Thermal fault detection by changes in electrical behaviour in lithium-ion cells

Klink, Jacob; Grabow, Jens; Orazov, Nury; Benger, Ralf; Börger, Alexander; Tidblad, Annika Ahlberg; Wenzl, Heinz; Beck, Hans-Peter

doi:10.1016/j.jpowsour.2021.229572

Cited by 21 publications

(13 citation statements)

References 57 publications

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“…As influenced by the temperature the most, U 01 shows greater values than the approximately unaffected U 12 . This observation is in accordance to previously published experimental results on cell level (see [68]) and the above-described (Table 1) inversely proportional temperature dependency of cell impedance-as the impedance decreases, the internal over-voltage decreases as well.…”

Section: Development Of Thermal Runawaysupporting

confidence: 93%

“…The same results were achieved for the other tests, as summarised in Table A1 in Appendix A. Thus, the proof-of-concept described initially in [68] has been successfully transferred from cell level to module level, and the feasibility has been confirmed. As the achieved detection time is highly dependent on the chosen thresholds, the selection of these provides an approach to increase the pre-warning time.…”

Section: Approaches For Early Fault Detectionsupporting

confidence: 69%

“…This study aims to build on that work by rerunning a similar sensor setup on an automotive battery case in comparison to a model-based detection method from our previous work [68]. By this, the change of impedance should be incorporated as well.…”

Section: Temperaturementioning

confidence: 99%

“…To define a baseline for detection time without additional sensors, the model-based method, principally from our previous work [68], was employed. By modelling the battery behaviour under normal conditions, the cell-level voltage measurement can be set in contrast and potential deviations can be detected.…”

Section: Model-based Fault Detectionmentioning

confidence: 99%

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Comparison of Model-Based and Sensor-Based Detection of Thermal Runaway in Li-Ion Battery Modules for Automotive Application

et al. 2022

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In recent years, research on lithium–ion (Li-ion) battery safety and fault detection has become an important topic, providing a broad range of methods for evaluating the cell state based on voltage and temperature measurements. However, other measurement quantities and close-to-application test setups have only been sparsely considered, and there has been no comparison in between methods. In this work, the feasibility of a multi-sensor setup for the detection of Thermal Runaway failure of automotive-size Li-ion battery modules have been investigated in comparison to a model-based approach. For experimental validation, Thermal Runaway tests were conducted in a close-to-application configuration of module and battery case—triggered by external heating with two different heating rates. By two repetitions of each experiment, a high accordance of characteristics and results has been achieved and the signal feasibility for fault detection has been discussed. The model-based method, that had previously been published, recognised the thermal fault in the fastest way—significantly prior to the required 5 min pre-warning time. This requirement was also achieved with smoke and gas sensors in most test runs. Additional criteria for evaluating detection approaches besides detection time have been discussed to provide a good starting point for choosing a suitable approach that is dependent on application defined requirements, e.g., acceptable complexity.

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Section: Development Of Thermal Runawaysupporting

confidence: 93%

Section: Approaches For Early Fault Detectionsupporting

confidence: 69%

Section: Temperaturementioning

confidence: 99%

Section: Model-based Fault Detectionmentioning

confidence: 99%

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Comparison of Model-Based and Sensor-Based Detection of Thermal Runaway in Li-Ion Battery Modules for Automotive Application

et al. 2022

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“…Pressure sensors and gas detection are quite similar, since they are activated by gas emissions from the cells, whereas the pressure sensor is monitoring the forces between cells and how these are affected by cell swelling, and the strain gauge measures cell deformation and excessive swelling. There are also examples of more advanced detection principles based on acoustic signals, e.g., ultrasonic methods, and predictive models incorporated into the BMS, which use data from the battery, e.g., cell voltages, temperatures and currents, to detect inconsistencies [213,214]. Commercial systems based on these principles are appearing on the market.…”

Section: Safety Considerationsmentioning

confidence: 99%

Future Material Developments for Electric Vehicle Battery Cells Answering Growing Demands from an End-User Perspective

et al. 2021

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Nowadays, batteries for electric vehicles are expected to have a high energy density, allow fast charging and maintain long cycle life, while providing affordable traction, and complying with stringent safety and environmental standards. Extensive research on novel materials at cell level is hence needed for the continuous improvement of the batteries coupled towards achieving these requirements. This article firstly delves into future developments in electric vehicles from a technology perspective, and the perspective of changing end-user demands. After these end-user needs are defined, their translation into future battery requirements is described. A detailed review of expected material developments follows, to address these dynamic and changing needs. Developments on anodes, cathodes, electrolyte and cell level will be discussed. Finally, a special section will discuss the safety aspects with these increasing end-user demands and how to overcome these issues.

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Electrothermal Characterization and Modeling of Lithium-Ion Pouch Cells in Thermal Runaway

2022

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Thermal fault detection by changes in electrical behaviour in lithium-ion cells

Cited by 21 publications

References 57 publications

Comparison of Model-Based and Sensor-Based Detection of Thermal Runaway in Li-Ion Battery Modules for Automotive Application

Comparison of Model-Based and Sensor-Based Detection of Thermal Runaway in Li-Ion Battery Modules for Automotive Application

Future Material Developments for Electric Vehicle Battery Cells Answering Growing Demands from an End-User Perspective

Electrothermal Characterization and Modeling of Lithium-Ion Pouch Cells in Thermal Runaway

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