An Online Rolling Optimal Control Strategy for Commuter Hybrid Electric Vehicles Based on Driving Condition Learning and Prediction

This paper presents an online supervisory control strategy for commuter hybrid electric vehicles (HEVs) based on driving condition learning and prediction. The aim is to provide an online self-learning-based framework to keep Pontryagin's minimum principle (PMP)-based control adapting to the time-varying driving condition on commuting routes and minimize fuel consumption. There are three steps to realizing this strategy. First, two novel statistical features are proposed to describe the frequency distribution of achievable working points of the hybrid powertrain under a driving condition. Second, based on the characteristic that commuting trips with similar trip start time, direction, and weather condition have similar driving conditions, we develop an instance-based machine learning algorithm to learn the driving condition. A k-nearest neighbor (k-NN) prediction algorithm is used to predict future driving conditions. Third, we establish an online supervisory control strategy, together with rolling driving condition prediction and optimal costate approximation. The approximation algorithm can approximate the optimal constant costate for the entire prediction horizon just based on the proposed two features. Simulation study and bench tests are conducted on a parallel hybrid powertrain of a city bus using standard driving cycles and real-world sampled commuting trips with different trip start times and weather conditions. The results show that the proposed driving condition learning and prediction method is effective for predicting the upcoming driving conditions. Meanwhile, the proposed strategy shows substantial improvement of fuel economy compared with the rule-based (RB) strategy and the adaptive equivalent consumption minimization strategy (A-ECMS).