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• W2198569023 abstract "In recent years much attention has been focused upon performance degradation for lithium-ion batteries due to safety issues. Although SOH diagnosis and remaining life predication for individual cell is relatively sophisticated, module diagnosis especially for electric vehicle(EV) has rarely been reported and applied due to complexity of system. This paper focused on the performance degradation of EV power module during practical conditions, and proposed a diagnosis approach based on wavelet transformation and statistical analysis. This approach provides not only the performance degradation information of entire module, but also the individual cell information at different frequency range, with which ageing process of module, potential failure of individual cell, location of worst cell and fault mechanism can be obtained to guarantee the safety of battery system and reduce maintenance costs." @default.
• W2198569023 created "2016-06-24" @default.
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• W2198569023 date "2015-10-01" @default.
• W2198569023 modified "2023-09-22" @default.
• W2198569023 title "Diagnosis of Performance Degradation for Lithium-Ion Battery Module in Electric Vehicle" @default.
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• W2198569023 doi "https://doi.org/10.1109/vppc.2015.7352929" @default.
• W2198569023 hasPublicationYear "2015" @default.
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