Browse Topic: Vehicle acceleration
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
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
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
This research aims to develop an inverse controller to track target vibration signals for the application to car subsystem evaluations. In recent times, perceptive assessments of car vibration have been technically significant, particularly parts interacting with passengers in the car such as steering wheels and seats. Conventional vibration test methods make it hard to track the target vibration signals in an accurate manner without compensating for the influence of the transfer function. Hence, this paper researched the vibration tracking system based on inverse system identification and digital signal processing technologies. Specifically, the controller employed a semi-active algorithm referring to both the offline modeling of the inverse system and the adaptive control. The semi-active controller could reconstruct the target vibration signal in a more efficient and safer way. The proposed methodology was first confirmed through computation simulations using Simulink. The
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