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A Holistic Review of Deep Learning Methodologies for State Estimation in Lithium-Ion EV Batteries | ||
| AUT Journal of Electrical Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 19 اسفند 1404 اصل مقاله (2.27 M) | ||
| نوع مقاله: Review Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2026.24523.5752 | ||
| نویسنده | ||
| SH Suresh Kumar Budi* | ||
| Assistant Professor, CMR Technical Campus, Kandlakoya, Medchal, 501401, Telangana, India | ||
| چکیده | ||
| Accurate estimation of battery parameters, particularly State of Charge (SOC) and State of Health (SOH), is critical for the operational reliability and safety of electric vehicles (EVs). These parameters influence driving range, charging strategy, and long-term battery lifespan. Traditional methods such as Coulomb counting, equivalent circuit models, and Kalman filters have been standard for battery state estimation but struggle with noisy data, variable loads, and nonlinear battery ageing. Recently, deep learning has shown promise in addressing these challenges by offering more robust and adaptive performance. A recent review proposes a 4C framework—Correctness, Compute, Calibration, and Compliance—to evaluate deep learning models for next-generation Battery Management Systems (BMS). This scheme prioritises practical deployment aspects alongside accuracy. The review covers over 60 studies from 2019 to 2024, assessing model architectures, input features, training methods, and deployment readiness. It highlights advances such as physics-informed and uncertainty-aware models and offers a comparative evaluation of accuracy and computational efficiency on public datasets. Deep learning methods consistently outperform traditional approaches, achieving SOC errors below 2% and SOH deviations within ±3%. Transformer-based and hybrid models improve accuracy by 10–20% compared to simpler recurrent models. Lightweight architectures like GRUs offer fast inference (less than 20 milliseconds), suitable for in-vehicle real-time applications. Despite promising results, challenges remain around data generalizability, explainability, and real-time deployment. The 4C framework offers a roadmap for bridging laboratory advances with reliable, production-ready BMS technologies. | ||
| کلیدواژهها | ||
| Electric Vehicles؛ State of Charge and State of Health؛ Deep Learning؛ Physics-Informed Neural Networks؛ Edge AI | ||
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آمار تعداد مشاهده مقاله: 2 تعداد دریافت فایل اصل مقاله: 1 |
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