico, Roberto; Greco, Viviana; Menale, Carla

doi:10.1016/j.ssci.2018.04.024

Cited by 43 publications

(19 citation statements)

References 52 publications

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“…However, Li-ion batteries may experience a fast degradation or even become hazardous if operated out of the limits specified by the manufacturer [2]. Therefore, a battery management system (BMS) to monitor and control the state of the battery is required.…”

Section: Introductionmentioning

confidence: 99%

Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage

2020

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The state of charge (SOC) and state of health (SOH) are two crucial indicators needed for a proper and safe operation of the battery. Coulomb counting is one of the most adopted and straightforward methods to calculate the SOC. Although it can be implemented for all kinds of applications, its accuracy is strongly dependent on the operation conditions. In this work, the behavior of the batteries at different current and temperature conditions is analyzed in order to adjust the charge measurement according to the battery efficiency at the specific operating conditions. The open-circuit voltage (OCV) is used to reset the SOC estimation and prevent the error accumulation. Furthermore, the SOH is estimated by evaluating the accumulated charge between two different SOC using a recursive least squares (RLS) method. The SOC and SOH estimations are verified through an extensive test in which the battery is subjected to a dynamic load profile at different temperatures.

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Section: Introductionmentioning

confidence: 99%

Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage

2020

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“…Some accidents occurred when a dynamic crash occurred on the road, whereas some occurred after collision or damage to the automotive LIBs. Some of these events may result in the automotive LIBs malfunctioning or the toxic substances leaking from the LIBs, whereas some of these events may even contribute to thermal runaway in the battery modules, which has the potential to cause fires or explosions 5 . Hence, these events create safety risks to not only vehicles but also to drivers and the wider public.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these events may result in the automotive LIBs malfunctioning or the toxic substances leaking from the LIBs, whereas some of these events may even contribute to thermal runaway in the battery modules, which has the potential to cause fires or explosions. 5 Hence, these events create safety risks to not only vehicles but also to drivers and the wider public. Unfortunately, as the total number of EVs is steadily increasing, accidents of EVs and automotive LIBs are inevitable.…”

Section: Introductionmentioning

confidence: 99%

A fast‐validated computational model for cylindrical lithium‐ion batteries under multidirectional mechanical loading

Xia

Tang

2020

Int J Energy Res

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Summary In the current electric vehicle (EV) market, cylindrical lithium‐ion batteries (LIBs) have played an indispensable role due to their high capacity and stability. However, LIBs are generally recognized as fragile and may cause fires or explosions when broken. Upon this issue, most studies focus on revealing the detailed reaction and interior change in the LIB during using or abuse conditions. However, in this study, we would like to provide a highly accessible model for automotive cylindrical LIBs that is practical in engineering application and can reduce computational effort in the entire EV crash simulation. In order to achieve this goal, the LIB model is built and meshed as a whole and a specific anisotropic material model is set so as the model can apply to multidirectional mechanical loading. Considering various operating condition of LIBs, state of charge (SOC) dependence and dynamic effect is termed as factors worth noticing when establishing this model. Furthermore, to predict the electrochemical responses and failures of LIBs, two optional short‐circuit criteria are defined. The proposed modeling method provides a powerful tool for analyzing the safety of batteries and battery packs with less computational and analytic effort than previous approaches.

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“…Saxena et al [1] presented a comprehensive review of publicly reported e-cigarette fire and explosion incidents and identified the main causes. Bubbico et al [13] investigated various dangerous scenarios associated with systems having installed batteries and developed a failure modes and effects analysis (FMEA) for hazard identification.…”

Section: Introductionmentioning

confidence: 99%

Li-ion Battery Reliability – A Case Study of the Apple iPhone^®

et al. 2019

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Li-ion battery reliability has attracted significant research attention, but few studies have incorporated in-service performance and reliability data of batteries installed within a product. This paper investigates battery drain, unexpected shutdowns, and slow performance associated with Li-ion batteries, with a focus on Apple iPhones R as a case study. The actions taken by Apple in response to these performance issues are discussed, especially from the perspective of Li-ion battery degradation. It is shown that not all battery reliability issues can be attributed to normal battery degradation; battery manufacturing defects and high Crate applications are also shown to be contributors to battery reliability and performance issues, including those causing a low state of charge, a low state of health, and unexpected shutdowns. INDEX TERMS Li-ion battery, reliability, iPhone battery, chemical aging, manufacturing defects, Crates .

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Hazardous scenarios identification for Li-ion secondary batteries

Cited by 43 publications

References 52 publications

Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage

Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage

A fast‐validated computational model for cylindrical lithium‐ion batteries under multidirectional mechanical loading

Li-ion Battery Reliability – A Case Study of the Apple iPhone^®

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Hazardous scenarios identification for Li-ion secondary batteries

Cited by 43 publications

References 52 publications

Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage

Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage

A fast‐validated computational model for cylindrical lithium‐ion batteries under multidirectional mechanical loading

Li-ion Battery Reliability – A Case Study of the Apple iPhone®

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Li-ion Battery Reliability – A Case Study of the Apple iPhone^®