Device usage in operating rooms consumes significant electricity, resulting in high hospital expenditure and emissions. Prior studies have evaluated institutional policy approaches toward this problem, but there is limited analysis of individual devices' energy consumption in US operating rooms. This study's objective was to quantify the energy expenditure of equipment commonly used in the operating room, serving as a foundation for future sustainability efforts. The 29 most commonly identified operating room devices at a tertiary academic medical center were categorized as structural (built into operating rooms) or procedural (brought in for procedures). Each device's electrical use was quantified in kilowatt-hours using standard technical ratings and estimated use times. Cost calculations using 2024 electrical prices were scaled to estimate those in average (7 operating rooms, as per literature) and large (69 operating rooms, as per literature) size hospitals. An average size hospital spends $19,207 annually on operating room equipment electricity; larger institutions spend around $189,327 annually. Stand-alone suction devices, x-ray generators of C-arm x-ray machines, and heated air devices were the highest energy consumers. Procedural devices accounted for 64% of total annual electrical costs. Annual electricity costs for operating room equipment at a large hospital equal that of 5 school buildings; an average size hospital equates to a warehouse. Stand-alone suction devices at a large hospital draws the equivalent as 5,120 electrical cars; even turning them off for 1 hour per day can save $4,650.56 per year. Incremental reforms in operating room equipment utilization can substantially reduce hospital expenses and carbon footprint.

Time to adopt a segmented approach to accelerate electric car adoption in emerging economies? Evidences from Indian metro cities

Renewable energy technology usage and environmental security: Public perception and governance's role in Hail, Saudi Arabia

The scaling effects make energy supply a fundamental challenge for achieving autonomy in insect-scale robotics. Inspired by the multifunctionality of biological tissue, dual-function structural-electrochemical integration based on a microfluidic aluminum-air battery (MFAAB) paves a promising path toward energy autonomy in insect-scale robotics. However, suboptimal mono-surface anode utilization and restricted microfluidic transport dynamics of conventional AABs accelerate passivation and byproduct accumulation, hindering output performance. Here, we proposed and developed a centimeter-size MFAAB with dual reaction surfaces for anode, and decompose byproduct accumulation via F-. Owing to the novel structural configuration and electrolyte optimization, it achieves a high capacity of 2697.05mAh/gAl. The developed MFAABs demonstrate operational capacities of tens of milliwatts, successfully powering LEDs, DC motors, and electric toy car. We demonstrate the first successful integration of Al-air batteries with insect-scale robots through a structural-electrochemical co-design framework, harnessing energy weight proportion of 51.38%, achieving dual energy-storage and electromechanical actuation functions for self-sustaining operation. The integrated system simultaneously serves as a body-conforming structural power source while exhibiting 2.56-fold and 1.85-fold higher endurance than a commercial lithium-polymer (401015) and two series-connected alkaline button batteries (LR44), respectively.

A distinct obstacle to mainstreaming sustainability is when strategic actions to integrate sustainability create tensions between a firm’s established identity and its projected image. This study examines how such strategy-image tensions are managed. Based on a qualitative, longitudinal study of luxury sports car manufacturer Porsche during the introduction of electric cars, the analysis traces the discursive process of implementing strategic actions that challenge the legacy product. The findings suggest the reconciliation of strategy-image tensions is a three-stage process: detaching the image from the legacy product, constructing a positive narrative around the new product, and endorsing the fit of the new product category to the corporation. The study contributes to research on incumbent change and managing identity-related tensions. It further contributes to the literature on sustainability mainstreaming with a critical discussion of the ambivalent dynamics that arise when established firms attempt to make sustainability “fit” their legacy.

ABSTRACT Air pollution is one of the major environmental issues worldwide. Individuals can contribute by adopting eco-friendly practices, such as reducing energy consumption, using public transportation, and adopting electric cars (EC). However, the adoption of EC has been a challenge, and this study investigates the role of consideration of future consequences (CFC) in shaping consumers’ intention to purchase. It examines how CFC influences key attributes such as problem awareness, outcome efficacy, personal norms, and consumers' attitudes towards EC, using a sample of 491 individuals and structural equation modelling. The findings reveal a significant and positive relationship between CFC and problem awareness, and it also positively influences outcome efficacy, personal norms and attitude. The study provides insights for marketers and policymakers, emphasising the importance of leveraging consumers’ future-oriented thinking to accelerate the adoption of EC and thereby mitigate the environmental risks associated with air pollution.

