Vehicle Users Research Articles

Advancement in Thermoelectric Innovation: Crafting a Portable Climate Control System

Greenhouse gas emissions are on the rise globally due to increasing demands for electricity, vehicle usage, desertification, and inadequate waste management practices. In response to these challenges, a portable heater and cooler utilizing the thermoelectric effect have been developed. This device comprises four thermoelectric Peltier modules, a heat sink with four fans, two batteries, a charge controller, and a solar panel. The four Peltier modules, each capable of 50 watts, are interconnected in series and parallel configurations on the cover of a thermocol box. To ensure effective heating and cooling throughout the workspace, all modules are equipped with heat sinks and fans operating at 12V and 0.28A. To optimize thermal transfer, the Peltier modules are sandwiched between heat sinks using thermal paste, resulting in one side becoming hot and the other cold when power is supplied. These sandwich configurations are placed atop the thermocol box. Testing of the portable cooler and heater was conducted under various load conditions, including no load, 1 liter of water, and 2 liters of water, with batteries, for both cooling and heating. The fully charged batteries can power the entire experimental setup for 21 hours in each scenario. During cooling, the device achieved a minimum working area temperature (thermocol box) of 18.8°C under no load conditions and a minimum water temperature of 18.6°C for 1 liter of water. In heating mode, the experimental setup reached a maximum working area temperature of 42.4°C and a maximum water temperature of 33.3°C for 1 liter of water.

Effect of vehicle speed on injury of child passenger in frontal crashes

Speed is major cause of death to vehicle occupants especially children. Studying effect of speed on child injury severity is crucial to establishing safety regulations. This work investigates the effect of vehicle speed on the severity of injury sustained by three-year-old (3YO) children. Restrained 3YO child dummy Finite Element (FE) model was used to conduct crash simulation at impact speed of 40km/h, 46 km/h and 48 km/h in Ford Taurus FE model using LS DYNA code. Vehicle deceleration was found to increase with increase in speed. Head Injury Criteria (HIC36), HIC15, chest acceleration (CA), chest severity index (CSI), chest deflection (CD) and neck moment (NM) were evaluated and compared with National Highway Traffic Safety Administration (NHTSA) threshold. It was found that the injury parameters sustained by 3YO child increased with increase in speed. HIC36 was found to be above threshold for 48km/h and 46km/h. Speed of 40 km/h had all the injury values below the threshold. Chest deflection was lower than the threshold for all the three speeds. Chest acceleration and neck moment were also presented and their values also increase with speed, though, NHTSA doesn’t capture their threshold. The study disseminates information on the effect of speeding on child injuries in frontal crashes for road safety agents and vehicle designers and users. Limiting vehicle speed is therefore crucial to child occupant protection.

Development and testing of immersive virtual reality environment for safe unmanned aerial vehicle usage in construction scenarios

Robotics and autonomous systems are increasingly being used on construction sites to complete tasks and complement human effort. One such autonomous system is unmanned aerial vehicles (UAV), although their wrong use could be potentially hazardous to humans and the environment. Given its relative novelty in construction, there remains a dearth of knowledge about safety risks associated with their use as well as knowledge on skills and protocols for their safe operation. With construction being one of the most dangerous and accident-prone sectors, there is even more impetus to develop UAV safety competence. In this study, we explore the application of immersive virtual reality (VR) use for purposes of safety training pertaining to the use of UAVs in construction scenarios. Whereas, previous studies, focussed on effectiveness of VR as a tool for training individuals on UAV operations and safety risks in general, this study evaluates its effectiveness across distinctive themes of safety in order to compare its effectiveness in different domains of UAV safety. The study adopts design science for development of bespoke UAV safety training tool in VR using Building Information Modelling (BIM) and game engine-driven virtual prototyping. The immersive training tool was then applied in an experiment of participants (n = 100) with (n = 50) constituting a control group using more traditional training methods and a series of pre/post-test assessments. The findings confirmed the relative superiority of VR training over traditional methods, improving performance and retention by up to 22 % on average. The immersive VR was found to be most effective on participants retention of knowledge in general flight preparation, air traffic documentation, visibility management and warning signals. Further usability tests were performed on all participants (n = 100) revealing general presence and positive attitudes towards VR based UAV safety training when compared to traditional training. From a practice perspective this research proofs the effectiveness of a VR approach as more cost effective and safe approach to UAV training for both academia and practice with added advantage of being more realistic simulation of the UAV risk scenarios.

