As an important part of an advanced driving assistant system, vehicle autonomous queue driving can effectively control the speed based on the pre-set safety distance and improve the safety and passing rate during the driving [
1,
2,
3]. However, it requires longitudinal control technology for a smart car. Meanwhile, the HEV is the best solution to achieve “energy saving and emission reduction” under the current technical level. To achieve this goal, the control strategy of energy matching for HEV is essential. The rapid development of future vehicles will drive the combination between hybrid and intelligent assisted driving technology, which will eventually lead to the achievement of low fuel consumption, low emission, safety, and intelligence.
For longitudinal control of intelligent vehicle, Tang et al. applied an artificial potential field to plan the expected motion state of vehicles [
4,
5,
6]. Although the solution for conflict resolution was proposed, the factor of the passing rate was neglected. Lu et al. proposed a self-adaptive system for vehicle cruise control, and the dynamic model of a single lane vehicle was simulated under the hybrid traffic flow of smart cars [
7,
8,
9]. Wang et al. quantified the impacts on the behaviors of vehicles following in a string (or string stability) to establish potential performance enhancements of automated vehicle following systems from the aspects of the CACC algorithm, software system architecture, and module implementation [
10,
11,
12,
13]. The work mentioned in [
10,
11,
12,
13] improved the stability of traffic flow from the intelligent driving model and the collaborative adaptive cruise model. However, the energy optimization problem during the driving was ignored. Chu et al. proposed an energy efficient longitudinal driving strategy with a stop-and-go function to achieve full speed range driving [
14,
15,
16,
17]. The works in [
14,
15,
16,
17] optimized the energy saving under slow driving and intersection. For better results, more driving speed ranges should be analyzed. In general, the energy consumption of the car during longitudinal driving should be considered, especially in the energy optimization for the energy matching of HEV.
For passing rate and energy saving, Kitayama et al. used the radial basis function network (RBF) to optimize the variables in the torque control strategy of the internal combustion engine to improve the fuel economy for a parallel HEV [
18,
19]. Lin et al. proposed a framework of an adaptive control strategy for a series-parallel hybrid electric bus (SPHEB), based on the extracted hierarchical energy management strategy from dynamic programming (DP) and combined with driving pattern recognition (DPR) to improve the fuel economy [
20]. Xu et al. proposed a double fuzzy control strategy combined with the braking energy recovery strategy, which took SoC, required torque, and bus speed as the inputs, and the double fuzzy control strategy obtained better fuel economy than the single fuzzy logic control strategy in the Chinese Bus Driving Cycle [
21]. Jung et al. proposed a modified thermostatic control strategy for a parallel mild hybrid electric vehicle by operating internal combustion engine (ICE) in a high-efficiency region, and the vehicle’s fuel economy was improved by 3.7% compared with that of the conventional strategy under urban driving [
22]. Fu et al. proposed a parameter matching optimization method for hybrid electric vehicles based on multi-objective optimization (MOO), which used the weight coefficient method to transform the multi-objective optimization problem of fuel consumption and emissions into a single objective optimization problem to reduce significantly the fuel consumption and emissions of a vehicle simultaneously [
23]. In [
18,
19,
20,
21,
22,
23], different energy management strategies were proposed for different hybrid vehicles, and the reduction of fuel consumption and emissions was verified by simulation analysis. The fusion algorithm of fleet control and energy saving control should also be considered. Li et al. demonstrated the fuel saving limitation and periodic fuel saving mechanism for both traditional and hybrid vehicles with respect to driving economy [
24,
25,
26]. For a bus with an internal combustion engine and variable speed unit, the optimal fuel consumption was analyzed under impulse, sliding, and constant speed. The work in [
26] proposed three optimal strategies for vehicle cruising, which were impulse speed and sliding, load state pulse and sliding, and constant speed cruise, respectively. However, the energy optimization matching of HEV during cruise was ignored. Zlocki et al. analyzed the improvement of potential fuel economy for hybrid vehicles from both theoretical calculation and experimental verification under ACC driving [
27,
28]. A methodology was proposed to quantify the fuel reduction potential for different driving strategies in ACC relevant driving scenarios. Sun et al. proposed a powertrain efficiency model of HEV based on situations of vehicle following, energy optimization of HEV, and economical HEV following, which showed that fusion control was theoretically superior to series control [
29,
30,
31]. However, the energy consumption impact caused by energy loss of motor and battery efficiency was ignored. The SARTRE project in Europe, the ENERGY ITS project in Japan, and the GCDC project in the Netherlands have all shown that the vehicle queue stream could significantly reduce traffic congestion and improve traffic efficiency and fuel economy, which has become one of the leading directions for vertical control of smart vehicles [
32,
33,
34,
35]. At present, there is still a potential problem for the fuel consumption of the whole vehicle, which combines the efficiency of the hybrid vehicle engine and motor, the efficiency of battery charge and discharge, the efficiency of the transmission system, and the control of the transmission speed ratio.
In view of the current traffic congestion and the relative tail-end traffic accidents, the energy crisis, as well as environmental pollution, this paper proposes a control method of the combination of HEV fleet control and energy saving. Based on the intelligent fuzzy control algorithm, the distance between vehicles was controlled, and the driving demands (speed and acceleration) of different vehicles were obtained. According to this requirement, the efficiency optimization model of the vehicle system under different working modes was established, and the efficiency optimization of the engine, motor, battery, and transmission system was carried out, while the energy matching control strategies of different vehicles in the driving process were obtained. Finally, the effectiveness of the control method was verified by simulation analysis. The paper’s organization is as follows:
Section 1 gives the Introduction. In
Section 2, the research object is chosen as a single axis parallel HEV with two clutches. The optimal efficiency model of HEV is established to ensure the energy optimization during driving in
Section 3, which is based on the analysis of the operating mode characteristics of HEV. In
Section 4, the intelligent fleet model and energy matching model of HEV are built with the simulation platform of MATLAB/Simulink/Stateflow. The validity of the energy matching strategy of HEV under the principle of optimal system efficiency is verified by simulation analysis, and the purpose of improving the driving safety, traffic efficiency, and fuel economy of the fleet is achieved. Conclusions are drawn in
Section 5.