Energy-saving speed profile planning for a connected and automated electric bus considering motor characteristic

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 448, P. 141721 - 141721

Published: March 8, 2024

Language: Английский

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A unified theory of acceptance and use of technology and fuzzy artificial intelligence model for electric vehicle demand analysis

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 11, P. 100455 - 100455

Published: April 4, 2024

This study aims to reveal consumers' intention purchase Electric Vehicles (EVs) based on the Unified Theory of Acceptance and Use Technology (UTAUT) model. A hybrid fuzzy decision-making model with three stages is proposed. First, experts' weights are computed using an artificial intelligence methodology. Second, eight UTAUT-based indicators examined a T-Spherical TOPSIS-based DEMATEL (TOP-DEMATEL) The criteria weighted by multi-SWARA (M-SWARA) Third, evaluation conducted for seven emerging countries considering Spherical Fuzzy (SF) Additive Ratio Assessment (ARAS) technique. main contribution this that new methodology can identify more significant determinants use EVs. methodological integrating theory. findings demonstrate environmental factors play most role in Additionally, performance expectancy also another critical determinant. We find issues should be given importance production process Using fossil fuels while producing these vehicles will significantly reduce users' confidence. phenomenon cause consumers awareness not vehicles.

Language: Английский

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A comprehensive study of various carbon-free vehicle propulsion systems utilizing ammonia-hydrogen synergy fuel

eTransportation, Journal Year: 2024, Volume and Issue: 20, P. 100332 - 100332

Published: April 15, 2024

Language: Английский

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Analyzing the impact of mixed vehicle platoon formations on vehicle energy and traffic efficiencies

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124448 - 124448

Published: Sept. 17, 2024

Language: Английский

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Optimal hybrid strategy in adaptive cruise control system for enhanced autonomous vehicle stability and safety

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 118, P. 109377 - 109377

Published: June 17, 2024

Language: Английский

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An Efficient Coordinated Observer LQR Control in a Platoon of Vehicles for Faster Settling Under Disturbances

World Electric Vehicle Journal, Journal Year: 2025, Volume and Issue: 16(1), P. 28 - 28

Published: Jan. 7, 2025

The rapid proliferation of vehicles globally presents significant challenges to road transportation efficiency and safety, including accidents, emissions, energy utilization, management. Autonomous vehicle platooning emerges as a promising solution within intelligent systems, offering benefits like reduced fuel consumption optimized use. However, implementing autonomous faces obstacles such stability under disturbances, safety protocols, communication networks, precise control. This paper proposes novel control strategy coordinated Kalman observer–Linear Quadratic Regulator (CKO-LQR) ensure platoon formation in the presence disturbances. disturbances considered include movements, sensor noise, delays, with leading vehicle’s movement serving commanding signal. proposed controller maintains constant inter-gap distance between despite utilizing observer estimate preceding movements. A comparative analysis conventional PID controllers demonstrates superior performance terms faster settling times robustness against research contributes enhancing systems.

Language: Английский

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Remaining Useful Life Prediction Method for Highway Electromechanical Equipment Based on Bayesian Algorithm-Optimized CNN-LSTM Model

SAE technical papers on CD-ROM/SAE technical paper series, Journal Year: 2025, Volume and Issue: 1

Published: Feb. 21, 2025

<div class="section abstract"><div class="htmlview paragraph">Efficient maintenance of highway electromechanical equipment is crucial for ensuring reliability within intelligent infrastructure and optimizing the allocation limited resources. Traditional Remaining Useful Life (RUL) prediction models frequently face limitations due to complex dynamic operating conditions such systems, which often hinder their predictive accuracy adaptability. To overcome these persistent challenges, this study introduces an advanced RUL model that integrates a Bayesian-optimized Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) network. Initially, identifies key health indicators effectively represent degradation performance over time. These undergo Spearman correlation analysis determine relevance capacity, only most pertinent features are used input. The CNN-LSTM leverages CNN’s spatial pattern recognition LSTM’s ability process temporal sequences, allowing it accurately capture trends time improve long-term reliability. further enhance accuracy, Bayesian optimization applied adjust model’s hyperparameters automatically, providing efficient, tailored solution aligns unique characteristics operational demands equipment. Validation on CALCE lithium battery dataset demonstrated exceeding 92%, confirming model's feasibility, robustness, strong potential real-world application in system maintenance. provides valuable insights operation, management, It supports strategies scheduling efficiency, extend lifespan critical infrastructure, reduce costs while simultaneously improving overall reliability.</div></div>

Language: Английский

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A comprehensive methodology for developing and evaluating driving cycles for electric vehicles using real-world data

eTransportation, Journal Year: 2025, Volume and Issue: unknown, P. 100409 - 100409

Published: Feb. 1, 2025

Language: Английский

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Longitudinal motion control algorithm for autonomous vehicles taking decisions based on the preceding vehicle behavior pattern

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 4, 2025

In the field of autonomous driving, a key concern is whether driving algorithms can better adapt to their environments. Currently, vehicles often adopt single control strategy, which reduce traffic efficiency and negatively impact other road users. To address this issue, paper presents longitudinal motion algorithm for that makes decisions based on preceding vehicle’s behavior pattern, aiming comprehensively improve both safety. Firstly, using NGSIM dataset, large number kinematic features from highway-driving are extracted standardized. Subsequently, Principal Component Analysis (PCA) applied dimensionality decouple data. Following this, Fuzzy C-Means clustering (FCM) employed categorize vehicles’ characteristics into several typical patterns. By incorporating regulations various countries, external metrics established evaluate results. Based these metrics, parameters optimized enhance reliability outcomes. Additionally, vehicle pattern identification module was developed lightweight Convolutional Neural Network (CNN), achieving high accuracy low computational load in online experiments. Depending different patterns vehicle, we design safety distance model balances efficiency. ensure target following met, Deep Reinforcement Learning (DRL) developed. Finally, comparative experiments conducted, results demonstrate proposed effectively optimizes efficiency, safety, comfort comprehensive manner, thereby verifying its feasibility.

Language: Английский

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Traversability Analysis for Tracked Vehicles in Unstructured Environments: An Approach Employing Vehicle Traversing Capability and Terrain’s Multi-modal Data

Automotive Innovation, Journal Year: 2025, Volume and Issue: unknown

Published: April 26, 2025

Language: Английский

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