Eco-Driving Optimization of Hybrid Electric Vehicle based on OSQP and Model Predictive Control under Dynamic Traffic Scenario

This paper proposes a novel eco-driving strategy for hybrid electric vehicle (HEV) under dynamic traffic scenario based on operator splitting quadratic program (OSQP) and model predictive control (MPC). Based on the space-time phase and waiting queue length of the signal light, the prediction trajectory of HEV in upcoming traffic intersections is obtained using OSQP. Based on the obtained demand trajectory, Pontryagin’s minimum principle (PMP) is applied to solve the optimal control sequence of HEV in the prediction time domain under the MPC predictive control framework, and rolling optimization is carried out in the short time domain to improve the eco-driving performance of HEV in the multi-intersections. The influence of the spatial-temporal phase of multiple signal lights and the length of waiting queue on the HEV’s trajectory prediction algorithm is analyzed through simulation on different traffic scenarios, and the effectiveness and robustness of the proposed eco-driving strategy has been verified by comparison with the Equivalent consumption minimization strategy (ECMS).