Prediction of annoyance evaluations of electric vehicle noise by using artificial neural networks

The indoor-outdoor connection provided by ventilation openings has been so far a limiting factor in the use of natural ventilation (NV), due to the apparent conflict between ventilation needs and the intrusion of external noise. This limiting factor impedes naturally ventilated buildings meeting the acoustic criteria set by standards and rating protocols, which are reviewed in this paper for residential buildings. The criteria reflect a general effort to minimize noise annoyance by reducing indoor sound levels, typically without a distinction based on a ventilation strategy. Research has developed a number of solutions, discussed here, that try to guarantee ventilation without compromising facade noise insulation, but, currently, none have been adopted on a large scale. This concept paper highlights the main limits of the current approach. First, a fragmented view towards indoor environmental quality has not included consideration of the following acoustic criteria: (i) how buildings are designed and operated to meet multiple needs other than acoustical ones (e.g., ventilation, visual, and cooling needs) and (ii) how people respond to multiple simultaneous environmental factors. Secondly, the lack of a perceptual perspective has led acoustic criteria to neglect the multiple cognitive and behavioral factors impinging on comfort in naturally ventilated houses. Indeed, factors such as the connection with the outside and the sense of control over one’s environment may induce “adaptive acoustic comfort” opportunities that are worth investigating. The mere use of different sound level limits would not be enough to define criteria tailored to the complex user–building interaction that occurs under NV conditions. More holistic and human-centered approaches are required to guarantee not only neutrally but even positively perceived indoor acoustic environments. For this reason, this paper considers this apparent conflict from a soundscape viewpoint, in order to expose still unexplored lines of research. By underpinning a perceptual perspective and by contextualizing it, the indoor soundscape approach provides a framework capable of overcoming the limits of the traditional noise control approach. This could provide the opportunity to foster a wider adoption of NV as a passive design strategy that enhances user health and well-being, while enabling low-cost, and low-energy cooling and ventilation, thereby contributing to current climate change challenges.

Acoustic Design Criteria in Naturally Ventilated Residential Buildings: New Research Perspectives by Applying the Indoor Soundscape Approach

Interconnected-energy hubs robust energy management and scheduling in the presence of electric vehicles considering uncertainties

Response surface methodology and artificial neural network analysis of crude palm kernel oil biodiesel production

The sound quality (SQ) and sound perception assessments of electric vehicles (EVs) clearly differ from those of conventional internal combustion engine vehicles (ICEVs). Therefore, it is essential to describe and evaluate the SQ of EVs. To evaluate the SQ in EVs, it is necessary to organize evaluators for conducting subjective jury tests, which are time-consuming and labor-intensive. In addition, the evaluation results are subject to the evaluators themselves and other external interferences. With the advancement of machine learning and artificial neural networks (ANNs), this problem can be well solved. This paper outlines a model for SQ estimation in EVs based on a genetic algorithm-optimized back propagation artificial neural network (GA-BP ANN). Moreover, the correlation between the physical-psychoacoustical parameters and the subjective SQ estimations obtained from the jury tests was investigated in this study. It was found that the GA-BP ANN SQ model has many advantages in comparison with the multiple linear regression (MLR) model in terms of precision and generalization. In addition, this method is ready to be applied for rapidly evaluating the SQ in EVs without jury tests, and it can also be of high significance in dealing with the acoustical designs and improvements of EVs in the future.

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Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks

Influence of an artificially produced stationary sound of electrically powered vehicles on the safety of visually impaired pedestrians

In today's urban environment inhabitants are permanently exposed to elevated noise levels, which are mostly dominated by traffic noise. The current electrification of vehicles might affect the traffic noise in city centers. The aim of this work was to determine the pedestrian reaction, the annoyance and the warning effect of electric vehicle sounds. For this purpose the differences in the perceived annoyance, warning effect and detection time were investigated with perception studies. Furthermore the sound level of a full speed-scaling of an approaching vehicle starting from 0 km/h at the critical distance is nearly 10 dB below the level of a constant speed of 10 km/h. Therefore variants of electric vehicle sounds were generated, in which a constant level is used below 5 or 10 km/h. The results show that the change of the speed-scaling influences the detection time enormously. In this study, Artificial neural network (ANN) is used as an indexing tool to imitate subjective perceptions, because in some furt...

Evaluation of electric vehicle sounds and new concepts regarding speed-dependency

Evaluation of Multiple Electric Vehicle Sources and New Concepts Regarding Speed-Dependency

In today’s urban environment inhabitants are permanently exposed to elevated noise levels, which are mostly dominated by traffic noise. The current electrification of vehicles might affect the traffic noise in cities. The aim of this work was to determine the pedestrian reaction, the annoyance and the warning effect of electric vehicle sounds. For this purpose the differences in the perceived annoyance, warning effect and detection time were investigated with perception studies. Furthermore the sound level of a full speed-scaling of an approaching vehicle starting from 0 km/h at the critical distance is nearly 10 dB below the level of a constant speed of 10 km/h. Therefore variants of electric vehicle sounds were generated, in which a constant level is used below 5 or 10 km/h. The results show that the change of the speed-scaling influences the detection time enormously. In this study, artificial neural network (ANN) is used as an indexing tool to imitate subjective perceptions, because in some further wo...

Lisa Steinbach

Papers

Prediction of annoyance evaluations of electric vehicle noise by using artificial neural networks

Influence of an artificially produced stationary sound of electrically powered vehicles on the safety of visually impaired pedestrians

Evaluation of electric vehicle sounds and new concepts regarding speed-dependency

Evaluation of Multiple Electric Vehicle Sources and New Concepts Regarding Speed-Dependency

Evaluation of electric vehicle sounds, new concepts regarding speed-dependency and prediction of the warning effect