Guiding Attention via a Cognitive Aid During a Simulated In-Hospital Cardiac Arrest Scenario: A Salience Effort Expectancy Value Model Analysis

Objective To investigate the effect of a cognitive aid on the visual attention distribution of the operator using the Salience Effort Expectancy Value (SEEV) model. Background Cognitive aids aim to support an operator during the execution of a task. The effect of cognitive aids on performance is frequently evaluated but whether a cognitive aid improved, for example, attention distribution has not been considered. Method We built the Expectancy Value (EV) model version which can be considered to indicate optimal attention distribution for a given event. We analyzed the eye tracking data of emergency physicians while using a cognitive aid application versus no application during a simulated in-hospital cardiac arrest scenario. Results The EV model could fit the attention distribution in such a simulated emergency situation. Partially supporting our hypothesis, the cognitive aid application group showed a significantly better EV model fit than the no application group in the first phases of the event, but a worse fit in the last phase. Conclusion We demonstrated that a cognitive aid affected attention distribution and that the SEEV model provides the means of capturing these effects. We suggest that the aid supported and improved visual attention distribution in the stressful first phases of a cardiopulmonary resuscitation but may have focused attention on objects that are relevant for lower priority goals in the last phase. Application The SEEV model can provide insights into expected and unexpected effects of cognitive aids on visual attention distribution and may help to design better artifacts.

A Fault Warning Method for Electric Vehicle Charging Process Based on Adaptive Deep Belief Network

If an accident occurs during charging of an electric vehicle (EV), it will cause serious damage to the car, the person and the charging facility. Therefore, this paper proposes a fault warning method for an EV charging process based on an adaptive deep belief network (ADBN). The method uses Nesterov-accelerated adaptive moment estimation (NAdam) to optimize the training process of a deep belief network (DBN), and uses the historical data of EV charging to construct the ADBN of the normal charging process of an EV model. The real-time data of EV charging is obtained and input into the constructed ADBN model to predict the output, calculate the Pearson coefficient between the predicted output and the actual measured value, and judge whether there is a fault according to the size of the Pearson coefficient to realize the fault warning of the EV charging process. The experimental results show that the method is not only able to accurately warn of a fault in the EV charging process, but also has higher warning accuracy compared with the back propagation neural network (BPNN) and conventional DBN methods.

Analysis and Improvement Measures of Driving Range Attenuation of Electric Vehicles in Winter

A great many EVs in cold areas suffer from range attenuation in winter, which causes driver anxiety concerning the driving range, representing a hot topic. Many researchers have analyzed the reasons for range attenuation but the coupling mechanism of the battery as well as the vehicle and driving conditions have not been clearly estimated. To quantitatively investigate the driving range attenuation of electric vehicles (EVs) during winter, an EV model mainly integrated with a passenger-cabin thermal model, battery model, and vehicle dynamic model was constructed and simulated based on the mass-produced Wuling HongGuang Mini EV. Real vehicle dynamic driving data was used to validate the model. Based on NEDC driving conditions, the driving range calculation formula and energy flow diagram analysis method were used. The reason for attenuation was evaluated quantitatively. Results show that battery energy loss and breaking recovery energy loss contribute nearly half of the range attenuation, which may be alleviated by battery preheating. Suggestions for extending driving range are proposed based on the research.

Influence Mechanism of Electromechanical Parameters on Transient Dynamics of FWD-EVs: Part I: Co-Modeling of IWM and Vehicle System

In-wheel motor (IWM), as an ideal power source of independent four-wheel drive electric vehicles, has been paid more and more attention due to its high-power density, low starting current, wide speed adjustment range, simple control system and robustness. However, the electromechanical issue is enlarged in both longitudinal and vertical because of in-wheel driven scheme. In this paper, the electromagnetic multi-field characteristic of IWM is investigated based on Fourier series method. The negative vibration coupling on vehicle dynamics is discussed by proposing a conjoint electromechanical FWD-EV model. Results shows that the motor incentive coupled with the vehicle system in multi-degree of freedom, caused the body and wheel resonance in the low speed, meanwhile deteriorated the anti-rollover capability of the IWM-EV in the high speed.

Analysis of Scenarios for the Insertion of Electric Vehicles in Conjunction with a Solar Carport in the City of Curitiba, Paraná—Brazil

The growing environmental impact and rising emission of greenhouse gases have accelerated the research toward renewable energy sources and electric vehicles since one of the main sources of pollution is the CO2 emissions produced by conventional combustion vehicles. This article presents the analysis of the energy balance between a photovoltaic carport with 4.89 kWp installed capacity and an EV, model Renault Fluence ZE DYN, driven in real conditions. The driving tests were performed during the winter season in the city of Curitiba, the capital of the state of Paraná, Brazil, with approximately 1.7 million inhabitants and 1.1 million vehicles. During the test period, we attempt to reproduce the citizen’s daily routes through the city, presenting an average consumption of 15.75 kWh/100 km. The carport PV module’s energy generation and in-plane incident irradiation were acquired to calculate the performance ratio, making a comparison after cleaning maintenance possible. The solar carport system has 4.89 kWp and has generated an average of 465.37 kWh during its 24 months of operation. The analysis scenarios consist of replacing part of the city’s combustion vehicle fleet with the EVs (the same as used in the study) and thus determining how many replicas of the presented photovoltaic systems might be needed, as well as the area required for the installations. In a simulation with 15% of the fleet’s replacement, it would be necessary to generate 17,151.8 MWh, which requires the construction of 36,856 carports, covering an area of approximately 1,105,685 m². Finally, an economic comparison between an internal combustion vehicle and the EV determined that the expenditures involving electric energy to charge the batteries are 3.3 times lower than buying gasoline, assuming the same driving routines.

