Modeling and Comparative Analysis of the Impact of Driving Cycles on Battery State of Charge Performance and Electric Vehicle Driving Range
محل انتشار: مجله علم مهندسی خودرو، دوره: 15، شماره: 3
سال انتشار: 1404
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 11
فایل این مقاله در 15 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJAEIU-15-3_003
تاریخ نمایه سازی: 30 فروردین 1405
چکیده مقاله:
The escalating proliferation of electric vehicles (EVs) as a pivotal solution to address energy consumption and air pollution challenges within the transportation sector necessitates a comprehensive understanding of the factors influencing their performance and driving range. Among these factors, driving patterns exert a direct and significant impact on energy consumption and battery state. This study aims to quantify the influence of diverse driving cycles on the performance of an electric vehicle, specifically the Audi e-tron ۵۰. Utilizing Simcenter Amesim software, a longitudinal vehicle dynamics model, coupled with an equivalent circuit model (ECM) for the lithium-ion battery, was developed for simulation purposes. The vehicle's performance was evaluated under five distinct driving cycles, including global standards (WLTC, NEDC, HWFET) and two real-world driving cycles recorded in Tehran (Route۱, Route۲). Key parameters such as state of charge (SoC), depth of discharge (DoD), battery temperature, and estimated driving range were analyzed. The results revealed a significant impact of driving cycles on all investigated parameters. Driving cycles characterized by higher speeds and accelerations (e.g., WLTC and HWFET) led to increased specific energy consumption, accelerated temperature rise, and a notable reduction in estimated driving range (with the lowest range observed in WLTC). Conversely, milder urban driving cycles (particularly Route۱) resulted in improved energy efficiency, minimal thermal stress, and the highest estimated driving range. These findings underscore the critical importance of considering real-world and localized driving patterns for accurate performance evaluation, range estimation, and the development of optimized energy management strategies in electric vehicles.
کلیدواژه ها:
نویسندگان
Amir Ansari Laleh
Iran University of Science and Technology
mohammad hasan shojaeefard
Iran University of Science and Technology
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :