Electric Drivers Research Articles

Geographic Factors Impacting the Demand for Public EV Charging: An Observational Study

The practicality and substitutability of electric vehicles depend on there being a fast, reliable way to recharge on round trips beyond the range of a single charge. Grouping such infrastructure into charging hubs benefits developers and operators through economies of scale and electric vehicle drivers in terms of travel logistics and passed-through cost savings. The need for charging capacity at en-route charging hubs is impacted by the following four identifiable geo-social parameters: (a) highway travel volumes, reflecting the rate at which electric vehicles are expending energy in the area; (b) local population, reflecting both the increased needs of electric vehicle owners without dedicated home chargers and the reduced needs of those commuting into a metropolitan center; (c) the quantity of competing charging stations; and (d) being on a critical interprovincial route. Twelve charging stations located in diverse locations around Nova Scotia, Canada, were evaluated in terms of these four parameters, and their recorded use was investigated from a dataset of 26,000 charging events between April 2022 and April 2024. The regression reveals that there are strong positive correlations between demand for fast charging and (a) traffic volumes (45%) and (c) being on an interprovincial route (42%), while there is only a very weak correlation with (b) local population (2%). Interestingly, there is only a weak negative correlation with (c) the number and capacity of nearby competing chargers (−6%), suggesting that either in short-term route choice or longer-term vehicle choice, the presence of chargers encourages electric vehicles.

Vehicle-to-grid as a competitive alternative to energy storage in a renewable-dominant power system: An integrated approach considering both electric vehicle drivers' willingness and effectiveness

Vehicle-to-grid (V2G) technology, which enables bidirectional power flow between electric vehicles (EVs) and power grids, is a possible solution for integrating EVs and renewable energy (RE) into the power system. While EV drivers are indispensable components for the V2G applications, the extant power system studies have underexamined the willingness of EV drivers to participate in V2G. In addition, few studies investigate whether the small-scale and time-varying V2G supply can replace utility-level energy storage. Given this background, this study combined discrete choice experiments with energy storage capacity expansion planning (ES-CEP) to simulate time-varying V2G participation and effectiveness. The choice experiments identified drivers’ preferences for V2G, which was used to simulate V2G participation under different compensation schemes. Time-varying V2G participation was integrated into ES-CEP for renewable-dominant power system to assess the potential of V2G to replace ES at the utility scale. The results showed that most drivers were unlikely to engage in V2G, although this reluctance decreased during the night. Higher guaranteed state-of-charge, accessible V2G facilities, and monetary enrollment compensation encouraged the participation. The ES-CEP results showed that, even with a limited V2G (4.5–15.3 % of EV drivers), V2G significantly reduced the optimal ES capacity (36.5–45.6 % for power and 39.6–60.6 % for energy), shaved peak netload by 18.5 %, and saved the total cost by 3.35 %, even after accounting for V2G enrollment compensation and battery degradation. EVs that charged during the daytime and participated in V2G at night were the most utilized, resulting in 0.92 % of annual battery aging, but generated a profit of 636,749 KRW (553 USD). The findings of this study underscore the importance of a suitable compensation scheme for V2G and supplementary measures for incentivizing EV charging during off-peak hours, as these elements are essential for the successful implementation of V2G.

Privacy-preserving estimation of electric vehicle charging behavior: A federated learning approach based on differential privacy

With the popularity of connected electric vehicles, the openness and sharing of charging data between stakeholders allows a more accurate estimation of charging behavior, which is valuable for optimizing energy systems and facilitating travel convenience. However, to enable such an effective mechanism, the challenge of data security and privacy should be addressed. Federated learning in the vehicular network is appealing for utilizing individual vehicle data while preserving data privacy. We propose an improved local differential privacy-based federated learning approach for modeling charging session prediction problems while preserving user privacy against the threat from a honest-but-curious server. In this approach, all vehicles, within the coordination of a cloud server, collaboratively establish a global regression network through parameter exchange. Meanwhile, the servers may belong to third-party model owners and can be semi-honest when inferring private information on the collected model parameters. Hence, local differential privacy is adopted to perturb the parameters. Additionally, a combination of local and global models via elastic synchronization is proposed to improve the accuracy of the learned noisy global model. Through the test on a real data set, the results show the superiority of the proposed algorithm over traditional noisy federated learning methods. Furthermore, the practical value of the proposed method is validated with a real-world charging case. Such an accurate charging session prediction service for electric vehicle drivers facilitates charging and travel convenience in the green transportation world.

