A vehicle-cloud collaborative method for multi-type fault diagnosis of lithium-ion batteries

Xu, Chaojie; Li, Laibao; Xu, Yuwen; Han, Xiao; Zheng, Yuejiu

doi:10.1016/j.etran.2022.100172

Cited by 38 publications

(8 citation statements)

References 43 publications

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“…Early prediction of battery thermal catastrophe and ignition can also be made using data-driven methods that characterize internal parameters and detect faults by analyzing each cell's real-time state. These methods can accurately identify faulty cells, detect problems early, and provide early warning of the risk of thermal disaster [32,42].…”

Section: Introductionmentioning

confidence: 99%

Self-Diagnostic Opportunities for Battery Systems in Electric and Hybrid Vehicles

Szürke,

Sütheö,

Őri

et al. 2024

Machines

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The number of battery systems is also growing significantly along with the rise in electric and hybrid car sales. Different vehicles use different types and numbers of batteries. Furthermore, the layout and operation of the control and protection electronics units may also differ. The research aims to develop an approach that can autonomously detect and localize the weakest cells. The method was validated by testing the battery systems of three different VW e-Golf electric vehicles. A wide-range discharge test was performed to examine the condition assessment and select the appropriate state of charge (SoC) for all three vehicles. On the one hand, the analysis investigated the cell voltage deviations from the average; the tests cover deviations of 0 mV, 12 mV, 60 mV, 120 mV, and 240 mV. On the other hand, the mean value calculation was used to filter out possible erroneous values. Another important aspect was examining the relationship between the state of charges (SoC) and the deviations. Therefore, the 10% step changes were tested to see which SoC level exhibited more significant voltage deviations. Based on the results, it was observed that there are differences between the cases, and the critical range is not necessarily at the lowest SoC level. Furthermore, the load rate (current) and time of its occurrence play an important role in the search for a faulty cell. An additional advantage of this approach is that the process currently being tested on the VW e-Golf can be relatively simply transferred to other types of vehicles. It can also be a very useful addition for autonomous vehicles, as it can self-test the cells in the system at low power consumption.

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Section: Introductionmentioning

confidence: 99%

Self-Diagnostic Opportunities for Battery Systems in Electric and Hybrid Vehicles

Szürke,

Sütheö,

Őri

et al. 2024

Machines

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“…Ma et al [31] used the contribution degree of principal component analysis to judge the abnormal data, and used kernel principal component analysis to reconstruct the battery parameters and compared them with the normal parameters to achieve fault classification. Xu et al [32] extracted the data features of the simulation model and used decision tree and cloud algorithm to judge the fault classification. The data-driven diagnosis method has the merits of strong adaptability, low cost of update learning, and high robustness.…”

Section: Introductionmentioning

confidence: 99%

Multi-Fault Diagnosis of Electric Vehicle Power Battery Based on Double Fault Window Location and Fast Classification

Shen,

Lun,

2024

Electronics

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As energy supply units, lithium-ion batteries have been widely used in the electric vehicle industry. However, the safety of lithium-ion batteries remains a significant factor limiting their development. To achieve rapid fault diagnosis of lithium-ion batteries, this paper presents a comprehensive fault diagnosis process. Firstly, an interleaved voltage sensor topology structure is utilized to acquire battery voltage data. An improved complete ensemble empirical mode decomposition with adaptive noise method is introduced to process data. Then, the reconstructed voltage data sequence is used to eliminate the influence of noise. A fault location is performed using dichotomy correlation coefficient and time window correlation coefficient. Afterwards, principal component analysis is used to select the principal components with high contribution rate as classification features. The gray wolf optimization algorithm is used to find the parameters of the least squares support vector machine, constructing an optimal classifier for fault classification. A fault experiment platform is established to realize the physical triggering of faults such as external short circuit, internal circuit, and connection of experimental battery packs. Finally, the accuracy and reliability of the method are verified by the results of fault localization and fault type determination.

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“…Although the actual cloud-based battery condition monitoring for energy storage systems has been less applied, the potential benets of advancing the BMS has wide acknowledgement. 16 Cyber-physical systems (CPS), which fuse real-time sensing data with advanced models, therefore, is seen as the future of BMSs with core elements including cloud data storage, intelligent analytics, advanced control algorithms, and data visualization. 10 Motivated by the potential of new digital solutions towards advancing BMSs this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Towards interactional management for power batteries of electric vehicles

Xie

et al. 2023

RSC Adv.

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A vehicle-cloud collaborative method for multi-type fault diagnosis of lithium-ion batteries

Cited by 38 publications

References 43 publications

Self-Diagnostic Opportunities for Battery Systems in Electric and Hybrid Vehicles

Self-Diagnostic Opportunities for Battery Systems in Electric and Hybrid Vehicles

Multi-Fault Diagnosis of Electric Vehicle Power Battery Based on Double Fault Window Location and Fast Classification

Towards interactional management for power batteries of electric vehicles

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