Enhancing Electric Vehicle Performance with a Hybrid PI-Sliding Mode Controller for Battery Supercapacitor Integration

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system’s feasibility.

Understanding Perspectives on Presbyopia and Use of Pilocarpine HCl 1.25% Twice Daily from Participants of the Phase 3 VIRGO Study.

The phase 3, randomized, vehicle-controlled, 14-day VIRGO study evaluated the efficacy and safety of twice-daily dosing of pilocarpine hydrochloride ophthalmic solution 1.25% (Pilo) in presbyopia. On VIRGO exit, a companion study was conducted to assess the patient experience with presbyopia and satisfaction with Pilo. Recruited individuals completed the Presbyopia Patient Satisfaction Questionnaire (PPSQ) plus a three-part exit survey, or a live interview. The PPSQ evaluated respondents' experience with Pilo. Survey parts 1 and 2 evaluated experience managing presbyopia before and during VIRGO, respectively; part 3 assessed future possibilities of using Pilo in real-world situations. The interview further informed the interviewees' experience with presbyopia and Pilo. The primary endpoint was responders (%) in each rating category of the PPSQ items 1-7; the secondary endpoints were summary of categorical (survey) and qualitative (interviews) responses. The PPSQ and survey included 62 participants who received Pilo (N = 28) or vehicle (N = 34) in VIRGO; the interview included ten participants (Pilo, N = 4; vehicle, N = 6). Per the PPSQ, 64.3% of Pilo users reported vision improvement, including 17.9% with complete improvement; ≥ 46.4% were satisfied/very satisfied with their ability to perform daily activities, see up close unaided, and read in dim light. Among vehicle users, these percentages were 35.3%, 0%, and ≤ 23.5%, respectively. In both subgroups, ≥ 67.9% were interested in using Pilo or Pilo and eyeglasses/contact lenses in the future. Per the interview, vehicle users (n = 6/6) found the eyedrop easy to use but none experienced meaningful near-vision improvements, stopped using other correction method(s) part of the day, were satisfied with the eyedrop, preferred it over their previous correction method(s), or would continue using it if prescribed. Conversely, 75% (n = 3/4) of Pilo users responded positively to each of these six criteria. Findings validate the VIRGO results and improve our understanding of the patient experience, demonstrating improved vision and satisfaction with Pilo (vs. vehicle) when performing daily activities.

Sweet surrender? Changing autonomobile structures of feeling

ABSTRACT Imaginaries of smart, sustainable autonomous vehicles carrying people of all ages and abilities to relax en-route to myriad destinations, evoke a sweet surrender to the machine. But such technocratic visions clash with visions of non-anthropocentric autonomy. The urgency of mobility transformation in the face of ecological overshoot makes this a site of turbulence where new “structures of feeling” are emerging. But how to study changing structures of feeling and apply insights? In this paper we discuss an exploratory study with twelve users of autonomous vehicles, non-users, engineers, and experts in China and the UK who used galvanic skin response sensors to probe their autonomobile emotions. Extending Christian Nold’s Bio Mapping approach, we discuss fissures in values and norms about mobility and the prospect of doughnut mobilities, concluding with reflections on applied mobilities research.

A Branch-and-Price Algorithm for an Integrated Online and Offline Retailing Distribution System with Product Return

