Based on Pure Electric Vehicle Dynamics Simulation and Economic Cycle Power Consumption Factor Analysis
Year: 2025
Type: article
Abstract: Studying the energy consumption of electric vehicles (EVs) is critical due to its wide-ranging impacts. For users, lower energy consumption reduces charging costs, improving EV affordability. Environmentally, it minimizes carbon emissions from power generation, supporting sustainable development. For manufacturers, understanding energy consumption is essential for optimizing technologies and boosting product competitiveness. Accurate analysis of energy consumption is thus vital for advancing the EV industry.Using AVL Cruise’s simulation capabilities, this study analyzed a pure electric vehicle model with a maximum acceleration of 14 m/s², a 0–100 km/h acceleration time of 9 seconds, and stable gradeability above 99% at speeds over 2 km/h. The research explored relationships between energy consumption and factors like vehicle mass, battery configuration (series- and parallel-connected batteries), aerodynamic drag, and transmission ratio. Through data analysis and model fitting, mathematical equations were derived, offering deeper insights into EV dynamics.Additionally, MATLAB was employed for rigorous analysis of simulation data on "energy consumption influencing factors" and "energy." Residual analysis refined the models, ensuring accuracy and reliability. This data-driven approach significantly enhanced understanding of EV energy consumption.The societal benefits of this research are substantial. Optimizing energy consumption enables EVs to meet diverse transportation needs, improve commuting efficiency, and support public transportation electrification. It also enhances the sustainability of logistics and distribution networks, promoting environmentally friendly socio-economic operations. This study not only advances EV technology but also contributes to broader energy efficiency and sustainable development goals.
Author: Enji Shan
Institution: North China University of Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409778649