Ensemble Projected Gated Recurrent Unites For State Of Charge Estimation: A Case Study On Lithium-Ion Batteries in Electric Vehicles

State of Charge (SoC) estimation is important for improving performance and longevity of lithium-ion batteries in electric vehicles (EVs). Traditional methods such as voltage measurements and Coulomb counting lie in the inability to account for factors like battery aging and operational conditions variations, leading to potential errors in SoC estimation. Accordingly, this work overcomes these limitations by utilizing Ensemble Projected Gated Recurrent Units (E-PGRUs) for enhancing SoC estimation. Traditional methods often struggle with the non-linear dynamics and transient behaviors of battery systems, leading to suboptimal predictions. The proposed E-PGRU model leverages the adaptability of GRU, which efficiently handles time-series data, while employing an ensemble strategy to mitigate the risks of overfitting and improve generalization. In our methodology, we employed a publically available dataset specifically dedicated to the particular topic of real-world EV operations involving driving cycles and capturing varying operating conditions. E-PGRU architecture consists of multiple GRU networks, with projected layers features, each trained on different subsets of the data, and their outputs are aggregated to produce a more reliable SoC estimate. This ensemble technique targets specifically variability in prediction (i.e., standard deviation minimization), increasing prediction confidence and allowing the model to learn complex patterns in the battery's operational behavior. While, the experiments of this work are ongoing, it is expected to reach higher coefficient of determination, providing an explanation of the variance in dependent variable by independent variables in SoC estimation model. The expected result will demonstrate improvements in prediction performance compared to baseline models of recurrent neural networks in both coefficient of determination (i.e., due to ensemble learning) and computational time (i.e., due to projection layers) indicating a strong alignment with SoC values. Furthermore, E-PGRU expected to show superior adaptability to different usage scenarios and conditions, suggesting its potential for application in battery management systems.