A multi-layer predictive energy management strategy for intelligent hybrid electric trucks collaborated with eco-driving control

Abstract

Nowadays, hybrid electric trucks (HETs) are also urgently expected to further enhance energy efficiency with the development of intelligent driving system. A multi-layer predictive energy management strategy is designed for intelligent parallel HET to enhance fuel economy and computational efficiency. Depending on intelligently implementation of traffic prediction with navigation data, an online smart battery SOC generation is proposed to obtain an approximately global optimal SOC reference trajectory in the upper layer. In the bottom controller, a novel method, the tolerant sequential model predictive control (MPC) collaborated with sampling strategy, could achieve eco-driving in consideration of both driving comfort and safety with less computation time in autonomous driving scenario. Moreover, to simultaneously accomplish fuel economy and decrease computational burden, the integrated equivalent consumption minimization strategy with dynamic programming (ECMS-DP) solver for MPC scheme is presented to frozen SOC state and jointly control torque distribution and gear action. Numerical simulations demonstrate that the proposed multi-layer strategy has improved over 20 % fuel economy and decreased almost 50 % computational burden relative to traditional MPC benchmark strategy, which could exhibit great effectiveness and potential for practical implementation in real driving routes.