As fossil fuels continue to be extracted and used, issues such as environmental pollution and energy scarcity are surfacing. For the transportation industry, the best way to achieve the goal of “carbon neutrality” is to research efficient power systems and develop new alternative fuels. As the world’s largest product of chemicals, ammonia is a new renewable fuel with good combustion energy. It can be used as an alternative fuel to reduce carbon emissions because of its proven production process, low production and transportation costs, safe storage, the absence of carbon-containing compounds in its emissions, and its future recyclability. This paper firstly introduces the characteristics of ammonia fuel engine and its problems; then it summarizes the effects of various ammonia-blended fuels on the combustion and emission characteristics of the engine from the combustion problem of ammonia-blended engine; then the fuel storage of ammonia-blended hydrogen is discussed, the feasibility of hydrogen production instead of hydrogen storage is introduced.

As a complex field-circuit coupling system comprised of electric, magnetic and thermal machines, the permanent magnet synchronous motor of the electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency and communication characteristics make it difficult to diagnose faults. Meanwhile, the common intelligent diagnosis methods have low accuracy, poor generalization ability and difficulty in processing high-dimensional data. This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier. First, the motor signals collected from the experiment were processed, and the input data were randomly damaged by adding noise. Furthermore, according to the experimental results, the network structure of stacked denoising autoencoder was constructed, the optimal learning rate, noise reduction coefficient and the other network parameters were set. Finally, the trained network was used to verify the test samples. Compared with the traditional fault extraction method and single autoencoder method, this method has the advantages of better accuracy, strong generalization ability and easy-to-deal-with high-dimensional data features.

The PointPillars algorithm can detect vehicles, pedestrians, and cyclists on the road, and is widely used in the field of environmental awareness in autonomous driving. However, its feature encoding network only uses a minimalist PointNet network for feature extraction of point cloud information, which does not consider the global context information of the point cloud, and the local structure features are not sufficiently extracted, and these feature losses can seriously affect the performance of the object detection network. To address this problem, this paper proposes an improved PointPillars algorithm named TGPP: Transformer-based Global PointPillars. After the point cloud is divided into several pillars, global context features and local structure features are extracted through a multi-head attention mechanism, so that the point cloud after feature coding has global context features and local structure features; the two-dimensional pseudo-image generated by this feature is used for feature learning using a two-dimensional convolutional neural network. Finally, the SSD detection head is used to achieve 3D object detection. It is demonstrated that the TGPP achieves an average accuracy improvement of 2.64% in the KITTI test set.

In response to the complex nonlinear characteristics of magnetorheological (MR) dampers, which make it difficult to achieve strict vibration reduction requirements in time-varying working environments due to uncertainties, this paper aims to develop a refined model for the MR damper and improve the deep reinforcement learning network based on its dynamic characteristics to optimize the control strategy. Firstly, a refined dynamic model for the MR damper is designed, and its Simulink model is established and corrected based on the characteristic data of the custom damper. Secondly, an optimization framework for suspension control is constructed, where the suspension and road surface serve as the environment, and the proximal policy optimization (PPO) algorithm is used as the agent. Next, the nonlinear relationship between the damping force of the damper and its performance indicators is explored, and a PPO-neural ordinary differential equations (NODEs) algorithm is proposed by combining NODE with PPO. This approach allows for continuous fitting of state variables, enabling high-precision nonlinear fitting between the state space and policy representation. Finally, a quarter-car test bench is set up using the custom MR damper, and the simulation and experimental results of passive suspension, fuzzy PID, original PPO, and the proposed method are compared. The experimental results show that the proposed method demonstrates significant advantages: under class C road conditions, the body acceleration in simulation and test bench experiments is reduced by 30.22% and 77.51% respectively compared to passive suspension; 20.55% and 37.82% compared to fuzzy PID; and 11.26% and 25.53% compared to the original PPO. Additionally, the simulation results on Class B random road further validate the good generalization performance of the proposed method.

To address the issue of incorrect recognition in the automatic emergency braking (AEB) systems on curved roads, a target recognition model is proposed to obtain the road curvature and to calculate the relative lateral distance. Based on the information from the ego vehicle and the preceding vehicles, the accurate selection of the hazardous target is accomplished. After identifying the dangerous target, a control strategy based on the fusion algorithm is proposed, because the safety distance model and the Time-to-Collision (TTC) model both have their limitations and cannot ensure driving safety and comfort simultaneously. The TTC model is optimized according to the actual relative distance between two vehicles on curved roads, the graded warning strategy and braking intervention time are established by the TTC model. And then the graded braking strategy is designed according to the safety distance model. The simulation platform is built based on Carsim and Simulink for verification and analysis. The results demonstrate that the proposed AEB control strategy on curved roads can accurately and efficiently identify the target vehicles on curved roads, avoid false triggering issues, and improve the AEB system’s reliability. And effectively avoid collisions with target vehicles that are in the same lane, improving driving safety and comfort.