This study identifies critical inefficiencies within a dual-channel operation model employed by a fast fashion company, particularly the independent operation of three logistics distribution systems. These systems result in high operational costs and low resource utilization, primarily due to redundant vehicle dispatches to meet the distinct demands of retail store replenishment, online customer orders, and customer return demands, as well as random and scattered return requests leading to vehicle underutilization. To address these challenges, we propose a novel integrated logistics distribution system design and management method tailored for dual-channel sales and distribution businesses. The approach consolidates the three distribution systems into one cohesive framework, thus streamlining the delivery process and reducing vehicle trips by combining retail and customer visits. An optimization algorithm is introduced to factor in inventory and distribution distance, aiming to achieve global optimization in pairing retail store inventory with online customer orders and unifying the distribution of replenishment products, online products, and returned products. The paper contributes to the field by introducing a new variation of the Vehicle Routing Problem (VRP) that arises from an integrated distribution system, combining common VRP issues with more complex challenges. A custom Branch-and-Price (B&P) algorithm is developed to efficiently find optimal routes. Furthermore, we demonstrate the benefits of the integrated system over traditional, segregated systems through real-world data analysis and assess various factors including return rates and inventory conditions. The study also enhances the model by allowing inventory transfers between retail stores, improving inventory distribution balance, and offering solutions for scenarios with critically low inventory levels. Our findings highlight a significant reduction in total operating cost savings of up to 49.9% and vehicle usage when using the integrated distribution system compared to independent two-stage and three-stage systems. The integrated approach enables the utilization of vacant vehicle space and the dynamic selection and combination of tasks, preventing unnecessary mileage and space wastage. Notably, the integration of inventory sharing among retail stores has proven to be a key factor in generating feasible solutions under tight inventory conditions and reducing operational costs and vehicle numbers, with the benefits amplified in large-scale problem instances.

Resolving Complexities in the Integration of Autonomous Vehicle

Smart vehicles become increasingly prevalent, there is a growing need for sophisticated monitoring systems that go beyond basic parameters. Current Smart EV monitoring systems often lack features that address critical aspects such as engine temperature, drunk and drive prevention, and immediate accident response. There is a pressing need for an all-encompassing monitoring system that enhances both vehicle performance and user safety. This project introduces an advanced Smart E-Vehicle Monitoring System designed to offer a comprehensive suite of features, including engine temperature monitoring, drunk and drive testing, accident detection, and remote control via mobile devices. The system not only ensures optimal vehicle performance but also prioritizes user safety through proactive anomaly detection. Keywords: Smart Vehicles, Electric Vehicles, monitoring systems, performance, Engine temperature, mobile devices.

Research on end-route planning for community group purchasing for vehicles with different energy sources

To address the issue of final delivery route planning in the community group purchase model, this study takes into full consideration logistics vehicles of different energy types. With the goal of minimizing the sum of vehicle operating costs, delivery timeliness costs, goods loss costs, and carbon emissions costs, a multi-objective optimization model for community group purchase final delivery route planning is constructed. An improved genetic algorithm with a hill-climbing algorithm is utilized to enhance adaptive genetic operators, preventing the algorithm from getting stuck in local optima and improving the solution efficiency. Finally, a case study simulation is conducted to validate the feasibility of the model and algorithm. Experimental results indicate that currently, among the three types of vehicles, fuel logistics vehicles still have an advantage in terms of vehicle usage cost. Electric logistics vehicles exhibit the poorest performance with the highest cost per hundred kilometers, but their sole advantage lies in their high energy release efficiency, enabling optimal low-carbon vehicle performance. Battery-swapping logistics vehicles perform the best in terms of carbon emissions, combining the advantages of both fuel-based and electric logistics vehicles. Therefore, battery-swapping logistics vehicles are a favorable choice for replacing fuel-based logistics vehicles in the future, offering promising prospects for future development.

Electromobility Stage in the Energy Transition Policy—Economic Dimension Analysis of Charging Costs of Electric Vehicles

The available literature emphasizes that by 2040, electric vehicles may constitute up to 50% of the fleet of all passenger vehicles. This process will be one of the elements of the energy transformation and, at the same time, consistent with the idea of sustainable transport. As part of this research, the actual energy consumption and the range of electric vehicles were determined. This research was carried out using a selected group of electric cars from the most popular segments of passenger cars. The calculations were based on three charging scenarios: a home electrical network, a public alternating current (AC) charging station that allows charging with single-phase alternating current or alternating current, and a public direct current (DC) charging station that allows charging with direct current. The obtained results were compared with the results of cars with internal combustion and diesel engines after driving a 100-kilometer section of the route. In a broader scope, this research addresses the entities responsible for the energy transformation and the electromobility development strategy. In a narrower sense, vehicle users are considering the purchase of this type of vehicle for political, economic and technological reasons.

