Fault detection and isolation in batteries power electronics and chargers

Alavi, Seyed Mohammad Mahdi; Fekriasl, Sajjad; Niyakan, Seyed Nami; Saif, Mehrdad

doi:10.1016/j.est.2019.100807

Cited by 17 publications

(5 citation statements)

References 33 publications

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“…Furthermore, soft SC quantitative analysis is developed with cell difference model and state estimation algorithm in [21]. In [22] and LFP (LiFePO 4 )) and configurations is a real challenge. An analytical model is usually adopted to develop control methods or/and monitoring techniques.…”

Section: Introductionmentioning

confidence: 99%

Incipient short-circuit fault diagnosis of lithium-ion batteries

Meng

Boukhnifer

Delpha

et al. 2020

Journal of Energy Storage

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Section: Introductionmentioning

confidence: 99%

Incipient short-circuit fault diagnosis of lithium-ion batteries

Meng

Boukhnifer

Delpha

et al. 2020

Journal of Energy Storage

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“…As fault location or fault isolation is a recurring aspect with fault detection, e.g., [90], meaning the identification of the faulty cell within the whole system, this is evaluated as well. Obviously, the methods on cell level provide such functionality while the systembased sensors, e.g., pressure, cannot.…”

Section: Approaches For Early Fault Detectionmentioning

confidence: 99%

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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“…The design to find out the fault source and provide an estimation of isolation fault can realize the simultaneous fault isolation as well as estimation of the battery [11]. In another case, as shown by the simulation results, it was stated that the Learning Observers method was effective in detecting the faults errors, but there were still some weaknesses in the decision-making process [12][13] [14]. In addition, another very important part of fault detection and isolation is battery modeling to facilitate the understanding of battery characteristics [15].…”

Section: Introductionmentioning

confidence: 98%

Battery Insulation Performance Analysis in Electric Vehicles for the Improvement of Battery Lifetime

Amifia¹,

Bismoko²,

Daely

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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The battery is the main component both as an energy provider and as an interface for several systems in an electric vehicle. It has three important parameters: current, voltage, and temperature that must be maintained as the battery can have a harmful reaction that can lead to overcurrent. The battery must also not overcharging or discharging for too long because it can cause damage and affect its lifetime. Another error that can arise is sensor failure due to the interference or noise that can cause an error in data reading. To prevent this problem, it needs protection by means of isolation in operating the battery. In this research, planning in optimizing battery work was conducted by designing the process of detection and isolation of faults that occurred in batteries, particularly lithium polymer battery to reach their more optimal and good performance. Battery modeling was needed as the parameter identification, and the Kalman Filter algorithm was applied to help to reduce the detection rate and fault isolation. The results of detection and isolation of overcurrent and sensor failure using Kalman Filter were found quite accurate. In overcurrent isolation, a discharge current of 6A was obtained from the maximum current limit of 10 A, and for sensor failure isolation, the Kalman Filter algorithm succeeded in improving the results of the previous reading.

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Fault detection and isolation in batteries power electronics and chargers

Cited by 17 publications

References 33 publications

Incipient short-circuit fault diagnosis of lithium-ion batteries

Incipient short-circuit fault diagnosis of lithium-ion batteries

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

Battery Insulation Performance Analysis in Electric Vehicles for the Improvement of Battery Lifetime

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