Wenguang Luo

This paper proposes a distributed model predictive control (DMPC) framework integrating an event-triggered mechanism and an adaptive alternating direction method of multipliers (ADMM) to address the challenges of constrained computational resources and stringent real-time requirements in distributed vehicle platoon control systems. Firstly, the longitudinal dynamic model and communication topology of the vehicle platoon are established. Secondly, under the DMPC framework, a controller integrating residual-based adaptive ADMM and an event-triggered mechanism is designed. The adaptive ADMM dynamically adjusts the penalty parameter by leveraging residual information, which significantly accelerates the solving of the quadratic programming (QP) subproblems of DMPC and ensures the real-time performance of the control system. In order to reduce unnecessary solver invocations, the event-triggered mechanism is employed. Finally, numerical simulations verify that the proposed control strategy significantly reduces both the computation time per optimization and the cumulative optimization instances throughout the process. The proposed approach effectively alleviates the computational burden on onboard resources and enhances the real-time performance of vehicle platoon control.

Energy management is the key technology for power distribution and driveability of fuel cell hybrid electric vehicles. To maximize the economic potential and prolong the power source lifetime of hydrogen fuel cell/battery/supercapacitor-based hybrid electric vehicles, this paper proposes an energy management strategy (EMS) based on dual fuzzy control with Pareto multi-objective optimization. Firstly, the dual fuzzy control strategy is developed to satisfy the vehicle dynamics, wherein the main-fuzzy controller determines the power output of the fuel cell, and the sub-fuzzy controller regulates the power output of the Li-ion battery and supercapacitor or recovers the braking energy of the vehicle. Secondly, the improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective problem, wherein both the equivalent energy consumption and the equivalent power source performance degradation are incorporated in the cost function. And then, to obtain an optimized EMS, the membership functions of the dual fuzzy controllers are optimized by using the obtained Pareto non-inferiority solution set. Finally, the joint simulation model is built using MATLAB and ADVISOR, and nine Pareto non-inferior solutions are selected to verify the effectiveness and superiority of the proposed strategy. The results show that the proposed strategy is able to select different optimized control schemes according to the different situations of the amount of hydrogen stored in the fuel cell, ensuring that the equivalent energy consumption value and the equivalent power source performance degradation value reach the desired effect. Taking the fuel cell storing a moderate amount of hydrogen as an example, compared with the power following control strategy and the unoptimized double fuzzy control strategy, the proposed strategy can reduce the equivalent energy consumption by 3.1% and 2.1%, and the equivalent power source performance degradation by 88.8% and 86.4%, respectively, and it effectively reduces fluctuations of the FC output power.

In complex road scenarios, issues such as low-detection accuracy of positive and negative obstacles on the road surface and weak generalization ability of detection models are prevalent. We propose a high-precision detection network for positive and negative road obstacles, named multiscale diffusion and mix-attention transformer (MSD-MixFormer), which is based on multiscale fusion and diffusion mix-attention mechanisms. This network is designed based on the transformer architecture with a window adjustment mechanism to capture multi-scale features of obstacles, thereby addressing the issue of confusion between obstacle foreground and background. In the shallow and deep stages of the encoder module, dilation self-attention and diffusion self-attention are introduced respectively to enhance the feature fusion capability of the network. In the decoder module, mix-attention operations are incorporated to improve the segmentation accuracy of detailed features, addressing the problem of detail feature loss. To improve the generalization ability of the model, we collect and extend the VRE-NPO dataset with images from actual complex road scenarios. Experiments show that the MSD-MixFormer model converges faster and more stably during training compared with other higher-level models in the same series. In testing, MSD-MixFormer achieves higher detection accuracy for positive and negative obstacles, as well as for objects of varying sizes, across various complex road scenarios.

