Abstract:
Electric vehicles (EVs) are gaining popularity thanks to advancements in green energy technology. However, a major concern for potential users is range anxiety—the fear that an EV might run out of power before reaching a destination. A key factor contributing to this concern is the impact of passenger weight on the vehicle's range. Extra weight from passengers increases energy consumption, particularly in stop-and-go traffic, leading to reduced driving range.
To address this issue, we developed a method to improve range prediction by considering the effect of passenger weight. Our approach uses real-world data to understand how passenger load impacts energy consumption, especially when battery levels and vehicle speed are constant. By fine-tuning the range prediction model based on passenger weight, we aim to reduce range anxiety while ensuring optimal vehicle performance.
This research offers a practical solution to range anxiety by providing more accurate range estimates, helping EV drivers feel more confident about their vehicle's capabilities. This improvement could encourage wider adoption of electric vehicles, benefiting both the environment and drivers.
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