The powertrain of a fuel cell vehicle typically consists of two energy sources: a proton electrolyte membrane fuel cell (PEMFC) stack and a battery package. In this paper, multi-dimensional dynamic programming (MDDP) is used to solve the energy management strategy (EMS) of fuel cell hybrid powertrain. This study built a fuel cell hybrid powertrain model, in which the battery model is built based on the Thevenin equivalent circuit. In order to improve the calculating efficiency and maintain the accuracy of the algorithm, the state variables in each stage are divided into primary and secondary. In the reverse solution process, the corresponding relationship between the multi state variables grid and the optimal cumulative function has been changed from three-dimensional to two-dimensional. The EMS based on MDDP is applied to component sizing of a commercial vehicle. Simulations were conducted using MATLAB under the C-WTVC working condition. By analyzing the fuel economy and system durability, the optimal component combination of comprehensive performance is obtained. Compared with the EMS based on dynamic programming (DP), the proposed method effectively improves the calculation accuracy: the hydrogen consumption can be reduced by 3.10%, and the durability of the fuel cell and battery can be improved by 1.08% and 0.13%, respectively.

The autonomous driving is rapid developing recently and model predictive controls (MPCs) have been widely used in unmanned vehicle trajectory tracking. MPCs are advantageous because of their predictive modeling, rolling optimization, and feedback correction. In recent years, most studies on unmanned vehicle trajectory tracking have used only linear model predictive controls to solve MPC algorithm shortcomings in real time. Previous studies have not investigated problems under conditions where speeds are too fast or trajectory curvatures change rapidly, because of the poor accuracy of approximate linearization. A nonlinear model predictive control optimization algorithm based on the collocation method is proposed, which can reduce calculation load. The algorithm aims to reduce trajectory tracking errors while ensuring real-time performance. Monte Carlo simulations of the uncertain systems are carried out to analyze the robustness of the algorithm. Hardware-in-the-loop simulation and actual vehicle experiments were also conducted. Experiment results show that under i7-8700, the calculation time is less than 100 ms, and the mean square error of the lateral deviation is maintained at ${10}^{-3} {\mathrm{m}}^2$, which proves the proposed algorithm can meet the requirement of real time and accuracy in some particular situations. The unmanned vehicle trajectory tracking method provided in this article can meet the needs of real-time control.

Energy management strategy (EMS), as a core technology in fuel cell vehicles (FCVs), profoundly influences the lifespan of fuel cells and the economy of the vehicle. Aiming at the problem of the EMS of FCVs based on a global optimization algorithm not being applicable in real-time, a rule extraction-based EMS is proposed for fuel cell commercial vehicles. Based on the results of the dynamic programming (DP) algorithm in the CLTC-C cycle, the deep learning approach is employed to extract output power rules for fuel cell, leading to the establishment of a rule library. Using this library, a real-time applicable rule-based EMS is designed. The simulated driving platform is built in a CARLA, SUMO, and MATLAB/Simulink joint simulation environment. Simulation results indicate that the proposed strategy yields savings ranging from 3.64% to 8.96% in total costs when compared to the state machine-based strategy.

In order to improve the efficiency of gearshift controller parameter optimization and obtain a good gearshift controller control effect, this article proposes an optimization method for electric vehicle transmission gearshift controller, and selects dual clutch transmission as the research object that establishes an 11-degree-of-freedom gearshift dynamics model and a feedforward-feedback gearshift control model. The feedforward control is chosen from the target trajectory given by the Legendre pseudo-spectral approach, and the feedback controller is a Gaussian kernel radial basis function neural network controller. The feedback controller performs parameter optimization by the Probabilistic Inference for Learning Control (PILCO) reinforcement learning algorithm to obtain a control strategy that matches the actual gearshift conditions. By comparing how well the master-slave moving disk can follow the target trajectory during various optimization iterations, it is verified that the algorithm requires only a few experiments to complete the optimization, and the optimized Radial Basis Function Neural Network (RBFNN) control has a better control effect by comparing the results of different iterations. Applying the learned controller to various slope and load circumstances yields data that demonstrate that all can have an obvious optimization effect with good robustness. Additionally, the reinforcement learning technique suggested in this research can be used for various gearshift controller parameter optimization to assist engineers and technicians in increasing the effectiveness of their Research and Development.

Distributed drive electric vehicles (DDEV), characterized by their independently drivable wheels, offer significant advantages in terms of vehicle handling, stability, and energy efficiency. These attributes collectively contribute to enhancing driving safety and extending the all-electric range for sustainable transportation. Nonetheless, the challenge persists in designing a control strategy that effectively coordinates the objectives of handling, stability, and energy efficiency under both lateral and longitudinal driving conditions. To this end, this paper proposes a cooperative control strategy for DDEVs, incorporating active rear steering (ARS) and direct yaw moment control (DYC) to enhance handling capabilities, stability, and energy efficiency. A stability boundary is delineated using an analytical expression that correlates with the front wheel steering angle, and an adjustment factor is introduced to quantify vehicle stability based on this input parameter. This factor aids in establishing a coordinated control reference for handling and stability. At the upper-level motion control layer, a model predictive control method is developed to track this reference and implement ARS and DYC for superior performance. Specifically, the rear lateral force serves as the control command for ARS, which is converted into a rear wheel steering angle using a tire inverse model. Meanwhile, the front lateral force is modeled as linear-time-varying to simplify calculations. At the lower-level torque allocation layer, the adjustment factor is utilized to balance tire workload rate and in-wheel motors’ (IWM) energy consumption, enabling efficient switching between energy consumption and driving stability targets, and the torque allocation is conducted to acquire the expected IWMs’ command. Both the upper and lower-level optimization problems are formulated as convex problems, ensuring efficient and effective solutions. Simulations verify the effectiveness of this strategy in improving handling, stability, and energy economy under DLC cases, while maintaining high computational efficiency.

In increasingly complex parking lot environments, driving path planning needs to balance multiple objectives such as efficiency and safety. Aiming at the problems of difficult weight determination and single solution in traditional weighted sum multi-objective optimization, this paper proposes a multi-objective driving path planning method based on the Non-Dominated Sorting Dynamic Programming (NSDP) algorithm. In grid map path planning, NSDP has more effective solutions compared with other algorithms, providing a wider selection space for path planning. In parking lot path planning, first, the A* algorithm is used to generate path guide points and discretize them. Variable-curvature Dubins curves are adopted to connect state points. After calculating the cost of each optimization objective, NSDP is used to screen optimal solutions. Then, the entropy weight-Topsis method is applied to recommend paths, and finally the conjugate gradient method is used to smooth the paths. The results show that the proposed method is verified in the parking lot scenario: the recommended driving paths effectively balance multiple objectives, and the curvature is second-order continuous.