Suzuki Takashi

<jats:p>With the decarbonization of internal combustion engines, alternative fuels have gained increasing attention. When using fuels with low combustibility, such as ammonia, detailed analysis of the intake system and in-cylinder flow is essential for improving combustion efficiency. Proper orthogonal decomposition (POD) has been widely used to extract coherent structures in flow fields within internal combustion engines. However, most previous studies have focused on analyzing cycle-to-cycle variations in gasoline engines, while time-resolved analysis within a single cycle of diesel engines has rarely been conducted. In this study, the effect of tangential port opening on in-cylinder flow characteristics was investigated using an optical single-cylinder diesel engine equipped with two intake ports and two exhaust ports. The opening area of the tangential port was varied under five conditions using different gaskets, and in-cylinder velocities were measured using particle image velocimetry. POD was applied to the acquired velocity data to evaluate the flow structures of the higher modes and their correlations with the mean flow and turbulence intensity. The results showed that in POD mode 1, a swirl flow was formed during the compression stroke when the tangential port opening exceeded 25%. Evaluation of the correlation between POD mode 1 and the ensemble-averaged flow using the relevance index revealed a strong correlation during the compression stroke. In POD mode 2, complex flows were observed during the intake stroke, and structures different from the mean flow were also confirmed during the compression stroke. A moderate correlation was observed between POD mode 2 and turbulence intensity under all conditions. Energy contribution analysis indicated that in the early intake stroke, the variation in mode 1 was large, and the flows represented by mode 2 and higher modes were dominant, whereas in the late compression stroke, mode 1 consistently accounted for a higher proportion.</jats:p>

<jats:p>Torque vectoring (TV) is a commonly used method for four in-wheel motor electric vehicles (4-IWM EVs). Several existing studies based on model predictive control (MPC) focus on improving system stability and energy efficiency by minimizing or maximizing a performance function, defined as the time integral of the weighted sum of two cost functions. However, this approach must address the challenge of balancing these two objectives. Furthermore, the MPC framework lacks sufficient robustness against model uncertainties and external disturbances. This study proposes a two-layer TV controller for a 4-IWM EV, designed to enhance both robustness and energy efficiency, as specified in the Autonomous Driving Control Benchmark Challenge of IEEE CDC 2023. The first layer includes a direct yaw moment control module and a longitudinal force control module based on first-order sliding mode control, integrated with nonlinear disturbance observers (NDOBs) to estimate disturbance amplitudes from rough roads and reduce chattering. The second layer employs MPC to allocate torque among the wheels to minimize total energy consumption. Simulations, performed using a full 4-IWM EV simulator developed in Modelica, focused on the ISO double lane change on a rough road, as required by the benchmark challenge. The results demonstrate that the proposed control system significantly improves the vehicle’s robustness and energy efficiency in this scenario.</jats:p>