• Energy Research

Abstract Homogeneous charge compression ignition (HCCI) combustion mode is a relatively new combustion technology that can be achieved by using specially designed cams with reduced lift and duration. The auto-ignition in HCCI engine can be facilitated by adjusting the timing of the exhaust-valve-closing and, to some extent, the timing of the intake-valve-opening so as to capture a proportion of the hot exhaust gases in the engine cylinder during the gas exchange process. The effects of variable valve timing strategy on the gas exchange process and performance of a 4-valve direct injection HCCI engine were computationally investigated using a 1D fluid-dynamic engine cycle simulation code. A non-typical intake valve strategy was examined; whereby the intake valves were assumed to be independently actuated with the same valve-lift profile but at different timings. Using such an intake valves strategy, the obtained results showed that the operating range of the exhaust-valve-timing within which the HCCI combustion can be facilitated and maintained becomes much wider than that of the typical intake-valve-timing case. Also it was found that the engine parameters such as load and volumetric efficiency are significantly modified with the use of the non-typical intake-valve-timing. Additionally, the results demonstrated the potential of the non-typical intake-valve strategy in achieving and maintaining the HCCI combustion at much lower loads within a wide range of valve timings. Minimizing the pumping work penalty, and consequently improving the fuel economy, was shown as an advantage of using the non-typical intake-valve-timing with the timing of the early intake valve coupled with a symmetric degree of exhaust-valve-closing timing.

Abstract In this paper, n-hexane flashing jets discharged from a single-hole gasoline direct injector (GDI) were studied numerically with the adoption of diffuse Eulerian framework and the homogeneous relaxation model (HRM). The fuel temperature ranged from 30 to 130 °C, and the ambient pressure varied from 20 to 101 kPa. The results showed that considerable vaporization started at the counter bore and a liquid core existed near the nozzle exit. Due to drastic vaporization, the pressure within the liquid core increased so the two-phase flow became under-expanded. Violent expansion then occurred and a low-pressure region was formed, which is believed as the origin of the spray collapse under flashing conditions for multi-hole GDI injectors. At high superheat levels, shock wave structures similar to those in highly under-expanded gaseous jets were identified. However, the transonic position located at some distances from the nozzle rather than at the throttle. Besides, vapor fraction played the dominant role in the onset of expansion, while the expansion was ended by the pressure difference between the two sides of the Mach disk.

A qualitative and quantitative analysis of toxic but currently non-regulated hydrocarbon compounds ranging from C5–C11, before and after a zoned three-way catalytic converter (TWC) in a modern gasoline direct injection (GDI) engine has been studied using gas chromatography-mass spectrometry (GC–MS). The GDI engine has been operated under conventional and advanced combustion modes, which result in better fuel economy and reduced levels of NOx with respect to standard SI operation. However, these fuel-efficient conditions are more challenging for the operation of a conventional TWC, and could lead to higher level of emissions released to the environment. Lean combustion leads to the reduction in pumping losses, fuel consumption and in-cylinder emission formation rates. However, lean HCCI will lead to high levels of unburnt HCs while the presence of oxygen will lower the TWC efficiency for NOx control. The effect on the catalytic conversion of the hydrocarbon species of the addition of hydrogen upstream the catalyst has been also investigated. The highest hydrocarbon engine-out emissions were produced for HCCI engine operation at low engine load operation. The catalyst was able to remove most of the hydrocarbon species to low levels (below the permissible exposure limits) for standard and most of the advanced combustion modes, except for naphthalene (classified as possibly carcinogenic to humans by the International Agency for Research on Cancer) and methyl-naphthalene (which has the potential to cause lung damage). However, when hydrogen was added upstream of the catalyst, the catalyst conversion efficiency in reducing methyl-naphthalene and naphthalene was increased by approximately 21%. This results in simultaneous fuel economy and environmental benefits from the effective combination of advanced combustion and novel aftertreatment systems.

