Driving behavior-based state of charge estimation for electric vehicles under sensor-degraded conditions
Year: 2026
Type: article
Abstract: Accurate estimation of the State of Charge (SoC) is essential for reliable and energy-efficient operation of electric vehicles (EVs), particularly under dynamic driving conditions and sensor degradation. Traditional SoC estimation approaches often rely on battery-internal measurements and electrochemical models, which limit their transferability across heterogeneous vehicles and restrict applicability in large-scale fleet deployments. This study proposes a battery-agnostic SoC estimation framework that integrates Global False Nearest Neighbor (GFNN)-based temporal embedding with a Convolutional Long Short-Term Memory (Conv-LSTM) network using real-world driving-behavior inputs (velocity, acceleration, and distance). Unlike many existing methods that rely on laboratory drive cycles or empirically selected window lengths, the proposed approach determines the sliding-window length systematically and is trained and validated using heterogeneous real-world driving data from BluSmart Mobility EV fleets collected across campus, market, city, and ring routes. To enhance practical relevance, the model is further evaluated under missing-data and multi-level noise scenarios to emulate realistic sensor degradation. Across four test trips, the proposed method achieves approximately 99.96%, 98.89%, 99.3%, 99.23%, and 98.72% accuracies under normal, missing, and low, medium, and high noise conditions, respectively, demonstrating reliable SoC estimation under real-world operating conditions.
Source: Electric Power Systems Research
Authors: Anyuti Tiwary, Sukumar Mishra, Yashasvi Bansal
Institution: Indian Institute of Technology Delhi
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,810
OpenAlex ID: W7160647815