Separate DC-DC converters for each energy source are typically configured in fuel-cell hybrid vehicles. This results in a complex control structure of the powertrain system, low energy density of the converter, and high cost due to the large number of components. Conducting research on DC-DC converters with good energy flow management and high integration is a trend to solve such problems. Based on the analysis of the basic functional structure of the converter, this paper designs a buffering unit circuit with energy collection and distribution functions and appropriately connects it with the pulse unit circuit of the converter. Through device optimization reuse and power transmission path integration, a class of non-isolated four-port DC-DC converters is constructed, which consists of an auxiliary energy charging module, input energy source control module, braking energy feedback module and forward bootstrap boost circuit. This converter has two bi-directional ports, a uni-directional input and a bi-directional output, for separate connection to the power batteries, supercapacitors, fuel cells and DC bus. It can adapt to the fluctuation of the vehicle’s driving condition while achieving dynamic and flexible regulation of power flow and can flexibly allocate power according to the load current and voltage level of energy. It can realize a total of 14 operation modes, including six output power supply operation modes, five auxiliary power charging operation modes, and three braking energy regeneration operation modes. Furthermore, the mathematical model of this converter is constructed using the state-average method and the small-signal modeling method in order to achieve the responsiveness and stability of switching multiple operating modalities. The PI control parameters are optimized using the particle swarm optimization algorithm to achieve optimized control of the converter. The simulation system is set up using MATLAB R2024a to verify that the proposed converter topology and algorithm can dynamically allocate appropriate current paths to manipulate the power flow under various operating conditions, effectively improving the utilization rate and efficiency of energy. The converter has the characteristics of high gain and high power density, which is suitable for three-energy fuel cell hybrid electric vehicles.

In response to the fact that autonomous vehicles cannot avoid obstacles by emergency braking alone, this paper proposes an active collision avoidance method for autonomous vehicles based on model predictive control (MPC). The method includes trajectory tracking, adaptive cruise control (ACC), and active obstacle avoidance under high vehicle speed. Firstly, an MPC-based trajectory tracking controller is designed based on the vehicle dynamics model. Then, the MPC was combined with ACC to design the control strategies for vehicle braking to avoid collisions. Additionally, active steering for collision avoidance was developed based on the safety distance model. Finally, considering the distance between the vehicle and the obstacle and the relative speed, an obstacle avoidance function is constructed. A path planning controller based on nonlinear model predictive control (NMPC) is designed. In addition, the alternating direction multiplier method (ADMM) is used to accelerate the solution process and further ensure the safety of the obstacle avoidance process. The proposed algorithm is tested on the Simulink and CarSim co-simulation platform in both static and dynamic obstacle scenarios. Results show that the method effectively achieves collision avoidance through braking. It also demonstrates good stability and robustness in steering to avoid collisions at high speeds. The experiments confirm that the vehicle can return to the desired path after avoiding obstacles, verifying the effectiveness of the algorithm.

Energy management strategies for hydrogen fuel cell hybrid electric vehicles (FCHEVs) are a key factor in achieving real-time vehicle energy optimization control, vehicle driving economy, and fuel cell durability. In this paper, for an FCHEV equipped with a fuel cell and supercapacitor, the quantitative information, logic rules, and operational constraints are transformed into linear integer inequalities according to its different operating modes, and the Hysdel language is used to establish its mixed logic dynamic model (MLD). Then, the energy management strategy based on model predictive control (MPC) is developed using the MLD model as the prediction model and the equivalent hydrogen consumption and the performance degradation of the fuel cell as the optimization performance indexes. Finally, under the World Light Vehicle Test Cycle, a joint simulation was carried out with Advisor and Simulink to verify the proposed strategy’s superiority by comparing it with the power following control strategy (PFCS) and the compound fuzzy control strategy (CFCS). The results show that the strategy not only ensures real-time FCHEV energy control, but also reduces hydrogen consumption by 10.98% and 1.98% and the number of start/stop times of a fuel cell by six and four, compared to PFCS and CFCS, respectively, which improves the economy of the whole vehicle as well as the durability of the fuel cell.