Economic optimization of fresh logistics pick-up routing problems with time windows based on gray prediction

The rapid development of modern cold chain logistics technology has greatly expanded the sales market of agricultural products in rural areas. However, due to the uncertainty of agricultural product harvesting, relying on the experience values provided by farmers for vehicle scheduling can easily lead to low utilization of vehicle capacity during the pickup process and generate more transportation cost. Therefore, this article adopts a non-linear improved grey prediction method based on data transformation to estimate the pickup demand of fresh agricultural products, and then establishes a mathematical model that considers the fixed vehicle usage cost, the damage cost caused by non-linear fresh fruit and vegetable transportation damage and decay rate, the cooling cost generated by refrigerated transportation, and the time window penalty cost. In order to solve the model, a hybrid simulated annealing algorithm integrating genetic operators was designed to solve this problem. This hybrid algorithm combines local search strategies such as the selection operator without repeated strings and the crossover operator that preserves the best substring to improve the algorithm’s solving performance. Numerical experiments were conducted through a set of benchmark examples, and the results showed that the proposed algorithm can adapt to problem instances of different scales. In 50 customer examples, the difference between the algorithm and the standard value in this paper is 2.30%, which is 7.29% higher than C&S. Finally, the effectiveness of the grey prediction freight path optimization model was verified through a practical case simulation analysis, achieving a logistics cost savings of 9.73%.

Optimal Scheduling of Integrated Energy System Considering Electric Vehicle Battery Swapping Station and Multiple Uncertainties

In recent years, there has been rapid advancement in new energy technologies aimed at mitigating greenhouse gas emissions stemming from fossil fuels. Nonetheless, uncertainties persist in both the power output of new energy sources and load. To effectively harness the economic and operational potential of an Integrated Energy System (IES), this paper introduces an enhanced uncertainty set. This set incorporates N-1 contingency considerations and the nuances of source–load distribution. This framework is applied to a robust optimization model for an Electric Vehicle Integrated Energy System (EV-IES), which includes Electric Vehicle Battery Swapping Station (EVBSS). Firstly, this paper establishes an IES model of the EVBSS, and then proceeds to classifies and schedules the large-scale battery groups within these stations. Secondly, this paper proposes an enhanced uncertainty set to account for the operational status of multiple units in the system. It also considers the output characteristics of both new energy sources and loads. Additionally, it takes into consideration the N-1 contingency state and multi-interval distribution characteristics. Subsequently, a multi-time-scale optimal scheduling model is established with the objective of minimizing the total cost of the IES. The day-ahead robust optimization fully considers the multivariate uncertainty of the IES. The solution employs the Nested Column and Constraint Generation (C&CG) algorithm, based on the distribution characteristics of multiple discrete variables in the model. The intraday optimal scheduling reallocates the power of each unit based on the robust optimization results from the day-ahead scheduling. Finally, the simulation results demonstrate that the proposed method effectively reduces the conservatism of the uncertainty set, ensuring economic and stable operation of the EV-IES while meeting the demands of electric vehicle users.

Taming travel time fluctuations through adaptive stop pooling

Ride sharing services combine trips of multiple users in the same vehicle and may provide more sustainable transport than private cars. As mobility demand varies during the day, the travel times experienced by passengers may substantially vary as well, making the service quality unreliable. We show through model simulations that such travel time fluctuations may be drastically reduced by stop pooling. Having users walk to meet at joint locations for pick-up or drop-off allows buses to travel more direct routes by avoiding frequent door-to-door detours, especially during high demand. We in particular propose adaptive stop pooling by adjusting the maximum walking distance to the temporally and spatially varying demand. The results highlight that adaptive stop pooling may substantially reduce travel time fluctuations while even improving the average travel time of ride sharing services, especially for high demand. Such quality improvements may in turn increase the acceptance and adoption of ride sharing services.

