Synergy optimization of energy management strategy for extended-range electric vehicles incorporating road noise perception

Abstract

Extended-range electric vehicles (EREVs) experience a notable increase in noise, vibration, and harshness (NVH) during range extender operation. To address this challenge, a multi-objective optimization energy management strategy incorporating road noise perception is proposed. First, a road noise prediction model is established, comprising pavement identification and velocity prediction sub-models. Based on this, an innovative NVH-oriented multi-objective Pontryagin's minimum principle (N-PMP) control algorithm is developed to optimize both fuel economy and NVH performance. Furthermore, by leveraging road noise prediction results, an integrated model predictive control (MPC)-N-PMP strategy is introduced to achieve ultra-quiet operation through the optimization of control variables. Simulation results demonstrate that the MPC-N-PMP algorithm effectively reduces noise levels while meeting real-time computational requirements compared to the MPC-PMP approach. Specifically, an 8.56 % reduction in NVH levels is achieved with only a marginal 2.32 % increase in fuel consumption, substantially enhancing overall vehicle comfort. Finally, the strategy's feasibility is validated through hardware-in-the-loop (HIL) experiments, laying a strong foundation for the future implementation of intelligent and efficient quiet control strategies in automotive applications.