Ensembled Traffic-Aware Transformer-Based Predictive Energy Management for Electrified Vehicles

The predictive energy management strategy (PEMS) offers potential advantages in enhancing the driving economy of electrified vehicles using vehicle speed prediction. However, realizing accurate predictions in practical contexts remains a challenge. Departing from conventional PEMS that rely on historical speed or static traffic data, we introduce a real-time traffic-aware PEMS for improved performance. To better understand the interplay between the host vehicle and its surrounding traffic, we use a Transformer network as the predictor that employs the speeds and relative distances of the surrounding six vehicles to forecast future speed sequences for the host vehicle. To augment this data-driven approach, we develop a dual-predictor strategy based on the deep ensemble technique. This strategy measures the Transformer's output uncertainty to gauge prediction reliability and introduce an automated threshold mechanism. Based on this threshold and real-time uncertainties, the strategy chooses between the Transformer and an exponential predictor to achieve improved prediction outcomes. A reinforcement learning method is integrated as the PEMS optimizer. For validation, we generate training data with traffic information based on the next generation simulation (NGSIM) dataset and create a test scenario in the SUMO simulator. The results confirm that speed predictions based on real-time traffic data surpass traditional PEMS, either directly inputting traffic data or excluding it. The Transformer predictor significantly outperforms the state-of-the-art predictor. Importantly, our dual-predictor design amplifies prediction accuracy by 27.2% against the standard single-network predictor under non-training conditions. Overall, our PEMS enhances driving economy by 11.1% relative to traffic-unaware models and 8.0% over non-Transformer schemes.