Co-optimization of speed planning and energy management for connected plug-in hybrid electric vehicles on slippery roads

With the development of Vehicle-to-Everything (V2X) technology, optimizing speed planning and energy management for Connected Plug-in Hybrid Electric Vehicles (CPHEVs) under complex conditions has become crucial. However, existing studies primarily focus on car-following scenarios with a single leading vehicle under ideal road conditions, neglecting the increase in vehicle stability and control risks on slippery roads. To address this gap, this study proposes an Improved Variable Time Headway (IVTH) strategy, integrated into a Model Predictive Control (MPC)-based optimization framework (IVTH-MPC) for multi-lead vehicle following scenarios on slippery roads. The proposed strategy enhances traditional VTH by incorporating relative speed, position, and acceleration differences among multi-lead vehicles, improving dynamic perception and prediction capabilities. Additionally, the IVTH-MPC strategy integrates adhesion coefficient constraints into the MPC optimization, achieving coordinated optimization of safety, comfort, and economy. The results show that compared to the traditional VTH-MPC, the IVTH-MPC reduces the maximum, minimum, and average absolute acceleration by 26.67 %, 6.67 %, and 16.90 %, respectively, thereby lowering the risk of instability and improving ride comfort. Moreover, the IVTH-MPC can respond (in braking or accelerating) 1–2 s in advance of the dynamic changes of the leading vehicles, while improving fuel economy with a 7.91 % reduction in total cost. Furthermore, adaptability is evaluated to verify the effectiveness of IVTH-MPC under different driving conditions.