Practical Method for Estimating Energy Consumption Model of Electric Vehicles Using Real-World Driving Data

To enhance the environment-friendly performance of electric vehicles (EVs), maximizing energy efficiency remains a critical challenge. Achieving this objective requires highly accurate vehicle dynamics and energy models tailored to the target EV. This study proposes a cost-effective parameter estimation method based on real driving data, eliminating the need for conventional motor bench testing. By collecting motion and energy-related measurements directly from the vehicle during actual driving, the method formulates an optimization problem that minimizes the discrepancy between measured and calculated values to identify model parameters. The proposed approach enables parameter estimation on each driving occasion, allowing it to adapt dynamically to variations in vehicle characteristics and road conditions. As a result, the obtained model closely reflects the actual energy consumption, leading to a reduction in the root mean square error.