When it comes to developing a strategy for EV charging networks, improving the utilization and planning of charging stations becomes critical with the fast growing market for electric cars. Improvement of power quality has the largest effect on the performance and dependability of EVs essential for their large-scale implementation. Issues like low power quality and very high THD present in conventional systems are addressed in this research through a two-stage EV charger consisting of an active power factor correction (PFC) front-end and an LLC resonant DC-DC converter, achieving an input power factor of 0.98, THD of 5% and efficiency of 95%. Applying Support Vector Regression (SVR) as the type of a supervised machine learning algorithm, we employ a predictive maintenance model which processes real-time information and decreases downtimes by a third. Based on the identified shortcomings of ordinary diode bridge rectifiers, this approach extends these advantages and enhances the functionality of EV chargers dramatically.

As electric vehicle systems grow, so does the need for more advanced energy control tools. This paper suggests a new DC/DC power link that works both ways for electric cars with more than one battery. This makes it easier for energy to move from the batteries to the goods inside the vehicle. The suggested system makes it easier to recover, charge, and release renewable energy in both directions. By keeping an eye on voltage, temperature, and charge levels, the complex control system improves battery performance and power flow. This makes sure that each battery works well with the others, uses less power, and is more reliable and efficient. This method gives us a flexible and scalable way to make fast and efficient electric cars (EVs).

Environmental protection is currently receiving significant attention, particularly in the context of transport's impact on the environment. Limited fossil resources, climate change, and global warming are driving the automotive industry toward more efficient and sustainable solutions. These challenges are driving car manufacturers to adopt new technologies and alternative drive systems. Examples of such vehicles include electric vehicles (EVs) and hybrid vehicles (HEVs or PHEVs). The impact of operating these modes of transport on the emission of pollutants other than exhaust gases is crucial. Examples of such emissions include particulate matter generated by brake and tire wear. All vehicles, whether conventionally or alternatively powered, generate such emissions during operation, regardless of their drive type. These particulate matter enter the air and can pose a threat to the environment and human health. While it may seem that electric cars may emit less particulate matter from their braking systems due to the frequent use of recuperation, tires remain a significant source of emissions. The article included measurements of dust emissions during the operation of an electric vehicle and a conventionally driven vehicle, as well as studies of the elemental composition of particles using scanning electron microscopy, analyzing dust collected from the vehicle's surroundings, braking system and tires

Abstract A new bioinspired Battery Thermal Management System (BTMS) for electric cars is presented in this work. It is based on the microstructure of the wings of the Morpho didius butterfly. The thermal performance of a conical frustum fin design was assessed using transient thermal and CFD simulations in three different materials: graphite, copper, and aluminium. The biomimetic casing is combined with machine learning (ML) and deep learning (DL) models, such as XGBoost, Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Linear Regression, in contrast to previous methods, to replace iterative simulation cycles. With a prediction accuracy of R 2 = 0.994 and RMSE = 0.098 °C, the CNN model was able to predict temperature gradients and convective heat flux under a variety of design conditions. The AI-driven system achieved design optimization speed improvements by decreasing repeated CFD computations through the replacement of about 50 iterative CFD design evaluations with less than 10 validation simulations after training the surrogate model. 3D plots, hybrid heatmaps, and residual maps are examples of advanced visualisation approaches. This work creates a scalable basis for applications in next-generation thermal systems by utilising CNN-enabled surrogate modelling for the first time on a Morpho-inspired EV case.

For electric cars and energy storage devices to be more dependable, secure, and simple to maintain, it’s crucial to possess the ability to precisely forecast the State of Health (SoH) and Lithium-ion battery Remaining Useful Life (RUL). Typical Structured battery data is a good fit for machine learning models. However, they don’t always recognize how things evolve over time. Models for deep learning, like Long Short Term Memory (LSTM) networks, learn sequential patterns well, but they need a lot of data and processing power. This study shows a hybrid prediction system which combines XGBoost and LSTM models to use feature-based learning and temporal dependency modeling to guess the battery life. SoH. Experimental evaluations are performed using publicly available NASA lithium-ion battery discharge datasets. We use the Leave-One-Battery-Out strategy to measure how well the model works using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results show that the proposed hybrid model works better and more consistently than individual models on a number of battery datasets. The predicted SoH also makes it possible to use data to guess RUL, which is useful for real-world battery health monitoring. Keywords— Lithium-ion battery; State of Health (SoH); Remaining Useful Life (RUL); XGBoost; Long Short-Term Mem ory (LSTM); Hybrid machine learning model; Battery health prediction

