Technical Paper
A data driven approach for real-world vehicle energy consumption prediction
2024-04-09
2024-01-2870
Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing fuel efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording vehicle data using data loggers to extract information from the CAN bus systems for ICE, hybrid electric, and hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.