Review of life cycle assessment for automobiles: A meta-analysis-based approach

Oda, Hiromu; Noguchi, Hiroki; Fuse, Masaaki

doi:10.1016/j.rser.2022.112214

Cited by 20 publications

(4 citation statements)

References 63 publications

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“…However, predicting the lifespan of capacitors is a challenging task due to the labor-intensive nature of lifespan data collection, as it consumes significant time and cannot yield large amounts of lifespan data [12]. Therefore, predicting the lifespan of capacitors faces challenges related to data quality and availability, feature selection, and engineering [13]. In recent years, the use of machine learning for predicting the performance of electrical devices and materials has garnered increasing attention from researchers worldwide due to its superiority in prediction accuracy [14], time efficiency, and cost-effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Approach for Failure and Life Prediction of Electronic Components under Accelerated Life Stress

Qiu,

2024

Electronics

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Researchers worldwide have been focusing on accurately predicting the remaining useful life of electronic devices to ensure reliability in various industries. This has been made possible by advancements in artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies. However, accurately forecasting device life with minimal data sets, especially in industrial applications, remains a challenge. This paper aims to address this challenge by utilizing machine learning algorithms, specifically BP, XGBOOST, and KNN, to predict device reliability with limited data. The remaining life dataset of electronic components is obtained through simulation for training and testing the algorithms, and the experimental results show that the algorithms achieve a certain level of accuracy, with the error rates being as follows: BP algorithm, 0.01–0.02%; XGBOOST algorithm, 0.01–0.02%; and KNN algorithm, 0–0.07%. By benchmarking these algorithms, the study demonstrates the feasibility of deploying machine learning models for device life prediction with acceptable accuracy loss, and highlights the potential of AI algorithms in predicting the reliability of electronic devices.

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Section: Introductionmentioning

confidence: 99%

Neural Network-Based Approach for Failure and Life Prediction of Electronic Components under Accelerated Life Stress

Qiu,

2024

Electronics

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“…Life Cycle Assessment (LCA) is a methodology that helps researchers and practitioners quantify the potential impacts of a good or a service throughout its entire life cycle. LCA has been used extensively for private road transport [7], and a few have been developed over the years to use ammonia as a fuelling vector. Angeles et al [8] compared the nitrogen and carbon footprint of various fuels assessed whilst employed in propulsion systems (i.e.…”

Section: Introductionmentioning

confidence: 99%

Environmental assessment of road transport fueled by ammonia from a life cycle perspective

Boero

Mercier

Mounaïm‐Rousselle

et al. 2023

Journal of Cleaner Production

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“…As each powertrain has its advantages and disadvantages, it is important to use a suitable powertrain combination for the correct application to achieve a medium-to longterm reduction in the environmental footprint. Previous studies have reported the use of various powertrains as substitutes for conventional ICVs, contributing to the reduction of CO 2 emissions [7,[10][11][12] in addition to reducing atmospheric pollutants, such as particulate matter (PM) [13,14]. For their utilization, the practical performance, such as the driving range and acceleration performance under real-world driving conditions, is important, along with an accurate estimation of their energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Examining Real-Road Fuel Consumption Performance of Hydrogen-Fueled Series Hybrid Vehicles

Naganuma,

Sakane

2023

Energies

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The use of hydrogen fuel produced from renewable energy sources is an effective way to reduce well-to-wheel CO2 emissions from automobiles. In this study, the performance of a hydrogen-powered series hybrid vehicle was compared with that of other powertrains, such as gasoline-powered hybrid, fuel cell, and electric vehicles, in a simulation that could estimate CO2 emissions under real-world driving conditions. The average fuel consumption of the hydrogen-powered series hybrid vehicle exceeded that of the gasoline-powered series hybrid vehicle under all conditions and was better than that of the fuel cell vehicle under urban and winding conditions with frequent acceleration and deceleration. The driving range was longer than that of the battery-powered vehicle but approximately 60% of that of the gasoline-powered series hybrid. Regarding the life-cycle assessment of CO2 emissions, fuel cell and electric vehicles emitted more CO2 during the manufacturing process. Regarding fuel production, CO2 emissions from hydrogen and electric vehicles depend on the energy source. However, in the future, this problem can be solved by using carbon-free energy sources for fuel production. Therefore, hydrogen-powered series hybrid vehicles show a high potential to be environmentally friendly alternative fuel vehicles.

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Review of life cycle assessment for automobiles: A meta-analysis-based approach

Cited by 20 publications

References 63 publications

Neural Network-Based Approach for Failure and Life Prediction of Electronic Components under Accelerated Life Stress

Neural Network-Based Approach for Failure and Life Prediction of Electronic Components under Accelerated Life Stress

Environmental assessment of road transport fueled by ammonia from a life cycle perspective

Examining Real-Road Fuel Consumption Performance of Hydrogen-Fueled Series Hybrid Vehicles

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