امين نجفي

This study focuses on enhancing EV performance at both the battery level (e.g., battery life, safe operating zones, energy losses) an‎d the vehicle level (milage), by limiting the battery pack based on its estimated SOP. A two-layer adaptive SOP estimation structure is proposed, incorporating a time-window prediction framework with future power-series feedback an‎d a multivariate polynomial estimation model using real-world driving micro-trip patterns. The best estimation method is selec‎ted based on accuracy an‎d computational time using the TOPSIS decision-making approach. Since SOP cannot be directly measured by the BMS, this research addresses the associated challenges by first modeling the battery’s dynamic behavior using a hybrid architecture (HABPE concept), then proposing a novel adaptive estimation strategy based on real driving pattern recognition (ASOP), an‎d utilizing micro-trip-based power estimation (FDPR). Polynomial-based estimation models are combined an‎d optimized with respect to estimation error an‎d computation time. Initially, an equivalent circuit model was developed using operational battery data without laboratory testing, achieving 85–94% accuracy in terminal voltage replication. By employing a hybrid static-dynamic architecture, accuracy of 87–90% was maintained while reducing processing time by 30–40%. The extracted model was then used for SOP estimation, yielding approximately 96% accuracy in validation tests. The adaptive SOP estimation, tailored to real-world driving conditions, achieved up to 30% improvement in functional accuracy compared to classical models, with a 20% reduction in computational load relative to more complex models. Finally, a GRU-RNN neural network was applied for time-series prediction of the driving cycle with approximately 90% accuracy. This predicted profile was used to apply power limitations in a forward dynamic model of the Mana electric bus. As a result, performance improvements include about 6% increase in driving range (considering the software-based nature of this work without hardware modifications, improvements below 10% are reasonable), 10–20% reduction in energy loss an‎d battery aging, an‎d up to 30% improvement in safety metrics such as cutoff voltage an‎d battery current profile.

تخمين سطح توان تطبيقي باتري , سيستم مديريت باتري , مدار معادل باتري , بهبود عملكرد خودروي برقي , باتري ليتيوم يون , رانندگي واقعي

Adaptive State of Power (ASOP) Estimation , Battery Management System (BMS) , Equivalent Circuit Model (ECM) , Hybrid Battery Parameter Estimation (HABPE) , Lithium-ion Battery , Fuzzy Driving Pattern Recognition (FDPR)

Amin Najafi

Dr. Masoud Masih Tehrani