Integrated Speed Planning and Energy Management Optimization for Hybrid Electric Buses in Car-Following Scenarios

The enhancing driving economy is a crucial pathway for promoting the development of electrified vehicles, wherein speed planning and energy flow management are two major ways to reduce energy consumption. Different from conventional solutions, this study proposes a coherent integrated methodology for simultaneously regulating the longitudinal speed and powertrain statues based on an improved model predictive control (MPC) method, aiming at further improving the optimization potential of the electrified vehicles. Sequential quadratic programming is integrated into the MPC architecture, and dynamic programming is utilized for generating optimized control sequences. The proposed method is applied to develop a coherent speed planning and energy management strategy for hybrid electric buses in diverse car-following scenarios with vehicle-to-vehicle communication contexts. Multiple objectives including energy economy, driving comfort, and safety are considered as the multiple optimization objectives. Simulation results suggest the proposed strategy achieved multi-objective optimization performance, achieving 98% energy economy compared to the offline global optimal strategy while exhibiting satisfactory real-time implementation ability.