Sliding Mode-Based Slip Control of Compact Electric Vehicle Truck for Varying Load and Yaw Rate

Vehicle stability is a critical problem, especially for compact electric vehicle (EV) trucks, owing to the impact of the cargo weight and cornering characteristics. In this study, this problem was approached by mathematically formulating the change in the understeer characteristics of an EV truck as variable mass understeer gradient (VMUG) according to the vehicle cargo weight to design the reference yaw rate without the need to consider cornering stiffness. Comparison was made with the conventional methods by applying the VMUG-based slip control while simulating the yaw rate and side-slip tracking performance of the compact EV model for normal loading and overloading conditions. The simulation results demonstrate the superior performance of the proposed method compared to the existing methods. The proposed method has the potential for application for stability enhancement in non-electric and general-purpose vehicles as well.

Design of a one kilowatt wireless charging system for electric vehicle in line with Bharath EV standards

Emerging technologies in an electric vehicle (EV) had greater advancement in the control, batteries and electric motors design. But safety and reliability are the major concerns when the consumer is dealing with a high voltage conductor for charging. The recurring of plugging in the switch for charging is an undeniable disadvantage. Therefore, to eliminate the human intervention in charging of a battery, wireless power transfer (WPT) will be the most effective methodology to charge the EV. This paper aims at building the prototype of 1 kW inductive WPT with high frequency supply with power converters. To design this system, standards of EV charging systems are incorporated; also, a suitable coil structure is identified for the given EV model as per the standards. The mismatch or misalignment of the receiver coil, air gap between receiver–transmitter (i.e., proximity) and compensation techniques are considered in this work. Efficient design of power electronic converter is implemented for both transmitter and receiver side. Both coil design model and the power electronic system are integrated to test the performance of proposed WPT technology.

Understanding Human Decision Making in an Interactive Landslide Simulator Tool via Reinforcement Learning

Prior research has used an Interactive Landslide Simulator (ILS) tool to investigate human decision making against landslide risks. It has been found that repeated feedback in the ILS tool about damages due to landslides causes an improvement in human decisions against landslide risks. However, little is known on how theories of learning from feedback (e.g., reinforcement learning) would account for human decisions in the ILS tool. The primary goal of this paper is to account for human decisions in the ILS tool via computational models based upon reinforcement learning and to explore the model mechanisms involved when people make decisions in the ILS tool. Four different reinforcement-learning models were developed and evaluated in their ability to capture human decisions in an experiment involving two conditions in the ILS tool. The parameters of an Expectancy-Valence (EV) model, two Prospect-Valence-Learning models (PVL and PVL-2), a combination EV-PU model, and a random model were calibrated to human decisions in the ILS tool across the two conditions. Later, different models with their calibrated parameters were generalized to data collected in an experiment involving a new condition in ILS. When generalized to this new condition, the PVL-2 model’s parameters of both damage-feedback conditions outperformed all other RL models (including the random model). We highlight the implications of our results for decision making against landslide risks.

Road surface influence on electric vehicle noise emission at urban speed

Considering the relative quietness of electric motors, tyre/road interaction has become the prominent source of noise emission from Electric Vehicles (EVs). This study deals with the potential influence of the road surface on EV noise emission, especially in urban area. A pass-by noise measurement campaign has been carried out on a reference test track, involving six different road surfaces and five electric passenger car models in different vehicle segments. The immunity of sound recordings to background noise was considered with care. The overall and spectral pass-by noise levels have been analysed as a function of the vehicle speed for each couple of road surface and EV model. It was found that the type of EV has few influence on the noise classification of the road surfaces at 50 km/h. However, the noise level difference between the quietest and the loudest road surface depends on the EV model, with an average close to 6 dBA, showing the potential effect of the road surface on noise reduction in the context of growing EV fleet in urban area. The perspective based on an average passenger EV in a future French or European electric fleet is addressed.

Model to assess workload of village doctors in the National Essential Public Health Services Program in six provinces of China

Background No studies, particularly quantitative analyses, have been conducted regarding the workload of village doctors in the National Essential Public Health Services (NEPHS) program and differences in service delivery by village doctors, according to region and services. In this study, we developed a quantitative analysis approach to measure the workload of NEPHS provided by village doctors in six provinces of China in 2016. We aimed to identify areas and services of the NEPHS needing improvement, so as to implement targeted measures to ensure adequate delivery of NEPHSs in rural remote underserved areas. Methods Based on survey data from 300 town hospital centers (THCs) located in 60 counties in the six selected provinces, we calculated village doctors’ share of workload under the NEPHS using the equivalent value (EV) model. To define the workload and corresponding EV of each NEPHS, a series of five meetings was held with THC managers, public health workers, family physicians, nurses and village doctors. Field observations were conducted to verify the workload and EV of each service. Results Village doctors’ share of the workload under the NEPHS program was 43.71% across the 300 sampled THCs in six provinces. The village doctors’ workload shares for different NEPHS ranged from 17.14 to 57.00%. The percentage workload undertaken by village doctors under the NEPHS program varied across different provinces, with the highest proportion 63.4% and the lowest 28.5%. Conclusions The total NEPHS workload assigned to village doctors by THCs in the six sampled provinces exceeded the Chinese government’s requirement of 40%, but the workload proportion in some provinces was less than 40%. In addition, the percentage workload for some NEPHS undertaken by village doctors was lower than others. We suggest conducting district-level analysis of the workload among village doctors under the NEPHS program using the EV method, to identify areas and services needing improvement, to implement targeted measures to expand and promote health service provision in China’s rural underserved areas.