Optimized GRU‐Based Voltage Fault Prediction Method for Lithium‐Ion Battery Packs in Real‐Life
Year: 2024
Type: article
Abstract: ABSTRACT Various failures of lithium‐ion batteries threaten the safety and performance of the battery system. Due to the insignificant anomalies and the nonlinear time‐varying properties of the cell, current methods for identifying the diverse faults in battery packs suffer from low accuracy and an inability to precisely determine the type of fault, a method has been proposed that utilizes the Random Forest algorithm (RF) to select key factors influencing voltage, optimizes model parameters through an Improved Dung Beetle Optimization algorithm (IDBO), employs a Gated Recurrent Unit (GRU) integrated with a channel and time attention mechanism (CTAM) for voltage fault prediction, and the consistency of the voltage is measured by quantifying the predicted voltage curve based on the curve Manhattan distance, a hybrid model for predicting voltage faults in lithium‐ion battery packs has been constructed, ultimately identifying faults such as overvoltage, undervoltage, and inconsistency of the battery pack. The experimental results show that the hybrid model proposed in this study outperforms the state‐of‐the‐art techniques such as informer and transformer in voltage fault prediction by achieving MAE, MSE, and MAPE metrics of 0.009272%, 0.000222%, and 0.246%, respectively, and maintains high efficiency in terms of the number of parameters and runtime.
DOI: 10.1002/qre.3708
Institution: Inner Mongolia University of Science and Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Awards: G3564078677, G4253438616
OpenAlex ID: W4405531122