Assessment of echocardiographic indicators of electric train drivers who have been ill with a new coronavirus infection

Assessment of echocardiographic indicators of electric train drivers who have been ill with a new coronavirus infection

Sizing Methodology of Dynamic Wireless Charging Infrastructures for Electric Vehicles in Highways: An Italian Case Study

The necessity of pushing the road mobility towards more sustainable solutions has become of undeniable importance in last years. For this reason, both research and industry are constantly investigating new technologies able to make the usage of battery electric vehicles(BEV) as accessible and usable as traditional internal combustion engine vehicles (ICEV). One of the most limiting issues concerns the short range of electric vehicles, which complicates their use for long distances, such as for highway travels. A promising solution seems to be the “charge-while-driving” approach, by exploiting the inductive dynamic wireless power transfer (DWPT) technology. Nevertheless, such systems show different issues, first of all, high investment and maintenance costs. Furthermore, it is not clear how extensive a potential dynamic wireless charging infrastructure needs to be to make a real advantage for electric vehicle drivers. As a consequence, the aim of this paper is to introduce a new methodology to estimate the number and length of wireless charging sections necessary to allow the maximum number of electric vehicles to travel a specific highway without the need to stop for a recharge at a service area. Specifically, the methodology is based on a algorithm that, starting by real traffic data, simulates vehicle flows and defines the basic layout of the wireless charging infrastructure. This simulator can provide a decision support tool for highway road operators.

Maximizing the number of satisfied charging demands of electric vehicles on identical chargers

This paper addresses the electric vehicle charging scheduling problem in a charging station with a limited overall power capacity and a limited number of chargers. Electric vehicle drivers submit their charging demands. Given the limited resources, these charging demands are either accepted or rejected and accepted demands must be satisfied. The objective of the scheduler is to maximize the number of satisfied demands. The paper provides theoretical results on the scheduling problem and proposes different linear programming models and heuristic methods to provide good-quality solutions in a shorter computational time.

Myotonometry in machinery operators and its relationship with postural ergonomic risk.

To analyze the association between occupational ergonomic risk, personal characteristics, and working conditions with the biomechanical properties of stiffness and muscular tone in the paravertebral muscles of electric pallet jack and forklift operators in the industrial sector. A total of 75 industrial sector machine operators were evaluated in 2021. Personal characteristics and working conditions were assessed through a questionnaire. Ergonomic risk was assessed using the Rapid Entire Body Assessment (REBA) method, and biomechanical properties of stiffness and muscular tone were obtained using the Myoton Pro device. Stiffness in paravertebral muscles was compared based on the operated machine and observed ergonomic risk. A multilevel linear regression model was employed to quantify the relationship, with mean differences and 95% CI calculated. Very high ergonomic risk was found in 75% of the electric pallet truck drivers. In this group with the highest ergonomic risk, an association between biomechanical properties and older workers was observed. Additionally, among electric pallet truck drivers, stiffness (mean difference 335.9 N/m, 95% CI: 46.4 (3.4 to 110.0), P < 0.05) and paravertebral muscle tone (mean difference 17.5 Hz, 95% CI: 1.4 (0.1 to 3.4), P < 0.05) showed statistically significant differences in the very high ergonomic risk category compared to the high-risk category. No significant differences were observed in any of the analyzed variables among forklift drivers. Workers operating electric pallet trucks with very high ergonomic risk according to the REBA method and aged over 40 yr are associated with increased muscle stiffness and tone.

Synthesis of Models for Predicting and Diagnosing Occupational Diseases Based on Hybrid Fuzzy Technology

The purpose of research is to develop a method for synthesizing models for predicting and diagnosing occupational diseases based on hybrid fuzzy technology, providing an increase in the quality of decision-making in occupational pathology.Methods. It is established that most of the problems related to the topic under study (forecasting, early diagnosis, assessment of the severity and dynamics of the development of professional diseases) belong to the class of poorly formalized problems with fuzzy and incomplete data structure, which are recommended to be solved using the methodology of synthesis of hybrid fuzzy solving rules based on on the interaction of the natural intelligence of doctors and a cognitive engineer with artificial hybrid intelligence. Using the chosen mathematical apparatus, a method for the synthesis of fuzzy models of forecasting and diagnosis of occupational diseases is proposed.Results. As a concrete example, the problem of predicting and diagnosing ischemic heart disease (CHD) in electric train drivers has been solved with the allocation of such classes of conditions as: "healthy and the appearance of CHD is not expected"; "healthy, but the appearance of CHD is expected after the predicted time"; "early stage of CHD"; "CHD disease has been detected". As a result of expert evaluation, it was shown that the confidence in the correct classification is at the level of 0.9. The same result was confirmed by the results of statistical tests on representative control samples in terms of diagnostic sensitivity and specificity.Conclusion. The proposed method of synthesis of hybrid fuzzy models makes it possible to synthesize hybrid decision rules that improve the quality of prediction and early diagnosis of the studied class of diseases both in the presence of training samples and in their absence by compensating for the lack of statistical material by methods of formalization of clinical thinking. As a concrete example, the problem of predicting and diagnosing ischemic heart disease in electric train drivers has been solved. It is shown that the confidence in the correct classification is at the level of 0.9, which allows us to recommend the results obtained for practical use in occupational pathology.

