Hyperscale-Cascaded Transformer-Net-Based Framework for Remaining Useful Life Prediction of Electric Vehicle Batteries

The accurate prediction of remaining useful life (RUL) of electric vehicle (EV) batteries is a critical aspect of intelligent battery management systems. Effective RUL prediction not only ensures vehicle safety and reliability but also plays a pivotal role in optimizing charging cycles, reducing maintenance costs, and extending the overall battery lifespan. This work presents a comprehensive Deep Learning (DL) framework for predicting RUL of EV batteries, using a novel Hyperscale-Cascaded Transformer Net architecture designed to capture long-term dependencies and degradation patterns in battery behavior. The proposed system initiates with data acquisition, wherein parameters such as cycle index, voltage, current, and time-based features are collected. Raw data undergoes preprocessing, which includes data cleaning to eliminate outliers and handle missing values, followed by Exploratory Data Analysis (EDA) to extract meaningful patterns through descriptive statistics, distribution analysis, and correlation heatmaps. Subsequently, the data is passed through a feature engineering pipeline, where feature scaling using Min-Max normalization is applied to enhance learning efficiency of model. Processed dataset is then split into training and testing sets, maintaining data integrity for unbiased evaluation. The core of the model lies in Hyperscale-Cascaded Transformer Net, a DL model that utilizes cascaded transformer layers to model complex temporal relationships and nonlinear degradation behaviors inherent in battery performance over time. Experimental esults demonstrate that proposed Transformer-based model outperforms traditional Machine Learning (ML) techniques in terms of accuracy and robustness in revolutionizing EV battery management systems.