Amid growing concerns about climate change and efforts to reduce greenhouse gas emissions, electric vehicles (EVs) have become a central component in achieving sustainable transportation. Advances in technology and supportive policies have driven the rise of Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Fuel Cell Electric Vehicles (FCEVs), which offer lower emissions and improved energy efficiency. However, their success depends on accurate powertrain modeling and intelligent energy management. In this paper, we reviewed key modeling approaches for EV design, simulation, and control, covering individual components, such as batteries, fuel cells, motors, and converters, and full system-level energy-flow models. Furthermore, we examined energy management strategies, ranging from straightforward rule-based approaches to advanced, real-time optimization algorithms. These strategies are essential to improving driving range, enhancing system reliability, and extending the lifespan of energy storage components.
Emphasis is placed on balancing model accuracy with computational efficiency, especially for real-time control applications. We also highlight several pressing challenges in EV modeling, including incorporating thermal and aging effects, addressing uncertainties in battery behavior, and integrating renewable energy sources for vehicle charging. Additionally, we highlight the increasing importance of machine learning (ML) and hybrid modeling techniques in improving prediction accuracy and adaptive control. We employed suitable modeling frameworks for the development of EVs. Thereafter, we concluded by outlining future directions in EV modeling, including the need for more adaptive, scalable, and cross-domain simulation environments that reflect the complexity of real-world applications.
However, due to the high complexity of the modeling frameworks, a comprehensive quantitative comparison of the electrochemical, mathematical, electrical, and data-driven modeling approaches has not been well studied. This paper fills the gap by systematically comparing models in terms of accuracy, computational cost, and relevance to EV design and control.
Citation: Bassam Adel, Mohamed M. A. Hassan. Modeling techniques for electric vehicle powertrains: A comparative review and future insights[J]. AIMS Energy, 2025, 13(6): 1432-1462. doi: 10.3934/energy.2025054
Amid growing concerns about climate change and efforts to reduce greenhouse gas emissions, electric vehicles (EVs) have become a central component in achieving sustainable transportation. Advances in technology and supportive policies have driven the rise of Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Fuel Cell Electric Vehicles (FCEVs), which offer lower emissions and improved energy efficiency. However, their success depends on accurate powertrain modeling and intelligent energy management. In this paper, we reviewed key modeling approaches for EV design, simulation, and control, covering individual components, such as batteries, fuel cells, motors, and converters, and full system-level energy-flow models. Furthermore, we examined energy management strategies, ranging from straightforward rule-based approaches to advanced, real-time optimization algorithms. These strategies are essential to improving driving range, enhancing system reliability, and extending the lifespan of energy storage components.
Emphasis is placed on balancing model accuracy with computational efficiency, especially for real-time control applications. We also highlight several pressing challenges in EV modeling, including incorporating thermal and aging effects, addressing uncertainties in battery behavior, and integrating renewable energy sources for vehicle charging. Additionally, we highlight the increasing importance of machine learning (ML) and hybrid modeling techniques in improving prediction accuracy and adaptive control. We employed suitable modeling frameworks for the development of EVs. Thereafter, we concluded by outlining future directions in EV modeling, including the need for more adaptive, scalable, and cross-domain simulation environments that reflect the complexity of real-world applications.
However, due to the high complexity of the modeling frameworks, a comprehensive quantitative comparison of the electrochemical, mathematical, electrical, and data-driven modeling approaches has not been well studied. This paper fills the gap by systematically comparing models in terms of accuracy, computational cost, and relevance to EV design and control.
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Bassam Adel, Mohamed M. A. Hassan. Modeling techniques for electric vehicle powertrains: A comparative review and future insights[J]. AIMS Energy, 2025, 13(6): 1432-1462. doi: 10.3934/energy.2025054
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