Investigation on different parameters associated with purchase of electric vehicle in India

The primary characteristics of electric vehicles (EVs) from urban areas place an emphasis on clean air and silent operation to protect the city's atmospheric pollution conditions. Thereby, EVs are coming on the Indian road with great popularity; however, some factors such as charging infrastructure, battery life of EVs are still challenging. Therefore, the present attempt is made to analyse the numerous factors associated with purchase of EVs. To gather technical data; a total of 398 participants with different vehicles such as EV two wheelers, EV four wheelers, IC engine vehicles from different Pune Metropolitan Region areas have replied. The ANOVA was used to determine the statistical significance of purchase of EVs in relation to gender, vehicle use, profession and driving experience. Furthermore, hypothesis has been checked to assess the significance of different parameters associated with purchase of EVs in India. The results of hypotheses testing show that the purchase of electric vehicle is significantly different according to EV owner’s gender. The Z-Score is 3.89391. The p-value is 0.0001. The result is significant at p < 0.05. Subsequently, the choice of electric vehicle is not unlike according to driving experience of electric vehicle drivers. TheZ-Score is 0.29779. Thep-value is 0.76418. The result isnotsignificant atp < 0.05. Furthermore, Electric vehicles are more popular among Indian people irrespective of distance of travel as thez-score is 0.00582. Thep-value is 0.99202. The result isnotsignificant atp < 0.05. The outcome of the study will be helpful for EV manufacturers for getting popularity and selling of vehicle according to demand.

Qualitatively and quantitatively explore injury severity of light motor vehicle drivers involved in heavy goods vehicle crashes

ABSTRACT Crashes involving heavy goods trucks (HGVs) are of significant concern as it poses a higher risk of fatality to light motor vehicles (LMVs). The study constructs three Deep Forest models with different Cascade structures to explore the relationship between HGV-LMV crash severity and risk factors. Based on the HGV-LMV crash data in Shandong province, China, motorcycles, electric vehicles, and sedans are defined as the LMV. According to the comparison results, the Deep Forest with Cascade LightGBM is significantly better. Through model interpretability tools, the study found that motorcycle and electric vehicle drivers aged 58 to 86, and LMV drivers with 1 to 3 years of driving experience are more likely suffering severity and fatal injury (SFI) in HGV-LMV crashes. And, disobey traffic sign, illegal turning, overtaking, changing lane, and crashes happened on non-motorway, national and provincial roads have an positive effect on SFI.

Prediction of Electric Vehicle Mileage According to Optimal Energy Consumption Criterion

In the field of electric vehicle usage, an inherent challenge lies in the restricted mileage capacity prior to requiring a recharge, hindering broader acceptance of electric vehicles. To alleviate this concern, enhancing the comprehension of vehicle energy consumption and range plays a pivotal role in easing the anxieties of electric vehicle drivers. Within this context, a novel model-based predictive approach is introduced for estimating electric vehicle energy consumption. This method considers the vehicle's specific parameters, the road network's topology, and actual traffic conditions. Through the macro model of electric vehicle energy consumption, real-time summary data can be extracted using conventional map-based web services. By representing the road network as a weighted directed graph tailored to the energy consumption model, an algorithm aids in mileage optimization by determining the optimal path for immediate use. The resultant motion range from this approach offers improved precision and dependability in contrast to conventional strategies based on average consumption and distance.

