Considering the limited driving range and inconvenient energy replenishment way of battery electric vehicle, fuel cell electric vehicles (FC EVs) are taken as a promising way to meet the requirements for long-distance low-carbon driving. However, due to the limitation of FC power ability, a battery is usually adopted as the supplement power source to fill the gap between the requirement of driving and the serviceability of FC. In consequence, energy management is essential and crucial to an efficient power flow to the wheel. In this paper, a self-optimizing power matching strategy is proposed, considering the energy efficiency and battery degradation, via implementing a deep deterministic policy gradient. Based on the proposed strategy, less energy consumption and longer FC and battery life can be expected in FC EV powertrain with optimal hybridization degree.

As an auxiliary component with the largest energy consumption in the fuel cell power system, the electric air compressor is of great significance to improve the overall efficiency of the system by reducing its power consumption under the premise of meeting the cathode intake demand. In this paper, the flow state of the gas in the flow field of the fuel cell TSEAC (two-stage electric air compressor) is analyzed by simulation, and the accuracy of the simulation results is verified by experiments. Through the research on the gas compression work of the fuel cell TSEAC, it is found that the higher temperature rise of the gas during the compression process will increase the compression work, thereby reducing the efficiency of the fuel cell TSEAC. Therefore, based on the field synergy theory, this paper designs the heat dissipation structure of the TSEAC elbow. In the common working conditions of fuel cell TSEAC, micro-fin tube is an effective energy-saving structure that takes into account heat dissipation enhancement and flow resistance, and its ratio of micro-fin height to laminar bottom layer thickness ε/δ = 1.6 has the best energy-saving effect. Finally, the energy-saving effect of the micro-fin tube is verified by simulation. The load torque of the optimized fuel cell TSEAC is reduced from 1.540 N·m to 1.509 N·m, and the shaft power is reduced from 14.51 kW to 14.22 kW. Its efficiency increased by 1.9%.

The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and decision-making technologies for autonomous vehicles. Although the accuracy of the global positioning system (GPS) is extremely high, it is vulnerable to interference. Further, despite the low positioning accuracy of inertial navigation systems (INSs), their robustness is notably high. Therefore, an integrated navigation information method based on the Adaptive Particle Filter and the Iterative Kalman Filter (APF-IKF) was developed in this study. Firstly, an integrated navigation system model was established. Then, the IKF was adopted to estimate the speed, latitude and longitude errors of the INS. Thirdly, the newest estimated error results were introduced into the APF to optimize the distribution function, and the particle quality was improved. In this process, the APF can filter non-Gaussian noise, preliminarily estimate the error, optimize the result with the IKF and correct the output information of the INS with the final estimated error. Finally, by using differential GPS positioning as the benchmark, we built a real-vehicle test platform with a low-cost and low-precision GPS and inertial units and carried out a series of real-vehicle tests. The experimental results show that compared with the traditional KF method, APF-IKF can significantly improve the positioning accuracy and robustness of the system.

Commercial vehicles generally drive at a higher speed on structured expressways, and their higher center of mass leads to a lower rollover threshold and a greater rollover risk while steering. Therefore, the design of a lateral trajectory-tracking control strategy for commercial vehicles should not only consider the accuracy of trajectory tracking but also consider roll stability. Based on this control objective, a fuzzy linear quadratic controller was designed in this study to ensure rolling stability in the path-tracking control process and improve the adaptability of the strategy to the driving scenario. Firstly, a steering and braking cooperative control model based on the four-degree-of-freedom model and the multi-point preview model was established. Then, a path tracking controller considering roll stability was designed based on the linear quadratic theory. On this basis, a fuzzy linear quadratic controller was designed to realize the online optimization of cost function weights. Finally, the effectiveness of the control strategy was verified using co-simulation and hardware-in-loop experiments. The results show that the designed controller can effectively adjust the weight of path-tracking and stability according to the vehicle’s state. This effectively improves the vehicle’s control distribution problem.