Energy Management Strategy for a Hybrid Commercial Vehicle Considering Road Gradient

Abstract

To improve the energy efficiency of hybrid commercial vehicles under varying road gradients, this study develops a road-gradient-aware hierarchical energy management framework. An interactive multiple-model Kalman filter (IMM-KF) is employed to estimate the current road gradient by fusing information from vehicle dynamics and onboard measurements. At the vehicle level, an MPC-based strategy uses future route-gradient information extracted from a pre-reconstructed road profile, rather than predicted by the IMM-KF, to plan an energy-efficient speed trajectory and reduce vehicle energy demand. At the powertrain level, a particle swarm optimization (PSO)-calibrated adaptive equivalent consumption minimization strategy (PA-ECMS) allocates the resulting power demand between the engine and motor, with the SOC-feedback PI parameters calibrated offline. The framework is evaluated in MATLAB R2024a/Simulink using standard driving-cycle and real-road data-based simulation scenarios. The IMM-KF achieves an RMSE of 0.04461° under constant-gradient conditions and 0.47587° under variable-gradient conditions. The upper-layer MPC reduces wheel-end energy consumption by 10.98% compared with PID-based speed control, while PA-ECMS reduces the comprehensive operating cost by 15.3% relative to A-ECMS under the prescribed CHTC-LT cycle. In the integrated real-road simulation, MPC + PA-ECMS reduces fuel consumption and comprehensive operating cost by approximately 4.0% and 4.7%, respectively, compared with PID + A-ECMS. These results demonstrate the effectiveness of coordinating road-gradient estimation, economic speed planning, and hybrid power allocation within the proposed hierarchical framework.