Polyoxymethylene dimethyl ether (PODE) and methanol are important low-carbon substitutable fuels for reducing carbon emissions in internal combustion engines. In the research, the impacts of methanol ratio, injection timing, and intake temperature on HCHO generation and emission were investigated using both engine tests and numerical simulations. Results suggest that an increase in methanol ratio suppresses auto-ignition tendency of PODE, leading to the increase of ignition delay period, pressure peak, and heat release rate peak inside the cylinder. The decrease in in-cylinder combustion temperature contributes to an increase in HCHO emission due to partial oxidation of methanol in the cylinder and exhaust pipe. While the injection timing is gradually postponed from -10 °CA ATDC to 2 °CA ATDC, in-cylinder high-temperature area decreases, the quantity of unburned methanol increases, but part of HCHO is converted to HCO due to H radical influence, resulting in 72% increased HCHO emission. With the increment of intake temperature, the oxidation and decomposition of in-cylinder methanol accelerate, leading to an improvement in combustion stability, more uniform temperature distribution, and a decrease in unburned methanol, which results in lower HCHO emission. When the intake temperature is rose from 30 to 60 °C, HCHO emission decreases by 11.2%.

Sugarcane crushing is a complex process with multiple factors, multiple objectives, strong coupling, large nonlinearity and uncertainty. Due to the ambiguity of the pressing mechanism and the unexplicability of data-driven, the process index of sugarcane pressing process is difficult to predict. In order to solve this problem, this paper combines deep learning with pressing mechanism, and establishes a process index prediction model of sugarcane pressing process based on physics-informed neural network (PINN). Firstly, the constitutive model of sugarcane was established based on the pressing mechanism. Combined with the porous medium control equation and numerical simulation, the sugarcane pressing mechanism model was established and verified, which provided high-quality simulation data for subsequent research. Secondly, the porous medium control equation is embedded into the PINN model as a physical law to establish a unique loss function of the sugarcane pressing process. Combining the historical data and simulation data of the workshop, a large sample data is made to train the model, and the model is compared with the common data-driven model to further illustrate the accuracy and stability of the established model.

Elastic materials for seat foundations come in a variety of materials, shapes, and dimensions. However, it is difficult to measure the stress-strain relationships of many elastic material combinations by conventional uniaxial compression tests. This study investigated the stress-strain relations of elastic material combinations for foam foundations using finite element analysis (FEA). First, the stress-strain relations of single-layer polymer foams and a combination of double-layer polymer foams with covering were quantified by uniaxial compression tests, and axial tensile tests quantified the properties of the covering material of the fabric. Then, based on the Ogden foam and Ogden constitutive equation of Ansys Workbench 19.2, the test data of single-layer polymer foams and covering were fitted by a non-linear least square method, and a combination of double-layer polymer foams with the covering is predicted by FEA. When the strain was 10% to 65%, the stress error between FEA and test results dropped from 95.68% to -5.08%.

An efficient and stable braking feedback scheme is one of the key technologies to improve the endurance performance of pure electric vehicles. In this study, four constraint conditions for different braking feedback schemes were clearly defined, and tests and simulation analysis were carried out based on “the relationship between rear-drive feedback efficiency and vehicle configuration conditions” and “the relationship between front-drive feedback efficiency and braking efficiency”. The results show that for rear-driving, the RSF2 scheme with low dependence on the constraint conditions of tramping characteristics is the comprehensive optimal scheme under the condition of decoupling control constraints, and the mileage improvement rate reaches 29.2%. For front driving, the FSF1A scheme is the comprehensive optimal scheme considering both braking efficiency and feedback efficiency, and the mileage improvement rate reaches 35.8%. Finally, the feasibility of the proposed braking feedback scheme is proved using the drum test under cyclic conditions, and the research results provide a theoretical basis for the optimization of braking feedback energy efficiency of small pure electric vehicles.

Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles’ power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks.

To explore the application potential of renewable methanol and direct dual fuel stratification (DDFS) technology in marine internal combustion engines, this study conducted a fuel injection strategy research based on performance optimization for a marine methanol/diesel DDFS engine under high methanol substitution rate (95%). The results show that the fuel mixing process plays a crucial role in methanol/diesel DDFS engine combustion state switching. Premature methanol injection under methanol single-stage injection strategy causes ringing intensity (RI) to exceed the engine limit. Additionally, when start of diesel injection (SOID) occurs earlier than start of methanol injection (SOIM), the shorter methanol/diesel injection interval (MDI) leads to the deterioration of diesel premixed combustion for reason of methanol spray interference. Therefore, adopting an injection strategy with SOID earlier than SOIM under methanol single-stage strategy combined with an appropriately extended MDI achieves better comprehensive engine performance. Further optimization of methanol/diesel DDFS engine using a methanol two-stage injection strategy shows that the methanol two-stage injection strategy provides better fuel economy while maintaining acceptable RI. By increasing the methanol pre-injection ratio (MR1) and optimizing the methanol two-stage injection interval (MMI), the engine economy was further enhanced. Compared with the methanol single-stage injection strategy, the optimized methanol two-stage injection strategy reduced equivalent indicated specific fuel consumption (EISFC) by 3.95%. However, it should be noted that the combustion completeness under the methanol two-stage injection strategy decreased, leading to higher emissions of HC, CO and soot. On the other hand, this strategy resulted in lower NOX emissions due to more sufficient premixed combustion.