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In this paper, the range extension control system based on least square method is proposed for electric vehicles with in-wheel motors and front active steering. This proposed method distributes front and rear wheel side-slip angles and driving force difference between left and right motors from lateral force and yaw-moment. The proposed method enables to reduce driving resistance generated from front steering angle. In fact, the mileage per charge is extended up to 200 m/kWh. Simulations and experiments are carried out to confirm the effectiveness of the proposed method.

In this paper an estimation method on location of peak pressure (LPP) employing flame ionization measurement, with the spark plug as a sensor, was discussed to achieve combustion parameters estimation at quick start of HEV engines. Through the cycle-based ion signal analysis, the location of peak pressure can be extracted in individual cylinder for the optimization of engine quick start control of HEV engine. A series of quick start processes with different cranking speed and engine coolant temperature are tested for establishing the relationship between the ion signals and the combustion parameters. An Artificial Neural Network (ANN) algorithm is used in this study for estimating these two combustion parameters. The experiment results show that the location of peak pressure can be well established by this method.

Soot and carbon dioxide released from internal combustion engines became the key issues when using fossil fuels. The use of zero-carbon fuel, ammonia, with hydrocarbon fuels may play an important role in reducing the exhaust effect on the environment and mitigating the reliance on nonrenewable energy resources. However, ammonia reduces the flame speed of hydrocarbon fuels. A numerical approach was executed to study the ammonia impact on n-heptane, a diesel surrogate, flame. A kinetic mechanism was prepared by adding the sub-mechanism of ammonia, NO2 and NO3 emissions, and soot precursors to the n-heptane kinetic mechanism. The modified Arrhenius equation and soot surface reactions were used to study the soot formation with NOx emissions. The results showed that ammonia decreased the fractions of carbon-related species and raised the concentration of non-carbon-related species.

Combustion variation is widely known as a factor that prevents engines from achieving high efficiency. In this study, a model to predict IMEP per cycle is constructed by machine learning. Furthermore, we propose a control method for cycle-to-cycle combustion variation using the model. The effectiveness and performance of the proposed method are experimentally validated on a spark-ignited gasoline engine test bench. From the experimental results, IMEP per cycle was not successfully controlled. This may be due to the low prediction accuracy of the model and the use of what is considered to be the highest efficiency for comparison.

In order to meet increasingly stringent emission regulations, it is significance to establish a control- oriented transient NOx and PM emission prediction model and improve the accuracy and real-time performance. In this study, the prediction model of transient PM emissions based on Transformer is established. In terms of model accuracy and real-time performance, Transformer emission prediction model is compared with Multilayer perceptron (MLP) neural network and Long-Short Term Memory (LSTM) emission prediction model. The results show that the performance of Transformer transient emission prediction model is superior to other model structures, it can be used for real-time prediction.

In order to improve the ignition stability and reduce the cycle-to-cycle variation, it is necessary to understand the mechanism of the flame kernel development and the local quenching effect during the spark ignition process. In this study, experiments for the spark ignition process in a high-speed lean gasoline-air mixture turbulent flow field were conducted. OH* chemiluminescence measurement and focusing Schlieren photography was applied to observe the development of flame kernel and discharge channel behaviors simultaneously. Results indicated that flame kernel fragments, generated by the restrike and short- circuit of discharge channels, quenched due to the local turbulence, which led to slow flame propagation or misfire. In that cases, the initial flame kernels showed stretched behaviors, along with high curvatures.

Soot and carbon dioxide released from internal combustion engines became the key issues when using fossil fuels. Ammonia and hydrogen having zero-carbon species can reduce carbon-related emissions and enhance the reliance on renewable fuels. A comparative study of ammonia and hydrogen impact on combustion and emission characteristics of iso-octane flame was performed under different combustion conditions. Arrhenius equation, soot surface reactions, and modified kinetic mechanism were used to study the flame growth, soot nucleation, and surface growth rates. The results show that hydrogen increased the temperature about 20.74 K and 59.30 K, whereas ammonia reduced it about 82.17 K and 66.03 K at premixed and counterflow conditions, respectively. The flame speed of iso-octane was increased 43.83 cm/s by hydrogen and decreased 34.36 cm/s by ammonia. A reduction in CH2O caused a reduction in CO and CO2 emissions.

The continuous improvement of spark-ignition direct-injection (SIDI) engines is largely attributed to the enhanced understanding of air-fuel mixing and combustion processes. The intricate interaction between transient spray behavior and the ambient flow field is important to unveil the airflow dynamics during the spray injection process. This study investigates the fuel-spray boundary interactions under different superheated conditions by analyzing the ambient flow field pattern with constraint-based modeling (CBM). In the experimental setup, superheated conditions are facilitated by adjusting different fuel temperatures and ambient pressures. By adding the tracer particles containing Rhodamine 6G to the ambient air, the combined diagnostic of fluorescent particle image velocimetry (FPIV) and Mie-scattering is implemented to measure the velocity distribution and flow trajectory of the air surrounding the spray formation and propagation.