As urban transportation evolves, shared e-mobility is increasingly recognized as a socio-technical system shaping urban equity, social inclusion, and mobility behaviour. However, existing platforms often lack multimodal integration and user-centric adaptability, limiting their ability to address diverse and behaviourally heterogeneous travel preferences. This study proposes a cloud-based shared e-mobility platform integrating docking electric cars, e-bikes, and e-scooters with large language model assistance for natural-language preference interpretation. The system enables human-centred decision-making through lexicographic multi-objective route optimization. The platform is evaluated using 500 expert-annotated queries, assessing both preference alignment and optimization performance. Results show that gpt-4.1 achieves the highest semantic alignment score (0.928) and best route quality measured by a Mean Optimality Gap of 18.62%. To jointly capture alignment and optimization performance, we introduce the Combined Alignment–Performance Metric, under which gpt-4o achieves the highest score (1.0594). Optimization experiments demonstrate high system robustness under varying traffic conditions, as key metrics such as travel time ( p = 0 . 771 ), risk ( p = 0 . 341 ), and walking distance ( p = 0 . 153 ) show no statistically significant differences. Furthermore, the platform exhibits significant scalability, where increasing e-hub density from 20 to 100 stations reduced the mean travel time from 1771 ± 497 s to 955 ± 343 s ( p < 0 . 001 ). These findings contribute to interdisciplinary transportation research by linking optimization with human-centred mobility analysis, offering actionable insights for equity-aware urban planning, inclusive mobility system design, and policy development supporting adaptive and sustainable transport systems. • Cloud platform enables multimodal routing across e-bikes, e-scooters and e-cars. • LLMs translate user travel preferences into lexicographic optimization priorities. • Eight LLMs benchmarked for preference alignment and routing performance. • Compact models achieve comparable route quality with faster response time. • Denser e-hub networks significantly reduce average multimodal travel time.

Mapping innovation pathways and future potential of electric cars: Insights from patent data analysis

With the growing demand for high-performance, low-maintenance, and dependable braking solutions, contactless braking technologies have emerged as a focal point of interest across modern transportation and industrial sectors. The Electromagnetic Braking System (EMBS) stands out as a particularly viable option, capable of producing braking force without any physical mechanical contact between components. Conventional friction-based brakes are prone to wear and tear, thermal degradation, and recurring maintenance needs. In contrast, electromagnetic braking operates on fundamental electromagnetic principles — specifically Faraday's Law of Induction and Lenz's Law. The braking effect is achieved by generating eddy currents within a spinning conductive element, which in turn produces an opposing force that decelerates the rotating part. This paper presents a comprehensive analysis of electromagnetic braking from the standpoint of mechanical engineering. The discussion encompasses the underlying working principles, system architecture, component selection criteria, governing mathematical formulations, torque-speed characteristics, and both the merits and constraints of this technology. Furthermore, it illustrates how performance parameters such as rotor velocity, magnetic flux density, and the electrical conductivity of the material collectively influence braking effectiveness. The study also examines real-world applications of electromagnetic braking across several domains, including high-speed rail systems, electric and hybrid automobiles, industrial equipment, and vertical transport systems such as elevators. Although the system demonstrates strong performance at moderate to high speeds, its effectiveness diminishes considerably at very low speeds, which currently prevents it from serving as a complete substitute for conventional braking mechanisms. In conclusion, electromagnetic braking is most effectively deployed as a complementary or hybrid braking solution in conjunction with traditional systems — particularly within advanced electromechanical platforms and the evolving landscape of electric mobility

This study presents an integrated framework for assessing the interdependent impacts of urban mobility, land use, walkability, and air pollution on environmental outcomes in New York City. It offers a holistic approach to understanding how urban form and transportation choices shape air quality patterns. We evaluated optimized routes across five scenarios (Car, Electric Car, Transit + Active, Multimodal Baseline, and Multimodal Complete Streets) under alternative time-emissions weightings. The analysis demonstrates that multimodal transport scenarios consistently outperform car-dominated alternatives: while the introduction of electric vehicles and congestion-driven speed improvements contributes to lower emissions (−47.5%), more integrated modes of transportation provide greater benefits in combined optimizations for time and emissions (up to −91.3%)—reduction by an idealized policy horizon under a complete streets framework. The study demonstrates the value of evaluating these dimensions jointly rather than in isolation and offers a replicable framework for planners and policymakers to support targeted interventions.

