The electrification of transportation is reshaping the automotive and logistics industries, with electric vehicles (EVs) playing an increasingly central role in passenger mobility and commercial operations. As EV adoption grows, the need for accurate range estimation becomes critical, particularly under varying operational conditions such as increased payload. A key business challenge lies in the significant variability of EV range due to changes in vehicle load, which can affect performance, operational efficiency, and cost-effectiveness—especially for fleet-based services.
This study focuses on addressing the technical gap in predicting EV range with respect to different payload conditions. Traditional range estimation methods often fail to account for real-world variables like additional cargo weight, leading to suboptimal route planning, increased energy consumption, and unanticipated charging requirements. Payload-induced range degradation can result in up to some deviation in estimated range, negatively impacting logistics efficiency and increasing total cost of ownership.
The objective of this work is to develop a robust, simulation-based framework to estimate EV range under both normal and increased payload scenarios, thereby enhancing prediction accuracy and informing data-driven operational decisions.
A comprehensive vehicle simulation tool was employed to model EV performance across varying load conditions. The model integrates key parameters such as road gradients, driving cycles, vehicle mass, regenerative braking, and battery dynamics. Simulations were conducted for two primary scenarios: a nominal load representing typical usage, and an increased payload representative of commercial and logistics applications.
The results revealed that increased payload leads to an average reduction in driving range, with more pronounced effects under urban driving conditions due to frequent acceleration and braking. Additionally, energy consumption per kilometer increased significantly, underscoring the need for precise range prediction to avoid operational disruptions.
This simulation-based approach delivers tangible business value by enabling optimized vehicle deployment, reducing charging frequency, and supporting efficient route planning. The modular design of the simulation framework allows for scalable application across diverse EV platforms and use cases. Future enhancements include integration with live telemetry systems for real-time range prediction, enabling smarter energy management and improved fleet reliability.