Developing a qualified eco-driving strategy for plug-in hybrid electric vehicles (PHEVs) remains challenging in urban traffic scenarios, due to the comprehensive influence of random traffic flow and signal lights. To solve it, this study develops a hierarchical eco-driving framework integrating lane-changing decisions through a virtual force-based trigger mechanism and an adaptive energy management strategy that dynamically adjusts the equivalence factor. Firstly, considering the interference of neighbor vehicles, the velocity planning layer generates the economic velocity trajectories using dynamic programming in short discrete intervals, enabling the ego-vehicle to navigate through signal intersections smoothly. In addition, the lane changing is properly conducted according to the state of the ego-vehicle, traffic flow, and signal light. In the energy management layer, an adaptive equivalent fuel consumption minimization strategy accounting for trip distance, initial state of charge, and remaining electric mileage is developed to ensure a reasonable power split based on the reference velocity. Simulation and hardware-in-the-loop experimental results indicate that the developed strategy improves the traffic efficiency by 1.68%, while reducing energy consumption by 9.81% and 31.71%, compared with Pontryagin's minimum principle and nonlinear model predictive control based methods.