چکیده مقاله
The global shift toward electric vehicles EVs to reduce CO2 emissions underscores the critical role of Battery Management Systems BMS in ensuring the safe and efficient operation of lithium ion batteries LIBS Accurate State of Charge SOC estimation is essential for optimizing battery performance, extending lifespan, and enhancing EV safety This paper critically reviews SOC estimation methods for LIBS in EVs, categorizing them into direct, model based, observer based, filter based, data driven, and hybrid approaches Each method is evaluated for accuracy, computational complexity, noise robustness, and hardware implementation feasibility Key challenges, including nonlinear battery dynamics, temperature variations, aging effects, and computational constraints, are analyzed, highlighting trade offs between model fidelity and real time applicability The review also examines hardware platforms, such as microcontrollers, DSPs, FPGAs, and GPUs, and their suitability for SOC estimation in BMS Recent advancements, such as physics informed machine learning and uncertainty aware hybrid models, are discussed alongside future research directions, including standardization, cross chemistry generalization, and edge friendly implementations This study provides a comprehensive framework for developing robust, accurate, and real time SOC estimation strategies to support the widespread adoption of sustainable EV technologies
کلیدواژهها
نویسندگان
Sahar Firoozabadi
Department of Electrical Engineering, SR C , Islamic Azad University, Tehran, Iran
Siavash E'shaghi
Department of Electrical Engineering, SR C , Islamic Azad University, Tehran, Iran
Hamid Soleimanimehr
Department of Electrical Engineering, SR C , Islamic Azad University, Tehran, Iran
شیوه ارجاع
Firoozabadi, Sahar and E'shaghi, Siavash and Soleimanimehr, Hamid,1404,Critical Review of State of Charge Estimation Methods for Lithium-Ion Batteries in Electric Vehicle Battery Management Systems,2nd International Conference on Modern Power Trains,Tehran
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