Akademik Veri Yönetim Sistemi
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH, cilt.74, 2026 (SCI-Expanded, Scopus)
Accurate state-of-charge (SOC) estimation is a key requirement for the safe and efficient management of lithium-ion batteries in electric vehicles, especially under varying thermal and dynamic operating conditions. This study presents a comprehensive, algorithm-oriented assessment of several deep learning and hybrid SOC estimation architectures-including feedforward neural networks (FNN), gated recurrent networks (GRU), long short-term memory networks (LSTM), temporal convolutional networks (TCN), and their hybrid combinations-using a multi-temperature dataset collected at 10 degrees C, 25 degrees C, and 40 degrees C under diverse dynamic load profiles and standardized drive cycles such as UDDS, HWFET, US06, and LA92. All architectures were trained and evaluated under a unified preprocessing and training configuration to ensure methodological consistency and a fair basis for comparison. The evaluation highlights how different recurrent, convolutional, and hybrid architectures respond to thermal variations and dynamic load transitions, revealing model-specific strengths and limitations under realistic operating conditions. Among the evaluated models, the hybrid FNN + GRU architecture demonstrated the most reliable overall performance, achieving an RMSE of 1.11 % and reducing peak estimation errors to 3.6 % under nominal temperature conditions. SOC-zone analysis further showed characteristic error amplification at low and high SOC levels, emphasizing the importance of architectures capable of capturing nonlinear boundary dynamics. Computational benchmarking indicated that hybrid structures-particularly FNN + GRU-also provide an advantageous balance between estimation accuracy and inference speed, supporting their suitability for embedded Battery Management Systems (BMSs) with real-time constraints. Overall, this study contributes a unified evaluation framework that simultaneously addresses thermal robustness, dynamic load variability, SOC-dependent behavior, and computational efficiency, offering practical guidance for selecting reliable and deployable SOC estimation models for next-generation electric vehicle BMSs.