Eco-driving technology is an effective way for connected and automated plug-in hybrid electric vehicles (CAPHEVs) to achieve energy conservation and emission reduction. However, the constraints of traffic flows and signal lights at intersections pose serious challenges to eco-driving of CAPHEVs. Therefore, an eco-driving control method based on dynamic consumption prediction is proposed. A hierarchical control scheme is designed that divides eco-driving problem into global velocity planning and energy management. Firstly, a multistage time-series prediction model is constructed based on two long short-term memory networks, which is applied to predict optimal energy consumption cost. Secondly, the velocity trajectory in global speed planning is optimized by combining the multistage consumption prediction model with dynamic programming. Finally, an energy management strategy considering energy consumption and pollutant emission is proposed. The real-world environment information is applied to construct the virtual simulation environment and verify the proposed method. The simulation results demonstrate that the proposed multistage prediction model has a better prediction performance than the existing popular models. Meanwhile, the total cost savings by the proposed scheme range from 13.5% to 48.7%. Particularly, the proposed method not only significantly reduces emissions, but also guarantees driving safety, and satisfies the different demands for travel time.

To further improve the comprehensive operating performance of the single motor hybrid electric vehicle, a single motor hybrid powertrain configuration with dual planetary gears (SMHPC-2PG) design is proposed in this paper. By adopting a topology design method that characterizes the constraint relationship between power resource components and planetary gear (PG) nodes, all feasible configuration candidates based on the basic configuration scheme are systematically explored, and dynamic models of configuration candidates are automatically generated. The optimal fuel economy and dynamic performance for configuration candidates are simulated by applying the global optimal control strategy based on dynamic programming (DP). Results of this study demonstrate that SMHPC-2PG with excellent operating performance can be screened out by this method.

Existing research on parallel hybrid electric vehicles (HEV) mainly focuses on optimizing the component sizes and control strategies of the single-motor parallel hybrid electric powertrain (SMPHP), and less analyzes the influence of powertrain configuration on the performance of the vehicle. Therefore, the influence of the power coupling type and transmission type of the powertrain configuration on the fuel economy and drivability performance of parallel HEVs is studied in this paper. Considering three types of powertrain topologies (P2 torque-coupled, P2 dual-mode coupled and P3 torque-coupled) and two types of automatic transmissions (DCT and CVT), six typical types of SMPHP configurations to be discussed are determined. To obtain their optimal fuel economy and drivability performance, a multi-objective optimization and analysis method based on dynamic programming and multi-objective particle swarm optimization algorithm is proposed to optimize the component sizes and control variables of powertrain configurations. Finally, the optimal performance and component size optimization results of six typical SMPHP configurations are analyzed and compared, and the influence of powertrain configuration on the performance and components sizing of the SMPHP is obtained, which contributes to the configuration design of the parallel hybrid electric powertrain.

The novel power-cycling variable transmission (PCVT) has special speed regulation characteristics, and its gearshift process is accompanied by sudden change of speed ratio and change of system efficiency, which affect the shift quality. In order to propose an effective shift control strategies, a dynamic model of shifting process is established based on the analysis of PCVT's working principle and speed regulation characteristics. According to the shifting mechanism of PCVT, the shifting process is divided into four stages, and the dynamic equation is established in stages. Based on the PCVT shift control requirements, the clutch and engine controller are designed in stages. For the torque phase, a finite time linear quadratic regulator is used to determine the optimal clutch engagement and disengagement speed, with the jerk and friction work as optimization indices. For the inertia phase, the fuzzy compensation controllers of the clutch and the engine are designed respectively under the comprehensive consideration of the factors such as the speed ratio of torque converter and the system efficiency. Simulation results show that the proposed shift control strategy can achieve smooth shift and meet the control requirements well.

For a two gear AMT pure electric MPV, on the basis of theoretical studies, the parameters of MPV are adjusted. The car is establish by AVL Cruise, and simulation is carried out. On the premise that the power performance and economy met the requirements, so as to mushroom the economy, a combined simulation model is established and MIGA (multi island genetic algorithm) is used to optimize the power performance and economy of the electric MPV to obtain the optimal vehicle layout. The optimization results show that under the NEDC (New European Driving Cycle) cycle condition, the 100 km power consumption is reduced by 0.52 kWh, and the economy is diminished by 3.4%; the 0~100 km/h acceleration time is reduced by 0.7 s, and the power performance is boosted by 5.8% compared with that before optimization.