Fuel cell hybrid electric vehicles have the advantages of zero emission, high efficiency and fast refuelling, etc. and are one of the key directions for vehicle development. The energy management problem of fuel cell hybrid electric vehicles is the key technology for power distribution. The traditional power following strategy has the advantage of a real-time operation, but the power correction is usually based only on the state of charge of a lithium battery, which causes the operating point of the fuel cell to be in the region of a low efficiency. To solve this problem, this paper proposes a hybrid power-following-fuzzy control strategy, where a fuzzy logic control strategy is used to optimise the correction module based on the power following strategy, which regulates the state of charge while correcting the output power of the fuel cell towards the efficient operating point. The results of the joint simulation with Matlab + Advisor under the Globally Harmonised Light Vehicle Test Cycle Conditions show that the proposed strategy still ensures the advantages of real-time energy management, and for the hydrogen fuel cell, the hydrogen consumption is reduced by 13.5% and 4.1% compared with the power following strategy and the fuzzy logic control strategy, and the average output power variability is reduced by 14.6% and 5.1%, respectively, which is important for improving the economy of the whole vehicle and prolonging the lifetime of fuel cell.

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.

In order to improve the real-time performance of the trajectory tracking of autonomous vehicles, this paper applies the alternating direction multiplier method (ADMM) to the receding optimization of model predictive control (MPC), which improves the computational speed of the algorithm. Based on the vehicle dynamics model, the output equation of the autonomous vehicle trajectory tracking control system is constructed, and the auxiliary variable and the dual variable are introduced. The quadratic programming problem transformed from the MPC and the vehicle dynamics constraints are rewritten into the solution of the ADMM form, and a decreasing penalty factor is used during the solution process. The simulation verification is carried out through the joint simulation platform of Simulink and Carsim. The results show that, compared with the active set method (ASM) and the interior point method (IPM), the algorithm proposed in this paper can not only improve the accuracy of trajectory tracking, but also exhibits good real-time performance in different prediction time domains and control time domains. When the prediction time domain increases, the calculation time shows no significant difference. This verifies the effectiveness of the ADMM in improving the real-time performance of MPC.

Aiming at the problem that the tracking accuracy of unmanned vehicle path tracking preview control is greatly affected by the preview time, a BP neural network adaptive preview control method is proposed. Considering that the prediction effect of the BP neural network is limited to the initial value setting, a preview time adjuster based on the SSA-BP neural network was established; by establishing the relationship between the front wheel steering angle and the preview time, a new direction control driver model was formed. The driver model and the preview time adjuster together constitute an adaptive steering controller. In order to solve the influence of the longitudinal speed change on the vehicle stability, a PID variable-speed controller was designed to realize the horizontal and vertical coordinated control of the path tracking of the unmanned vehicle. Compared with the fixed preview time and the BP preview time control method, the results show that the proposed method has strong tracking ability when driving at various speeds on three consecutive curves and Alt 3 test roads, and can be used when driving at a variable speed.

An improved RRT* algorithm, referred to as the AGP-RRT* algorithm, is proposed to address the problems of poor directionality, long generated paths, and slow convergence speed in multi-axis robotic arm path planning. First, an adaptive biased probabilistic sampling strategy is adopted to dynamically adjust the target deviation threshold and optimize the selection of random sampling points and the direction of generating new nodes in order to reduce the search space and improve the search efficiency. Second, a gravitationally adjustable step size strategy is used to guide the search process and dynamically adjust the step-size to accelerate the search speed of the algorithm. Finally, the planning path is processed by pruning, removing redundant points and path smoothing fitting using cubic B-spline curves to improve the flexibility of the robotic arm. Through the six-axis robotic arm path planning simulation experiments on the MATLAB platform, the results show that the AGP-RRT* algorithm reduces 87.34% in terms of the average running time and 40.39% in terms of the average path cost; Meanwhile, under two sets of complex environments A and B, the average running time of the AGP-RRT* algorithm is shortened by 94.56% vs. 95.37%, and the average path cost is reduced by 55.28% vs. 47.82%, which proves the effectiveness of the AGP-RRT* algorithm in improving the efficiency of multi-axis robotic arm path planning.

Conventional hybrid vehicles with two energy sources require two separate on-board DC/DC converters to connect the battery and the fuel cell, which have the disadvantages of large size, high cost, high losses and few applicable operating conditions. To address this situation, this paper proposes an optimized on-board integrated DC/DC converter with a non-isolated multi-port scheme that integrates a unidirectional port for the fuel cell and a bidirectional port for the battery and load. This can achieve a combined energy supply and recovery with a single integrated converter, effectively overcoming the above disadvantages. The optimized converter topology is relatively simple, and the magnetic losses of the transformer are removed. Furthermore, the switched capacitor is introduced as a voltage doubling unit to achieve high-gain output, so the fuel cell and battery voltage demand levels are reduced under the same load conditions. In addition, it has superior performance in system energy management for hybrid vehicles, which can distribute power and switch operating states by controlling the on/off of switching devices to make it suitable for five driving conditions. This paper discusses in detail the operating principles of the converter and analyzes its steady-state performance under five operating modes, derives its dynamic model, and proposes a proportional-integral control scheme. Finally, the simulation model of the topology is built by Matlab/Simulink software to verify the converter operation in each driving state, and the simulation experimental results verify the applicability of the proposed integrated DC/DC converter topology.