Browse Topic: Vehicle dynamics
In this study, vibration characteristics inside an electric power unit at gravity center where direct measurement is impossible were estimated by using virtual point transformation to consider guideline for effective countermeasures to the structure or generated force characteristics inside the power source. Vibration acceleration, transfer function and the generated force in operation at the gravity center of the electrical power source were obtained by vibration characteristics at around the power source which can be measured directly. In addition, the transfer functions from the gravity center to the power source attachment points on the product were also estimated. And then, the contribution from the gravity center to the power unit attachment point was obtained by multiplying generated force with the transfer function. As results, the obtained total contribution was almost same with the actual measured vibration at the attachment point. Furthermore, the rotational contribution
In cost- effective P2 hybrid vehicles with low voltage electric machines connected to the engine, an interesting control problem arises during the transition to a locked driveline state. This occurs when the engine connects to the wheels via a separation clutch. The two primary torque sources, the engine and the clutch, are traditionally imperfect estimators of applied and transferred torques. The Hybrid Supervisor’s feedforward constraints model relies on these imperfect inputs to determine torque and acceleration limits for the engine’s desired acceleration profiles and to specify engine feedforward commands, aiming for synchronization speed. Due to the inaccuracies in the torque estimates of the engine and clutch, the Hybrid Supervisor is susceptible to control windup, increased jerk to the driveline during synchronization, and inaccurate computation of its target acceleration profile, speed, and torque targets for the engine to achieve synchronization speed. This paper presents a
Vehicle handling is significantly influenced by aerodynamic forces, which alter the normal load distribution across all four wheels, affecting vehicle stability. These forces, including lift, drag, and side forces, cause complex weight transfers and vary non-linearly with vehicle apparent velocity and orientation relative to wind direction. In this study, we simulate the vehicle traveling on a circular path with constant steering input, calculate the normal load on each tire using a weight transfer formula, calculate the effect of lift force on the vehicle on the front and rear, and calculate the vehicle dynamic relation at steady state because the frequency of change due to aerodynamic load is significantly less than that of the yaw rate response. The wind velocity vector is constant while the vehicle drives in a circle, so the apparent wind velocity relative to the car is cyclical. Our approach focuses on the interaction between two fundamental non-linearity’s: the nonlinear
Drivers present diverse landscapes with their distinct personalities, preferences, and driving habits influenced by many factors. Though drivers' behavior is highly variable, they can exhibit clear patterns that make sorting them into one category or another possible. Discrete segmentation provides an effective way to categorize and address the differences in driving style. The segmentation approach offers many benefits, including simplification, measurement, proven methodology, customization, and safety. Numerous studies have investigated driving style classification using real-world vehicle data. These studies employed various methods to identify and categorize distinct driving patterns, including naturalist differences in driving and field operational tests. This paper presents a novel hybrid approach for segmenting driver behavior based on their driving patterns. We leverage vehicle acceleration data to create granular driver segments by combining event and trip-based methodologies
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