Zhi Wang

To reveal the physical evolution of methanol spray under different environmental conditions and injection strategies, this study focuses on the atomization and evaporation behavior of low-pressure methanol spray. The coupled effects of temperature, pressure, and injection parameters are systematically investigated based on constant-volume combustion chamber experiments and three-dimensional CFD simulations. The formation, evolution, and interaction mechanisms of the liquid column core and cooling core are revealed. The results indicate that temperature is the dominant factor influencing methanol spray atomization. When the temperature increases from 255 K to 333 K, the spray penetration distance increases by approximately 70%, accompanied by a pronounced shortening of the liquid-core length and enhanced evaporation and air entrainment. Under low-temperature conditions, a stable liquid-core structure and a strong cooling core are formed, characterized by a high-density, long-axis morphology and an extensive low-temperature region, which suppress fuel–air mixing and ignition. Increasing the ambient pressure improves spray–air mixing but reduces penetration; at 255 K, increasing the ambient pressure from 0.05 MPa to 0.2 MPa increases the spray cone angle by approximately 10% while reducing the penetration distance by about 50%. Furthermore, optimizing the injection pressure or shortening the injection pulse width effectively enhances atomization performance: increasing the injection pressure from 0.4 MPa to 0.6 MPa and reducing the pulse width from 5 ms to 2 ms increases the penetration distance by approximately 30% and reduces the mean droplet diameter by about 20%.

Methanol is a liquid fuel with high oxygen content and the potential for a closed-loop carbon-neutral production cycle. To investigate the mixture formation and combustion characteristics of a heavy-duty Port Fuel Injection (PFI) methanol engine, a three-dimensional numerical simulation model was established using the CONVERGE 3.0 software. Multi-cycle simulations were performed to analyze the influence of wall film dynamics on engine performance. The results indicate that the “adhesion–evaporation” equilibrium of the intake port wall film determines the in-cylinder mixture concentration. Due to the high latent heat of vaporization of methanol, severe wall-wetting occurs during the initial cycles, causing the actual fuel intake to lag behind the injection and leading to an overly lean mixture and misfire. Regarding injection strategies, the open valve injection (OVI) strategy utilizes high-speed intake airflow to reduce wall adhesion and improve fuel transport efficiency compared to closed valve injection. OVI refers to the fuel injection strategy that injects fuel into the intake port during the intake valve opening phase. The open valve injection strategy (e.g., SOI −500° CA) demonstrates distinct superiority over closed valve strategies (SOI −200°/−100° CA), achieving a 75% reduction in wall film mass. The long injection duration and early phasing allow the high-speed intake airflow to carry fuel directly into the cylinder, significantly minimizing wall film accumulation and avoiding the “fuel starvation” observed in closed-valve strategies. Additionally, OVI fully utilizes methanol’s latent heat to generate an intake cooling effect, which lowers the in-cylinder temperature and helps suppress knock. Furthermore, a dual-injector strategy is proposed to balance spatial atomization and rapid fuel transport, which achieves a 66.7% increase in the fuel amount entering the cylinder compared with the original strategy. This configuration effectively resolves the fuel induction lag, achieving stable combustion starting from the first cycle.

Ammonia can significantly reduce carbon emissions when used in internal combustion engines. However, pure ammonia is considered difficult to ignite and has a slow flame propagation speed, which makes its application challenging. Furthermore, previous research on pure ammonia engines has been based on bench tests, with no vehicle-level tests reported to date. In this study, an engine was tested using pure ammonia as a single fuel in a range-extended hybrid electric vehicle. First, a pure ammonia hybrid power system was implemented in a light-duty vehicle. By motoring the engine instantly to its optimal operating window, the hybrid mode ensures a rapid transition to stable combustion. The results show that, using pure ammonia, the engine can operate stably within a speed range of 1000–3175 rpm. The engine achieves an output power of 45 kW, with an indicated thermal efficiency exceeding 40% under 3175 rpm. Compared to gasoline, pure ammonia has a longer ignition delay but a similar combustion duration. Pure ammonia requires an earlier spark timing and higher intake temperature. The ammonia and NO remain high even after being treated by a three-way catalyst. This research verifies the feasibility of using pure ammonia as a single fuel in hybrid modes, offering broad application prospects in scenarios such as marine power and stationary power generation.

Ammonia is a highly promising zero-carbon fuel for engines. However, it exhibits high ignition energy, slow flame propagation, and severe pollutant emissions, so it is usually burned in combination with highly reactive fuels such as hydrogen. An accurate understanding and modeling of ammonia–hydrogen combustion is of fundamental and practical significance to its application. Deep Neural Networks (DNNs) demonstrate significant potential in autonomously learning the interactions between high-dimensional inputs. This study proposed a deep neural network-based method for optimizing chemical reaction mechanism parameters, producing an optimized mechanism file as the final output. The novelty lies in two aspects: first, it systematically compares three DNN structures (Multi-layer perceptron (MLP), Convolutional Neural Network, and Residual Regression Neural Network (ResNet)) with other machine learning models (generalized linear regression (GLR), support vector machine (SVM), random forest (RF)) to identify the most effective structure for mapping combustion-related variables; second, it develops a ResNet-based surrogate model for ammonia–hydrogen mechanism optimization. For the test set (20% of the total dataset), the ResNet outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R²) of 0.9923 and root mean square error (RMSE) of 135. The surrogate model uses the trained ResNet to optimize mechanism parameters based on a Stagni mechanism by mapping the initial conditions to experimental IDT. The results show that the optimized mechanism improves the prediction accuracy on laminar flame speed (LFS) by approximately 36.6% compared to the original mechanism. This method, while initially applied to the optimization of an ammonia–hydrogen combustion mechanism, can potentially be adapted to optimize mechanisms for other fuels.

