## 1 Introduction
Instead, the car following theory at the micro level is suitable for the description of car following behavior in the unsteady state, in which rules concerning how a driver follows his immediate leading vehicle are established. The car following theory attempts to describe driving behavior in detail at the micro level and is directly related to the driver's safety, providing a solution to overcome the defects of the macro traffic flow [37; 38; 39; 40; 41]. Many classic car following models have been proposed, including the intelligent driver model (IDM), the Gazis-Herman-Rothery (GHR) model, the full velocity difference (FVD) model, etc., which have been widely explored and have made great contributions to traffic theory. Although macroscopic models (i.e., continuum methods) excel at large-scale traffic simulation with aggregated behavior, agent-based traffic simulations (i.e., microscopic models) excel at the modeling of individual vehicles with inhomogeneous governing behaviors, so [PERSON] et al. [42] combined these two types of approaches and proposed a hybrid method to take advantage of these complementary features. Their approach simulates traffic in the areas of interest using an agent-based model, while the rest areas use a continuum model. On adjoining regions of a road network, vehicles passing from one regime to another must be converted to the representation used in the destination regime. They introduce flux capacitors to convert continuum flow to discrete agents, and use car averaging to handle discrete vehicles that flow into continuum regions. Hybrid models can combine mesoscopic models with microscopic or macroscopic models, as well as mixed-class and multi-class models. More valuable results can be obtained by combining ideas from different branches, as well as combining the advantages of different types of model [43]. Micro traffic models represented by car following models can be applied to the development of a vehicle's autonomous intelligent cruise control system (AICCS) [44] and autonomous cruise control system (ACCS) [45; 46], etc., to ensure more efficient and stable movement of connected automatic control vehicles. However, to the best of our knowledge, few studies have considered the collaborative optimization control of the physical process, from the macro large-scale traffic system to the vehicle level of the micro subsystem. The purpose of this paper is to propose a CHAIN concept energy management strategy for hybrid electric vehicles under large scale macroscopic boundary conditions, which enables seamless and smooth combination from macro to micro. A global energy planning method from the perspective of multi-scale mixed traffic management is proposed in this paper, where a macro traffic flow model is adopted at a large time scale and a corresponding car following model is used at a micro scale. In addition, energy trajectory replanning is carried out, when the expected driving behavior details at the micro scale change due to increase of uncertainty or information entropy. This novel method is suitable for system management from the macro traffic scenario to micro mechanism levels, taking the intelligent transportation system as the object, e.g., global energy management in the full temporal-spatial domain for plug-in hybrid electric vehicles.