Browse Topic: Vehicle acceleration
This SAE Standard is equivalent to ISO 362-1:2015 and specifies an engineering method for measuring the noise emitted by road vehicles of categories M and N under typical urban traffic conditions. It excludes vehicles of category L1, L2, L3, L4, and L5. The specifications are intended to reproduce the level of noise generated by the principal noise sources during normal driving in urban traffic. The method is designed to meet the requirements of simplicity as far as they are consistent with reproducibility of results under the operating conditions of the vehicle. The test method requires an acoustical environment that is obtained only in an extensive open space. Such conditions are usually provided for during: Measurements of vehicles for regulatory certification and/or type approval Measurements at the manufacturing stage Measurements at official testing stations Annex A provides background information on the use of this standard consistent with the intent.
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
Electrified powertrains, including Power Splits (Electrically Variable Transmissions), Range Extenders (Series Hybrids), and Electric Vehicles with Disconnect Actuators, offer significant flexibility in managing input actuator acceleration and output torque, drawing power from shared sources. The Hybrid Supervisory Controller (HSC) plays a crucial role in balancing these parameters to meet performance and drivability metrics, yet it often faces challenges under power constraints or sudden high output demands, which can lead to imbalanced control, reduced actuator performance, and unintended vehicle motion. Traditional solutions have typically prioritized one control objective over others, compromising overall system performance. This paper introduces an advanced control strategy that optimally distributes control efforts across multiple actuators with overlapping and conflicting objectives. By resolving these conflicts, the proposed approach ensures system stability and enhances
Model-Based Systems Engineering (MBSE) enables requirements, design, analysis, verification, and validation associated with the development of complex systems. Obtaining data for such systems is dependent on multiple stakeholders and has issues related to communication, data loss, accuracy, and traceability which results in time delays. This paper presents the development of a new process for requirement verification by connecting System Architecture Model (SAM) with multi-fidelity, multi-disciplinary analytical models. Stakeholders can explore design alternatives at a conceptual stage, validate performance, refine system models, and take better informed decisions. The use-case of connecting system requirements to engineering analysis is implemented through ANSYS ModelCenter which integrates MBSE tool CAMEO with simulation tools Motor-CAD and Twin Builder. This automated workflow translates requirements to engineering simulations, captures output and performs validations. System
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
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
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
Items per page:
50
1 – 50 of 2384