Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles.

Aiming at problems of large computational complexity and poor reliability, a parameter matching optimization method of a powertrain system of hybrid electric vehicles based on multi-objective optimization is proposed in this paper. First, according to the vehicle basic parameters and performance indicators, the parameter ranges of different components were analyzed and calculated; then, with the weight coefficient method, the multi-objective optimization (MOO) problem of fuel consumption and emissions was transformed into a single-objective optimization problem; finally, the co-simulation of AVL Cruise and Matlab/Simulink was achieved to evaluate the effects of parameter matching through the objective function. The research results show that the proposed parameter matching optimization method for hybrid electric vehicles based on multi-objective optimization can significantly reduce fuel consumption and emissions of a vehicle simultaneously and thus provides an optimized vehicle configuration for energy management strategy research. The method proposed in this paper has a high application value in the optimization design of electric vehicles.

In order to reduce the vibration caused by mode switching of hybrid electric vehicles (HEVs) and achieve smooth mode switching, the hybrid input and output automation (HIOA) model of power control system of a parallel HEV is established based on the theory of hybrid dynamical system (HDS). Taking the switching from electric drive mode to hybrid drive mode for example, the torque coordination control is considered, and the performance is compared with the method without the torque coordination by using a rule-based control strategy. The simulation results in AVL Cruise show that, on the premise of ensuring the fuel economy and the emission, the mode switching process becomes smoother with smaller torque fluctuation and better driving comfort by considering the torque coordination.

The fluidized bed is a critical reactor in the energy and chemical industries, where the mixing and agglomeration behaviors of binary particles significantly influence both the efficiency of reaction processes and the uniformity of final products. However, the selection of appropriate drag force models remains a subject of debate due to the variability in particle properties and operating conditions. In this study, we investigated the fluidization behavior of binary mixtures composed of two different sizes of Geldart-D particles within a fluidized bed, evaluating nine distinct drag force models, including Wen and Yu; Schiller and Naumann; Ergun; Gidaspow, Bezburuah, and Ding; Huilin and Gidaspow; De Felice; Syamlal and O’Brien; and Hill, Koch, and Ladd. We focused on four key parameters: particle mixing degree, migration characteristics, temperature variation, and mean pressure drop. Simulation results revealed that the choice of drag model markedly affected mixing behavior, migration dynamics, and temperature distribution. Notably, the Ergun; Gidaspow, Bezburuah, and Ding; and Hill, Koch, and Ladd models exhibited superior particle mixing uniformity. While the drag model had a relatively minor impact on particle temperature changes, its selection became critical in simulations requiring high-temperature precision. Regarding pressure drop, the Huilin and Gidaspow and Gidaspow, Bezburuah, and Ding models demonstrated smaller and more stable pressure drop fluctuations. These findings offer valuable theoretical insights into gas–solid two-phase flow under binary particle mixing and provide practical guidance for the design and operation of fluidized bed reactors.