Smart Parking System for Navi-Mumbai Airport

A commercial airport parking system has to deal with many problems such as unnecessary traffic congestion which is mainly caused due to the lack of smart and systematic parking. This eventually causes the time loss of the users while causing unwanted delays in their individual journey. Due to the lack of employees as well as inefficiency in their employment skills, the users again have to face various problems. All these problems ultimately lead to dissatisfaction in the experience of users. Its final capacity will be to handle 90 million passengers. Considering the problems mentioned above, we are providing a designed parking layout which includes a ground floor plan and a typical floor plan with all the basic amenities like washrooms, staircases, waiting areas, elevators, etc. This paper analyses a few parking apps and software and then suggests to build a dedicated app for the airport parking system which will help to counter the problems faced while enhancing the user’s experience. The smart features which should be included in the app are license plate recognition, online parking prepayment, parking guidance, vehicle Detection sensors and automated payment stations, unique code for the parking slot of the user's vehicle. Keywords – applications, airports, mlcp, Smart Parking systems, technologies

TOD effects on travel behavior: A synthesis of evidence from cross-sectional and longitudinal studies

The impacts of transit-oriented development (TOD) on travel behavior have been extensively studied, with a predominant focus on cross-sectional analyses that provide a static evaluation at a specific point in time by comparing TODs and non-TODs. Longitudinal assessments that capture changes in behavior over time remain relatively uncommon, and the literature tends to overlook differences in evaluating TOD effects across cross-sectional and longitudinal analyses. Additionally, the role of trip purpose as a significant but unexplored variable influencing the degree of TOD effects is often disregarded. To address these gaps, this systematic review examines 48 quantitative studies, comparing the effects of TOD on travel behavior from cross-sectional and longitudinal perspectives, restructuring indicators of effects into transit use, non-motorized travel, vehicle dependence, and vehicle ownership, and differentiating the effects by trip purpose. A metric has been introduced to quantitatively assess the impact of TOD on travel behavior. The pooled results indicate that private vehicle usage remains high in TOD areas, particularly for non-commuting trips, and that the longitudinal effects of TOD are limited and potentially influenced by individual travel attitudes, residential self-selection, and long-term travel habit change. Furthermore, the methodological differences between cross-sectional and longitudinal studies may lead to divergent conclusions regarding the effects of TOD on travel behavior. Our analysis sheds light on the importance of carefully selecting an appropriate method for a given research question to maximize the accuracy and relevance of the findings. Combining TOD and shared mobility can create a more efficient multi-model transport network that meets the diverse needs of city residents and improves accessibility for all. Overall, this review provides new insights into the impacts of TOD on travel behavior and supports the potential for a paradigm shift toward multimodal transport through the integration of TOD and shared mobility.

A Comprehensive Analysis of AI/ML-enabled Predictive Maintenance Modelling for Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADAS) are changing driver-vehicle interactions to improve road safety and reduce distractions. Technological advances like ADAS and AI in cars present societal challenges and opportunities. It shows how AI aids human-machine communication by improving motor skills. The auto industry is interested in ADAS because it can increase energy efficiency, safety, and comfort. Numerous studies have shown its benefits. ADAS and vehicle networking show promise, but establishing a sound control system is challenging. Model Predictive Control (MPC) is one answer to these problems. To manage higher-level connectivity and automation, the paper analyses and implements key research. It also finds issues and recommends solutions. The latest driverless car ADAS improvements have dramatically increased passenger safety. These systems are safer and more automated using sensors and ECUs. Most ADAS have RADAR, cameras, ultrasonic, and LiDAR. This work uses AI/ML-enabled Predictive Maintenance modelling to improve ADAS safety and longevity. AI and ML in Advanced Driver Assistance Systems (ADAS) are significant vehicle safety and reliability advances. AI/ML-enabled predictive maintenance detects and fixes ADAS component faults. ADAS predictive maintenance using AI/ML can detect issues, improve driver safety, and boost vehicle efficiency. Advanced sensor arrays and control units are needed for adaptive cruise control, traffic sign recognition, and lane-keeping assistance. AI/ML algorithms discover issues and enable early interventions in predictive maintenance models. Predictive maintenance is examined utilizing classical machine learning, deep learning, and reinforcement learning. Integration of numerous AI/ML models, real-time data processing, customization based on vehicle usage patterns, scalability, and adaptability of predictive maintenance models to new ADAS technologies are research gaps.

