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Urbanization in addition to employ of green technology has boosted the use of electric vehicles in the cities. Since electric vehicles are free from exhaust gases, there would be an improvement in environmental conditions; however, an unintentional upcoming risk is observed due to the silent feature of electric vehicles. Similarly, the rising trend of accidents causes major injury or even permanent disability which is strongly associated with human health and global safety. This study attempts to address three extensive objectives. Is human health risk associated with the silent nature of electric vehicles from the pedestrian's gender perspective? Is the safety of pedestrians depending upon the distance traveled by them? How the age group is responsible for any fatal or accidents? Quantitative primary data were collected from 401 pedestrian respondents through an extensive questionnaire survey carried out in Mumbai, India. The hypothesis is evaluated to justify the human health risk associated with the low noise of electric vehicles based on their technical response. In addition to ANOVA, a nonparametric tool was used to analyze perceived risk in the sense of human health. Utilizing the ground level essential information obtained from the road users, the authors have investigated the key risk associated with electric vehicles. The results showed that nominal/severe accidents may cause injury to pedestrians and thereafter it leads to temporary or permanent disability; thereby it is a foremost important condition for road users to identify the presence of electric vehicles while walking on the road. The study indicates that human safety is closely linked to the safe travel of pedestrians. The quiet nature of electric vehicles are difficult for a pedestrian to identify their presence on the road which is an alarming situation for the possibility of accidents, however, there is no significant difference between the people (road users) who travel a short or long distance in a day. It is the foremost requirement to recognize the level of consequence in the context of accidents to improve the global health.

Not only pollution concerns, but also the rising prices of fuel used in conventional vehicles are enforcing people to make use of electric vehicles. In spite of numerous advantages such as pollution-free, noiseless smooth drive; there may be the new possibility of difficulties occurring due to the quiet nature of electric vehicles (EVs). To examine this, it was intended to conduct an extensive questionnaire survey with 398 driver participants to acquire technical data at Mumbai Metropolitan Region (MMR). The analysis used a well-proven driver behaviour questionnaire (DBQ) built on a six-point scale to statistically evaluate driver behaviour responses to perceived risk. This study aims to evaluate the perceived risk encountered by drivers that influence road safety by considering age group, driving experience, and gender. A systematic ANOVA approach was employed to evaluate the significant factors. The results show that the perceived risk is different based on the gender of the driver, especially when parking the vehicle (p=0.000, F=10.11716>Fcrit). The moderate difficulty level for identifying the presence of electric vehicles is present in almost all situations; however, no significant difference was recorded based on gender, age, and driving experience in the rest of the cases. The outcome of the proposed work would be useful while designing the safety policy for electric vehicles.

In the industry, ball bearings are the most widely used machine element. The ball materials may differ in various bearing applications. Wear of the ball and recess after a period of use is the most common cause of ball bearing failure. The present study aims to develop the artificial neural network model for assessing the wear of different ball bearing materials. A wear test method has been followed as suggested by the ASTM-G99 standard. The pin on disc apparatus was selected to conduct numerous trials. L9 array was considered to design the experiments. The factors considered for this study were load, time, and sliding speed. Based on the results obtained, ANN code was proposed to evaluate wear using numerous test parameters. The results obtained from the proposed model are nearly similar to experimental results, which would be evidence for the correctness of the model. The proposed neural network model can be used in numerous applications with given parameters.

The key features of the electric vehicle (EV) from urban area emphasizes on clean air as well as quiet running nature to preserve atmospheric pollution conditions in the city, however, the new public health risk is arising due to calm running environment of electric vehicles. Since the electric vehicles run at low noise levels, pedestrians being troubled in road traffic accidents which tend to focus on the safety of the pedestrian. The aim of the present study is to analyze the perception of pedestrian’s and driver’s with the quiet nature of electric vehicles by evaluating the data obtained through a questionnaire survey and interviews conducted at the Mumbai Metropolitan Region (MMR), India. A total of 398 drivers of various driving experiences and 401 pedestrians from various locations of MMR had been responded to acquire technical data. A hypothesis is evaluated for public health risk associated with the quiet nature of electric vehicles from the driver’s gender and age perspective. In addition, the ANOVA study was carried out to test the statistical significance of risk with respect to age, gender, vehicle usage, driving experience. The results illustrate that moderate risk is associated with the quiet nature of EV and more attention is required by road users as well as drivers, nevertheless gender (P=0.3321), profession (P=0.6537), driving experience (P=0.8888), vehicle use (P=0.3819) are not significant based on driver’s perception (P value greater than 0.05), whereas driver age group (P=0.0000) is accountable for perceived risk. Similarly, considering pedestrian’s perception, gender (P=0.7954), profession (P=0.8188), distance walk (P=0.2313), location (P=0.3896) are not significant. The outcome of this study recommends the foremost need for the advanced control system in electric vehicles.

Long-term or prolonged noise sensitivity as well as wear particles such as Particulate Matter 10 (PM10) and Particulate Matter 2.5 (PM2.5) emitted from conventional disc brake system has been attributed to a variety of health issues including stress, reduced concentration, decreased productivity at works, and exhaustion from a lack of sleep, among others. Automobile brakes are widely used in city traffic to control the vehicle. Since the use of automotive brake is frequent, the friction and wear exist between the contact pairs of disc brake may cause unnecessary noise level/wear particles. To tackle this, it was intended to develop the smart braking system and evaluate the significance of noise level and wear emission from the newly developed braking system. A noise measuring unit such as a sound intensity meter or decibel (dB) meter with data recording capability, as well as a wear particle measuring device for measurement of PM2.5 and PM10 particles, were used to investigate the noise and wear emission behavior of the proposed braking system. The numerous trials were conducted thoroughly on the test rig for the wear particle analysis and noise analysis. The findings show that the proposed smart brake blending system is very much efficient to reduce noise emission and wear emission during the braking of a vehicle. The outcome from the present study will help develop the noise-free eco-friendly braking system to enhance the human health aspects related to noise and air pollutions.

Purpose Electric vehicles are well known for a silent and smooth drive; however, their presence on the road is difficult to identify for road users who may be subjected to certain incidences. Although electric vehicles are free from exhaust emission gases, the wear particles coming out from disc brakes are still unresolved issues. Therefore, the purpose of the present paper is to introduce a smart eco-friendly braking system that uses signal processing and integrated technologies to eventually build a comprehensive driver assistance system. Design/methodology/approach The parameters obstacle identification, driver drowsiness, driver alcohol situation and heart rate were all taken into account. A contactless brake blending system has been designed while upgrading a rapid response. The implemented state flow rule-based decision strategy validated with the outcomes of a novel experimental setup. Findings The drowsiness state of drivers was successfully identified for the proposed control map and set up vindicated with the improvement in stopping time, atmospheric environment and increase in vehicle active safety regime. Originality/value The present study adopted a unique approach and obtained a brake blending system for improved braking performance as well as overall safety enhancement with rapid control of the vehicle.

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