Can app notifications reduce charging hogging? An experiment with electric vehicle drivers

Electric vehicles (EVs) have the potential to contribute to urban environments’ sustainability. However, charging hogging (e.g., leave the vehicle attached at charging stations beyond full charge) can limit the EV market, generate issues for local authorities and electricity providers, and hinder the experience of drivers. This study investigated experimentally whether different app notifications (involving loyalty points, reserved parking, and social norm messages) can encourage drivers in the Netherlands to move their vehicle when fully charged, involving 703 EV drivers of a high-end car manufacturer. Logistic regressions showed that offering reserved parking and sending social norm notifications both increased the intention to move vehicles after charge, among both fully electric and plug-in hybrid vehicle drivers. For fully EV drivers, receiving loyalty points also increased the intention to move the vehicle. These findings indicate that providing reserved parking, sending social norm notifications and, partially, offering loyalty points, might be effective systems to reduce hogging.

Developing a decentralized community of practice-based model for on-demand electric car-pooling towards sustainable shared mobility

Presently, majority of car-pooling services depend on a central third party which makes these platforms susceptible to data privacy, security concerns, and a single point of failure. Moreover, drivers and passengers are charged with different services fees by the electric mobility service provider. The emergence of emerging technologies such as Distributed Ledger Technologies (DLT) can foster trust, boost electric car-pooling business models. Hence, DLT is utilized to store electric car-pooling trips, drivers, and passengers’ information to ensure user privacy and maintain security. Similarly, social practice theories such as Community of Practice (CoP) is progressively considered as a significant structure within societies as it aids the development and sharing of resources across groups. But very little attention has been devoted to examined how CoP can be employed to support the design of decentralized on-demand electric car-pooling. Leveraging CoP and DLT, this paper proposes a decentralized community of practice-based model that enables drivers to publish electric car-pooling services and passengers to be matched to a driver without depending on a trusted third party. A systematic literature review was adopted to collect data and a case study of a decentralized on-demand electric car-pooling was presented. Findings from this study highlights conceptualization of CoP for improving decentralized on-demand electric car-pooling and provide insights on efficient decentralized mechanisms for electric car-pooling. Theoretically, this article identifies the current problems, state-of-the-art of decentralized electric car-pooling. For policy implications this study provides guidelines to effectively govern and manage the development of on-demand electric car-pooling for sustainable public transportation.

Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand

Electric vehicles have emerged as one of the top environmentally friendly alternatives to traditional internal combustion engine vehicles. The development of a comprehensive charging infrastructure, particularly determining the optimal locations for charging stations, is essential for the widespread adoption of electric vehicles. Most research on this subject focuses on popular areas such as city centers, shopping centers, and airports. With numerous charging stations available, these locations typically satisfy daily charging needs in routine life. However, the availability of charging stations for intercity travel, particularly on highways, remains insufficient. In this study, a decision model has been proposed to determine the optimal placement of electric vehicle charging stations along highways. To ensure a practical approach to the location of charging stations, the projected number of electric vehicles in Türkiye over the next few years is estimated by using a novel approach and the outcomes are used as crucial input in the facility location model. An optimization technique is employed to identify the ideal locations for charging stations on national highways to meet customer demand. The proposed model selects the most appropriate locations for charging stations and the required number of chargers to be installed, ensuring that electric vehicle drivers on highways do not encounter charging problems.

Multi-Criteria assessment of the user experience at E-Vehicle charging stations in Germany

We propose an assessment framework for the user experience at public electric vehicle charging stations in Germany, which enables a structured analysis from a driver’s perspective. The assessment criteria were derived from 20 interviews with battery electric vehicle drivers and corroborated by existing literature. Seven criteria are considered as particularly relevant: ease of use, green power, comfort, functionality, price, price transparency, and availability. Using Saaty’s Analytic Hierarchy Process approach, the weights assigned to these criteria were determined in an online survey with 108 drivers of battery electric vehicles. The results indicate the particular importance of availability and functionality. The assessment framework includes value scores that indicate the match between specific charging stations and the assessment criteria. The conceptual framework can help to support operators of public charging points and electric mobility providers in the design of their services and products, as well as guide policy-makers.

