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Hybrid Deep Learning Model Proposed for Electric Vehicle Range Prediction
Researchers from Saudi Arabia, India and Ethiopia unveiled a new architecture combining Hilbert-Huang transform feature extraction, path-informed adaptive modeling and chaotic optimization that achieved 99 percent validation accuracy on two EV datasets. The paper, received in December 2025 and accepted in January 2026, was published on 08 May 2026.
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Com reported. The adequate forecast of the electric vehicle (EV) range is the key to the success of the improved energy management and the improved battery utilization, the abstract states. Existing methods have struggled to capture the nonlinear, dynamic and non-stationary characteristics of EV operation and driving data, resulting in limited prediction accuracy and applicability across vehicle classes and driving scenarios.
The authors proposed a novel hybrid deep learning architecture to address these challenges through high-level feature extraction, adaptive sequence modeling and attention weight optimization. The framework consists of three components: the Hilbert-Huang Auto Recurrence Transform (HART), the Path-Informed Adaptive Inverted Trend Hub Range Analyser (PAITHRA) and the Chaotic Algae Sparrow Hyper-tuner (CASH).
HART performs rich temporal and recurrent features extractions.
PAITHRA is a combination of predictive model using pathformer and adaptive routing of features. CASH is a dynamic attention weight optimizer.
It was tested on the EV Energy Consumption Dataset and the Full Electric Vehicle Dataset 2024, which contain different driving styles, battery conditions, and weather conditions. 991. It required 35 minutes total for all epochs up to convergence and maintained an inference time of 8 ms per sample.
The proposed model is superior to conventional deep learning models. The paper's keywords are: Electric vehicles (EVs), Driving range prediction, Deep learning, Energy consumption modeling, Feature extraction, And optimization. Authors are Belqasem Aljafari, Thanikanti Sudhakar Babu, and Shitharth Selvarajan.