The synergy of reinforcement learning (RL)-based energy management and vehicle-to-everything communication has been proved effective in boosting the fuel economy of connected plug-in hybrid electric vehicles (PHEVs). However, the intricate coupling of mechanical, electrical, thermal states and driving cycle results in a high-dimensional complex energy control problem for PHEVs, which is challenging to optimally solve within the same time scale. To this end, this study designs a multi-horizon reinforcement learning (MHRL)-based energy management of PHEVs, aware of the traffic preview from intelligent transportation systems to optimize the energy flow and thermal states as well as the transient dynamics of the powertrain. The proposed strategy features a novel state space representation, and solves the coordinated training among multiple sub-networks belonging to different control tasks in various time scales. Simulation and hardware-in-the-loop experiments are carried out based on a standard driving cycle and a real-world driving cycle with real-time traffic data demonstrate that the MHRL strategy improves fuel economy by 3.0%~7.9% compared to conventional RL-based energy management under various coolant temperature conditions and dynamic driving scenarios.

Automotive energy management systems (EMSs) are evolving to achieve intelligence across their entire lifecycle, from initial product development to real-world customer usage. This paper proposes a bi-level transfer approach with model-agnostic meta-learning (MAML) to realize cross-platform transferable and online-adaptive EMS. In the development phase, MAML calibrates the heuristic control maps of an instantaneous optimization-based EMS, with a per-unit state-action space design ensuring seamless knowledge transfer between vehicle platforms. During the usage phase, real driving data are adopted to refine onboard control parameters. The framework is validated through real vehicle experiments. Firstly, the entire MAML-assisted V-cycle development is implemented to validate the optimality and knowledge transfer of the EMS, resulting in zero-shot transfer for EMS calibration on new vehicle products. Additionally, real vehicle experimental tests show that a correction of 8.0%~9.5% fuel economy is improved against the convention reinforcement learning-based EMS during usage via online-adaptation, effectively bridging the gap between pre-trained policies and real-world optimal energy management.

Combining the physical significance of theoretical models with the high fitting accuracy of data-driven models is crucial for precise characterization of fuel cell electric truck (FCET) energy consumption. This paper proposes a theory-constrained neural network (TCNN), integrating physical significance without compromising accuracy. A theory-guided filter is applied to ensure the interpretability of each sub-module. Fuel cell temperature and voltage are modeled separately, each serving as an important input for the other. Sub-networks are trained individually under the constraints of the theory-guided filter to minimize the error between the final output and the data. Additionally, a neural network based on the CNN-BiLSTM-MHSA architecture ensures model accuracy. Results show that the proposed neural network significantly improves fitting accuracy for both fuel cell temperature and voltage. The proposed TCNN successfully integrates the theoretical model, improving accuracy by 33.7% compared to the theoretical model and maintaining accuracy comparable to the purely data-driven model. Hydrogen consumption calculated based on this model shows accuracy improvements of 63.7% and 49.4% compared to traditional methods using efficiency curves and hydrogen consumption curves, respectively. The proposed voltage model and temperature model using TCNN enable accurate hydrogen consumption calculations, which can be broadly applied to energy management, component sizing, and other related areas.

Blending ammonia in combustion is an effective approach to reducing carbon emission for diesel engines, but the combustion of ammonia may result in the generation of elevated concentrations of nitrous oxide (N ₂ O), potentially contributing to a new source of greenhouse gas emissions. This study investigates the impact of engine load, diesel double injection strategy, and the ammonia energy ratio (AER) on N ₂ O emission in ammonia-diesel dual-fuel combustion mode. The results show that N ₂ O emission is increased with blending ammonia compared to pure diesel combustion, while as the load increases, the higher in-cylinder combustion temperature leads to a decrease in N ₂ O emission. In the ammonia-diesel dual-fuel combustion mode, diesel double injection strategy is conducive to the reduction in N ₂ O emission compared to single injection strategy. As the pre-injection timing is delayed, N ₂ O emission first decreases and then increases, reaching a minimum near −40 °CA ATDC. The postponed main-injection timing leads to the decreased in-cylinder combustion temperature, deteriorating N ₂ O emission. In the engine with a compression ratio (CR) of 18, when AER is in the range of 20% to 65%, the volume fraction of N ₂ O emission remains around 20 × 10 ⁻⁶ . However, under the condition of AER = 80%, low chemical reactivity of ammonia causes the substantially increased N ₂ O emission. By increasing CR to 21 and adopting optimal injection strategy, combustion in activated thermal atmosphere is achieved, resulting in a substantial reduction in the N ₂ O original emission concentration. Ultra-low N ₂ O emission (exhaust N ₂ O volume fraction less than 10 × 10 ⁻⁶ ) is achieved at AER = 80%.

Jet ignition is a promising technology for improving the thermal efficiency of internal combustion engines (ICEs). To achieve the highest possible thermal efficiency, the jet ignition strategy was studied on a gasoline engine. Firstly, a combustion process featuring three stages of heat release, based on uniformly distributed jet ignition, was proposed. Next, the geometric structure of an active jet igniter was optimized using numerical simulations. Using the designed jet igniter, the effects of engine operating conditions were explored, including auxiliary fuel injection in the jet igniter, gross excess air ratio, and engine load and speed, with the goal of maximizing thermal efficiency. A highest indicated thermal efficiency of 50.3% was achieved, verifying the effectiveness of the proposed combustion strategy with uniformly distributed jet ignition. This study provides a technical reference for developing high-efficiency gasoline engines using jet ignition.