Abstract Lithium-ion batteries have been widely used in renewable energy storage and electric vehicles, and state-of-health (SoH) prediction is critical for battery safety and reliability. Following the standard SoH prediction routine based on charging curves, a human-knowledge-augmented Gaussian process regression (HAGPR) model is proposed by incorporating two promising artificial intelligence techniques, i.e., the Gaussian process regression (GPR) and the adaptive neural fuzzy inference system (ANFIS). Human knowledge on voltage profile during battery degradation is first modeled with an ANFIS for feature extraction that helps reduce the need for physical testing. Then, the ANFIS is integrated with a GPR model to enable SoH prediction. Using a GPR model as the baseline, a comparison study is conducted to demonstrate the advantage of the proposed HAGPR model. It indicates that the proposed HAGPR model can reduce at least 12% root-mean-square error with 31.8% less battery aging testing compared to the GPR model.

The accurate determination and dynamic adjustment of key control parameters are challenges for equivalent consumption minimization strategy (ECMS) to be implemented in real-time control of hybrid electric vehicles. An adaptive real-time ECMS is proposed for hybrid heavy-duty truck in this paper. Three efforts have been made in this study. First, six kinds of typical driving cycle for hybrid heavy-duty truck are obtained by hierarchical clustering algorithm, and a driving condition recognition (DCR) algorithm based on a neural network is put forward. Second, particle swarm optimization (PSO) is applied to optimize three key parameters of ECMS under a specified driving cycle, including equivalent factor, scale factor of penalty function, and vehicle speed threshold for engine start-up. Finally, combining all the above two efforts, a novel adaptive ECMS based on DCR and key parameter optimization of ECMS by PSO is presented and validated through numerical simulation. The simulation results manifest that proposed adaptive ECMS can further improve the fuel economy of a hybrid heavy-duty truck while keeping the battery charge-sustainability, compared with ECMS and PSO-ECMS under a composite driving cycle.

Abstract Gasoline direct injection (GDI) engine development is facing the great challenges in both fuel economy and particulate emissions. Trade-off is often required in GDI engines to sacrifice fuel economy in order to meet the strict emission regulations. GDI injector deposits have been identified as a potential cause of increased particulate emissions. In this work, a series of experimental tests was conducted on a 1.5 L turbocharged GDI engine to further understand the effect of injector deposits. The deposits formed on the injector tip surface were removed after the 55-hour fouling test and their effects on fuel consumption, in-cylinder combustion, thermal efficiency and engine out emissions were investigated before and after the removal. The spray characteristics of an identical injector under clean and fouled conditions were examined and the deposits inside the injector nozzle holes were observed by a scanning electron microscope. The test injectors were mildly fouled with an average of 1.5% flow rate loss and 1.84% injection pulse width increase. After removing the injector tip surface deposits, the engine combustion phase became advanced and the peak in-cylinder pressure increased. The combustion efficiency was close to 98% and showed no significant change. Although the indicated thermal efficiency was only slightly improved by 0.31–0.44% after removing the tip surface deposits, the particulate emissions were significantly affected and reduced by up to around 45%. At the meantime, NOx emissions moderately increased by approximately 12% after removal and no significant change occurred in the THC and CO emissions.

Abstract In this study, the spray characteristics of a five-hole gasoline direct injector were investigated in a constant volume vessel. The absolute ambient pressure ranged from 0.5 to 10.0 bar, and the fuel temperatures were 20 °C and 80 °C. High-speed imaging and phase Doppler measurement technique were utilized to investigate the spray morphology and the droplet dynamics, respectively. Spray collapses were observed in the near field under the elevated ambient pressure (higher than 1.0 bar) conditions and in the far field under the flash boiling conditions in both macroscopic and microscopic levels. In addition, under the elevated ambient pressure conditions, the droplets at the inner side of the target jet were larger than those at the outer side, due to the increased probability of droplet coalescence as a result of spray collapse. Whilst, slight increase in droplet diameter in the inner side of the target jet was also found under the flash boiling conditions. Furthermore, the near-field collapse at the elevated ambient pressure conditions was attributed to the jet-air interaction, termed as jet-induced spray collapse; and the far-field collapse under the flash boiling conditions was attributed to the dramatic temperature drop and the resultant vapor condensation, termed as condensation-induced spray collapse.