Identifying charging zones to allocate public charging stations for electric vehicles

In recent years, several techniques have been presented in the specialized literature to identify the best location for installing public charging stations, considering the demand for charging the batteries of electric vehicles that travel along the main roads in urban areas. However, in cities with different travel patterns for electric vehicle drivers, such consideration may result in charging equipment serving few electric vehicles during high recharging demand. Thus, planning by zone can determine more chargers per station with greater utilization during the operation of charging stations. For this, this work presents a methodology that uses the concept of a charging zone, defined as a circular geographic space with charging equipment that will satisfy the charging demand of electric vehicles in their surroundings. Identifying the centroids of these zones is formulated as a p-median problem, solved by the Teitz-Bart algorithm to provide more coverage to the demands for electric charging in large urban areas. The proposed methodology was applied in a Brazilian city with approximately 3 million inhabitants to find the spatial distribution of public charging zones, considering six scenarios of the global penetration of electric vehicles. Furthermore, we compared the allocation of the centroids in these zones with the solution determined by commercial geoprocessing software. This comparison shows that the proposal determines a spatial distribution of 10% more of these zones with a load factor closer to 0.5 than the results of the commercial geoprocessing software. More zones with this load factor value contribute to better use of the power distribution network installations. In addition, the proposed methodology finds an average reduction of 319 kW of peak demand in regions with a low flow of electric vehicles to meet their charging needs in each analyzed scenario. This peak-demand reduction may allow less investment in connecting future charging stations to the power distribution network. Therefore, the proposed methodology can help public and private agents to disseminate electric mobility, finding public charging areas with greater utilization of chargers per station during the operation of charging stations.

Feasibility of electric two and three-wheelers in Africa

Due to the less stringent emission technology requirement, motorized two and three-wheelers (2&3W) generate more pollutants than cars. About 20% of the world's registered motorcycles are known to be in Africa. Vehicle pollution is one of the major causes of death and health problems in Africa. The electrification of transportation provides an opportunity for E2&3W in Africa. To assess this transition, this study quantifies the number of E2&3W present in some African countries. Surveys of electric vehicle start-ups, drivers, and owner experiences are used to determine the E2&3W's technical feasibility and ability to compete with petrol scooters (p-scooters). SimaPro 9.4 software together with Ecoinvent 3.8 database was used to conduct a cradle-to-the-grave analysis of the environmental impact of using electric scooters (e-scooters). The research found that Africa's E2&3W's have a 0.2% market share compared to gasoline versions. The main disadvantage of e-scooters is their limited range and battery life. The average range and speed are 50 ​km and 50 ​km/h, respectively. Overloading of E2&3W caused damage to traction motors and was of major concern to distributors. The main advantages of E2&3W are their low operating costs and low environmental impact. In South Africa, the total environmental impact of e-scooters outweighed that of p-scooters. Emissions that have a direct impact on human health, were significantly lower for e-scooters than for p-scooters. The lack of battery performance standards, battery swapping station standards, and charging station standards negatively affects the quality of imported e-scooters. African countries need to implement electric vehicle standards, and battery recycling policies, and establish electric vehicle training and research centers.

Electric taxi licensing under a tradable credit scheme: model and application

ABSTRACT Taxi market modeling has long attracted researchers’ and practitioners’ attention, with a variety of models presented to investigate the effects of different market regulation regimes. Motivated by the increasing interest in and theoretical advantages of tradable credit systems for mobility management, this study investigates the use of a tradable credit scheme for taxi licensing. A mixed integer non-linear programming problem is formulated to determine the credit price, daily charge and number of vehicles in order to maximize social welfare. In the social benefit-maximizing solution, the supply of conventional taxis is exhausted, while about 50% of the available electric taxi drivers enter the market. Sensitivity analysis shows that increasing the validity period of the credits, reducing the daily system fee for electric taxis or subsidizing the purchase of electric taxis could lead to greater social benefits by increasing the profit margin of electric taxi drivers and incentivizing their wider adoption.

Online parameters identification and state of charge estimation for lithium-ion batteries based on improved central difference particle filter

Accurate SOC is of great significance to alleviate the endurance anxiety of electric vehicle drivers. To improve the accuracy of SOC estimation, a method of combining FFRLS and GPR for parameter identification and an improved particle filter for lithium battery SOC estimation are proposed. The proposed collaborative parameter identification method uses the forgetting factor recursive least squares (FFRLS) to identify the parameters in the middle and high SOC region. In the low SOC region, the parameters identified by the FFRLS are used as the training data of Gaussian process regression (GPR) for model parameter prediction to improve the parameter identification accuracy in the low SOC region; in the SOC estimation stage, the central difference Kalman filter is used to generate the importance density function to overcome the particle degradation, and the step size h of the central difference is calculated suboptimal to further improve the prediction accuracy. Finally, the proposed method is compared with other methods under different working conditions and different temperatures, and the effectiveness and adaptability of the proposed method are verified.

Pragmatic and idealistic reasons: What drives electric vehicle drivers' satisfaction and continuance intention?

Pragmatic and idealistic reasons: What drives electric vehicle drivers' satisfaction and continuance intention?