This study investigates the thermal performance of n‐octadecane, a phase change material (PCM), in a Li‐ion battery cell through novel “N” shape fin configuration and the incorporation of Multi‐Walled Carbon Nanotubes (MWCNTs). The thermal management efficiency was assessed based on temperature variations, liquid fraction, energy storage, entropy, and heat generation. Adding 1% v/v. MWCNTs lowered the peak temperature to 301.9 K, indicating improved thermal conductivity. Energy storage results showed that the energy accumulation process centers on PCM melting: as the battery temperature rises, heat transfers to the PCM, which absorbs it until it melts and then dissipates it via natural convection. Enhancing PCM with MWCNTs improves thermal performance, allowing for greater energy absorption and faster battery cooling. Heat generation was highest at 53,347 W/m 3 for pure n‐octadecane but reduced to 21,996 W/m 3 in the eight‐fin configuration, and surface heat flux improved from −10.75 to −18.67 W/m 2 . These findings demonstrate that structural modifications and advanced materials substantially enhance thermal management performance, optimizing battery efficiency and longevity in energy storage applications. The proposed battery thermal management system (BTMS) shows significant promise for applications such as electric cars and renewable energy storage systems that require rapid charging and discharging.

It is noted that in electric vehicles, i.e., electric cars, increased loads most often occur on components that either operate in high traction/braking modes or bear impact and thermal effects, such as recuperation. One of the highly loaded components of an electric vehicle (EV) is its braking system. This is especially characteristic of the combined braking mode, which includes regenerative and mechanical braking. The reasons why the braking system of an electric vehicle requires increased attention are considered. Among the most important of these are intensive operating modes, high cyclicity, the hybrid nature of braking (including regenerative braking and mechanical braking), electromechanical risks, fault tolerance issues, and wear processes of brake components. Six features of the braking processes of electric vehicles are identified and described in detail. The article examines the calculation features of an electric vehicle's braking system and its main components, presenting a number of calculation formulas for determining the energy, thermal, and other brake characteristics. An algorithm that can be implemented using the electric vehicle's onboard microelectronics is presented. It is noted that the onboard computer checks the main parameters. If the monitored parameters are within acceptable limits, a message is displayed on the driver's display confirming the vehicle's operability. Otherwise, further testing and diagnostic work is required.

The climate protection goals and regulatory mechanisms of the world’s leading economic powers are accelerating the transition towards electric cars. The most important cornerstone of the successful transition to electric based mobility and product sale is that the value expected and the value perceived by the customer are matched. For understanding these values, it’s unavoidable to examine that what kind of factors are influencing the decision making process of the potential e-car buyers. This study focuses on the influencing factors examined in the Visegrad (V4) countries with the methodology of the systematic literature review (SLR). The research question is: “What are the key factors affecting consumers’ decisions to purchase electric vehicles in Hungary, Poland, the Czech Republic, and Slovakia?” Beside of the research question, the following hypothesis is validated: “The same influencing factors play a role in the electric car purchasing decisions in the Visegrad countries.” The analysis also use the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) as theoretical lens for analyzing customer decision making process. Based on the research results, it can be concluded that in the Visegrad countries, the electric car purchase process is primarily influenced by the purchase price, the inadequacies of the charging infrastructure and the insufficient range of electric vehicles. Environmental awareness is present in the decision-making process, but it plays only secondary role. Despite the fact that there are numerous differences (e.g. country size, GDP, e-car purchase support policy, purchasing power parity etc.) between the Visegrad countries. As having conclusion, the observations enable us to formulate hypotheses regarding the key influencing factors of electric car purchase decision making process. It also helps us to understand these influencing factors and based on them, all of the stakeholders like car manufacturers and dealers, political decision makers, local governments etc. could modify their strategies and take actions to promote the transition to electric vehicles.

Abstract: Electric cars are rapidly gaining adoption as an eco-friendly alternative to traditional cars, making battery health a key factor for performance and reliability. Over time, battery efficiency decreases due to repeated charging cycles, continuous usage, and varying environmental conditions. Monitoring battery health is essential to maintain optimal performance and extend battery life. This project presents a web-based system for predicting battery health using machine learning techniques. Users can provide simple inputs such as battery age, charge cycles, daily usage, and charging type through an interactive interface. The backend, developed using Flask, processes the data and communicates with a trained Random Forest model. The model analyzes the inputs and predicts battery health as a percentage. The results are displayed using visual elements like charts and battery indicators for better understanding. The system also provides insights into future battery degradation trends. This helps users make informed decisions about battery maintenance and usage. Overall, the solution offers a practical, efficient, and user-friendly approach for battery health monitoring in electric cars. Keywords: Electric Cars, Battery Health Prediction, Machine Learning, Random Forest, Web Application

Getting battery numbers right helps electric cars run safer and work better. Still, old-school techniques fall short when batteries act unpredictably or wear out differently. A new approach uses smart algorithms to guess SoC, SoH, and RUL more reliably. Instead of one model alone, it mixes two types - one sees patterns across data points, another tracks changes over time. Now here comes a twist - attention mechanisms team up with Bayesian methods