The DOC (diesel oxidation catalyst), DPF (diesel particulate filter), SCR (selective catalytic reduction), and ASC (ammonia slip catalyst) are widely used in diesel exhaust after-treatment systems. The thermal management of after-treatment systems using DOC, DPF, SCR, and ASC were investigated to improve the efficiency of these devices. This paper aims to identify the challenges of this topic and seek novel methods to control the temperature. Insulation methods and catalysts decrease the energy required for thermal management, which improves the efficiency of thermal management. Thermal insulation decreases the heat loss of the exhaust gas, which can reduce the after-treatment light-off time. The DOC light-off time was reduced by 75% under adiabatic conditions. A 400 W microwave can heat the DPF to the soot oxidation temperature of 873 K at a regeneration time of 150 s. An SCR burner can decrease NOx emissions by 93.5%. Electrically heated catalysts can decrease CO, HC, and NOx emissions by 80%, 80%, and 66%, respectively. Phase-change materials can control the SCR temperature with a two-thirds reduction in NOx emissions. Pt-Pd application in the catalyst can decrease the CO light-off temperature to 113 °C. Approaches of catalysts can enhance the efficiency of the after-treatment systems and reduce the energy consumption of thermal management.

A nanostructured solid solution catalyst CeZrK/rGO for soot oxidation in catalyzed diesel particulate filter was synthesized using the dipping method. The reduced graphene oxide (rGO) was used as the catalyst carrier, and CeO₂, ZrO₂, and K₂O were mixed with the molar ratio of 5:1:1, 5:2:2 and 5:3:3, which were referred to as Ce₅Zr₁K₁/rGO, Ce₅Zr₂K₂/rGO, and Ce₅Zr₃K₃/rGO, respectively. The structure, morphology and catalytic activity of the CeZrK/rGO nanocomposites were thoroughly investigated and the results show that the CeZrK/rGO nanocomposites have nanoscale pore structure (36.1–36.9 nm), high-dispersion quality, large specific surface area (117.2–152.4 m²/g), small crystallite size (6.7–8.3 nm), abundant oxygen vacancies and superior redox capacity. The 50% soot conversion temperatures of Ce₅Zr₁K₁/rGO, Ce₅Zr₂K₂/rGO, and Ce₅Zr₃K₃/rGO under tight contact condition were decreased to 352 °C, 339 °C and 358 °C respectively. The high catalytic activity of CeZrK/rGO nanocomposites can be ascribed to the following factors: the doping of Zr and K ions causes the nanocrystalline phase formation in CeZrK solid solutions, reduces the crystallite size, generates abundant oxygen vacancies and improves redox capacity; the rGO as a carrier provides a large specific surface area, thereby improving the contact between soot and catalyst.

The serpentine tube liquid cooling and composite PCM coupled cooling thermal management system is designed for 18650 cylindrical power batteries, with the maximum temperature and temperature difference of the power pack within the optimal temperature operating range as the target. The initial analysis of the battery pack at a 5C discharge rate, the influence of the single cell to cooling tube distance, the number of cooling tubes, inlet coolant temperature, the coolant flow rate, and other factors on the heat dissipation performance of the battery pack, initially determined a reasonable value for each design parameter. A control strategy is used to regulate the inlet flow rate and coolant temperature of the liquid cooling system in order to make full use of the latent heat of the composite PCM and reduce the pump’s energy consumption. The simulation results show that the maximum battery pack temperature of 309.8 K and the temperature difference of 4.6 K between individual cells with the control strategy are in the optimal temperature operating range of the power battery, and the utilization rate of the composite PCM is up to 90%.

To address the limitations in multi-scale feature processing and illumination sensitivity of existing steel surface defect detection algorithms, we proposed ADP-YOLOv8-n, enhancing accuracy and computational efficiency through advanced feature fusion and optimized network architecture. Firstly, an adaptive weighted down-sampling (ADSConv) module was proposed, which improves detector adaptability to diverse defects via the weighted fusion of down-sampled feature maps. Next, the C2f_DWR module was proposed, integrating optimized C2F architecture with a streamlined DWR design to enhance feature extraction efficiency while reducing computational complexity. Then, a Multi-Scale-Focus Diffusion Pyramid was designed to adaptively handle multi-scale object detection by dynamically adjusting feature fusion, thus reducing feature redundancy and information loss while maintaining a balance between detailed and global information. Experiments demonstrate that the proposed ADP-YOLOv8-n detection algorithm achieves superior performance, effectively balancing detection accuracy, inference speed, and model compactness.

Ammonia is increasingly regarded as a carbon-free energy carrier for hard-to-electrify power sectors, including marine propulsion, heavy-duty transport, and distributed generation. Its direct use in internal combustion engines, however, is constrained by high ignition energy, low laminar flame speed, narrow flammability limits, slow low-temperature chemistry, and strong trade-offs among efficiency, nitrogen-containing emissions, and unburned ammonia slip. Hydrogen enrichment is one of the most effective routes for improving ammonia combustion reactivity, but it also introduces a multivariable control problem: hydrogen fraction, ammonia injection timing, injection mode, air-path dilution, ignition strategy, and aftertreatment operation are tightly coupled and strongly condition-dependent. This review synthesizes recent progress in ammonia–hydrogen and ammonia-based dual-fuel engine control from a control-oriented perspective. The discussion first summarizes application scenarios, nonlinear combustion-mode transitions, emission-formation pathways, and control-relevant metrics. It then compares actuator-level strategies, including ammonia injection timing and staging, port and direct injection, hydrogen energy-fraction scheduling, excess-air-ratio and EGR control, high-energy ignition, and turbulent jet ignition. Advanced optimization methods are further reviewed, with emphasis on model predictive control, control-oriented combustion and emission models, artificial-intelligence-based virtual sensors, and reinforcement-learning control. The analysis shows that the central challenge is no longer whether ammonia can burn in an engine, but how a controller can keep the system inside a narrow moving window bounded by misfire, knock, NO_x, N₂O, and NH₃ slip. Finally, future research priorities are proposed, including engine–aftertreatment co-optimization, physics-informed virtual sensing, digital-twin-assisted calibration, lightweight deployment on electronic control units, and robust control under fuel and aging uncertainty.