Browse Topic: Vehicle acceleration
Semi-trailer trains are the main force of highway freight. In a complex environment with multiple vehicles, accidents are easily caused by complex structures and driver operation problems. Intelligent technology is urgently needed to improve safety. In view of the shortcomings of existing research on its dedicated models and algorithms, this paper studies the intelligent decision-making and trajectory planning of semi-trailer trains under multiple vehicles. A local trajectory planning method based on global path planning and Frenet coordinate decoupling based on the improved A* algorithm is proposed. The smooth weight transition function and B-spline curve are introduced to optimize the global path. The polynomial function is combined with the acceleration rate to optimize the local trajectory. TruckSim, Prescan and Simulink are used to build a joint simulation platform for multi-condition verification. The simulation results show that the search efficiency of the improved A* algorithm
A road simulator reproduction method was developed to reproduce the off-road conditions of utility vehicles in a laboratory setting. Off-road running behavior can be reproduced by considering the effects of inertial forces from jump landings owing to uneven terrain and slow-speed navigation. However, extremely low-frequency components and behaviors, including inertial forces from jumps, vehicle acceleration and deceleration, are difficult to reproduce with a normal road simulator in the limited test space of a laboratory. Therefore, it is common practice to intentionally remove input components below 1 Hz. Alternatively, inertial forces can be reproduced by adding a restraining device to the sprung mass of the vehicle along the wheel-axle inputs. Therefore, the former method excludes extremely low-frequency components, whereas the effects between actuators, which increase the test complexity and time required, should be canceled in the latter method. Furthermore, the restraining device
The growing demand for improved air quality and reduced impact on human health along with progress in vehicle electrification has led to an increased focus on accurate Emission Factors (EFs) for non-exhaust emission sources, like tyres. Tyre wear arises through mechanical and thermal processes owing to the interaction with the road surface, generating Tyre Road Wear Particles (TRWP) composed of rubber polymers, fillers, and road particles. This research aims to establish precise TRWP airborne EFs for real-world conditions, emphasizing in an efficient collection system to generate accurate PM10 and PM2.5 EFs from passenger car tyres. Particle generation replicates typical driving on asphalt road for a wide selection of tyres (different manufacturers, price ranges, fuel economy rating). Factors such as tyre load, speed and vehicle acceleration are also considered to cover various driving characteristics. The collection phase focuses on separating tyre wear particles from potential
To define a test procedure that will provide repeatable measurements of a vehicle’s maximum acceleration performance for launch and passing maneuvers and standardize time zero used in reported results.
Centralization of electrically driven hydraulic power packs into the body of aircraft has increased attention on the noise and vibration characteristics of the system. A hydraulic power pack consists of a pump coupled to an electrical motor, accumulator, reservoir, and associated filter manifolds. In previous studies, the characteristics of radiated acoustic noise and fluid borne noise were studied. In this paper, we focus on the structure-borne forces generated by the hydraulic pump characterized through blocked force measurements. The blocked force of the pump was determined experimentally using an indirect measurement method. The indirect method required operation with part under test fixed to an instrumented receiver structure. Measured operational accelerations on the receiver plate were used in conjunction with transfer function measurements to predict the blocked forces. Blocked forces were validated by comparing directly measured accelerations to predicted accelerations at
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
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
Fatigue design is invariably of prior concern for the automotive industry, no matter of the evolution of the mobility market: at first because carmakers must stay compliant with general structural integrity requirements for reliability, notably applicable to the chassis system, then due to the endless competition for lightweighting in order to mitigate product costs and/or enhance vehicle efficiency. In the past, this key performance was often tackled by basic reference load cases, making use of the simplest signal content, e.g. sinus functions, to practice constant amplitude loads on test rigs and for computations, respectively. Nowadays, full time series coming from proving ground measurements, or any corresponding virtual road load data computations, may be applied to feed complex vehicle computations for virtual assessment and complex test facilities for final approval, under variable amplitude loads. In between, the concept of load spectra (i.e. distribution of amplitudes with
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
Abstract Real-world driving data is an invaluable asset for several types of transportation research, including emissions estimation, vehicle control development, and public infrastructure planning. Traditional methods of real-world driving data collection use expensive GPS-based data logging equipment which provide advanced capabilities but may increase complexity, cost, and setup time. This paper focuses on using the Google Maps application available for smartphones due to the potential to scale-up real-world driving data logging. Samples of the potential data processing and information that can be gathered by such a logging methodology is presented. Specifically, two months of Google Maps driving data logged by a rural Michigan resident on their smartphone may provide insights on their driving range, duration, and geographic area of coverage (AOC) to guide them on future vehicle purchase decisions. Aggregating such statistics from crowd-sourcing real-world driving data via Google
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