Energy Consumption Prediction with Uncertainty Quantification for Electric Truck Operations: A Data-Driven Approach

The adoption of electric trucks in commercial applications is growing due the the adoption of zero-emission zones in large cities. However, the usage of these trucks shows challenges for fleet managers due to their limited range and uncertain energy usage. Accurately predicting the energy consumption of these vehicles is crucial for their optimal usage in commercial applications. This work introduces a novel energy consumption prediction method for electric trucks, based on a data-driven approach. The approach uses a two-stage Long Short-Term Memory (LSTM) architecture: the first stage predicts vehicle speed while the second predicts energy consumption. For the second stage, two updates to the LSTM encoder are proposed. The first improves the energy prediction by splitting the predictions into regenerated and consumed energy, whereas the second provides a score that quantifies the prediction uncertainty using Student’s t-distribution. Evaluating the approach using real-world truck-operation data shows that splitting the energy consumption into regenerative and consumed components contributes to a 20% reduction of error compared to a state-of-the-art LSTM model, mainly due to improved prediction accuracy for regenerated energy. Finally, the t-score demonstrates a 92% reduction of calibration error compared to a Gaussian equivalent. This ensures reliable application in the design of worst-case planning scenarios, decision thresholds, and probabilistic planning approaches.