An Analysis of Vehicle-to-Grid in Sweden Using MATLAB/Simulink

With more electric vehicles introduced in society, there is a need for the further implementation of charging infrastructure. Innovation in electromobility may result in new charging and discharging strategies, including concepts such as smart charging and vehicle-to-grid. This article provides an overview of vehicle charging and discharging innovations with a cable connection. A MATLAB/Simulink model is developed to show the difference between an electric vehicle with and without the vehicle-to-grid capabilities for electricity grid prices estimated for Sweden for three different electric vehicle user profiles and four different electric vehicle models. The result includes the state-of-charge values and price estimations for the different vehicles charged with or without a bidirectional power flow to and from the electric grid. The results show that there is a greater difference in state-of-charge values over the day investigated for the electric vehicles with vehicle-to-grid capabilities than for vehicles without vehicle-to-grid capabilities. The results indicate potential economic revenues from using vehicle-to-grid if there is a significant variation in electricity prices during different hours. Therefore, the vehicle owner can potentially receive money from selling electricity to the grid while also supporting the electric grid. The study provides insights into utilizing vehicle-to-grid in society and taking steps towards its implementation.

A Hybrid Deep Learning Model Utilizing Cross-Structural Multi-Behavioral Comparative Recommendation for Sustainable Electric Transportation Infrastructure

Ensuring the sustainability of transportation infrastructure for electric vehicles (e-trans) is increasingly imperative in the pursuit of decarbonization goals and addressing the pressing energy shortage. By prioritizing the development and maintenance of resilient e-trans platforms through the optimization of the public charging network, electric vehicle businesses can effectively meet the needs of users, thereby contributing to efforts aimed at improving environmental quality. To achieve this goal, researching the dynamics of vehicle user behaviors plays a crucial role. In this paper, we propose cross-structure multi-behavior contrastive learning for recommendation (C-MBR), which takes into account the dynamic preferences of users, and develops model profiles from the global structure module, local structure module, cross-behavior contrastive learning module, cross-structure contrastive learning module, and model prediction and optimization. C-MBR is mainly designed to learn user preferences from the diversity of users’ behaviors in the process of interacting with the project, so as to grasp the different behavioral intentions of users. The experimental and analytical research is further conducted and validated for dealing with cold start problems. The results indicate that C-MBR has a strong ability to deal with the problem of sparse data. Compared with the ablation experiment, the model performance of C-MBR is significantly enhanced, showing that the C-MBR model can fully apply the information of a global structure and local structure in cross-structure comparative learning and multi-behavioral comparative learning to further alleviate the problem of data sparsity. As a result, the e-trans infrastructure will be significantly enhanced by addressing the issue of data-driven disruption.

Internet of Vehicles (IoV) Based Framework for electricity Demand Forecasting in V2G

The integration of smart grids with Advanced Metering Infrastructure (AMI) has bridged the realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in managing EV Battery Range and charging infrastructure effectively. This paper presents an innovative IoV-based framework tailored for EVs, with a specific focus on forecasting electricity consumption in a Vehicle-to-Grid (V2G) scenario. By exploring the hurdles surrounding electric vehicle usage, the research lays the foundation for Electric Vehicles (EVs). The proposed IoV model optimizes IoT sensor utilization through a fog layer and employs a Back Propagation (BP) neural network for battery State of Charge (SoC) estimation, integrating Principal Component Analysis (PCA) for data dimensionality reduction. Leveraging substantial computing capabilities, the cloud layer predicts Electricity Consumption Data (ECD) associated with EVs in V2G scenarios. Performance evaluation metrics like Akaike Information Criteria (AIC), Mean Error (ME), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are assessed across state-of-the-art forecasting algorithms. Incorporating EV potential into the optimal model reveals a significant 10% reduction in electricity demand. This research advances IoV-based frameworks, offering insights to enhance EV efficiency within the broader energy infrastructure.

Privacy-protecting predictive cache method based on blockchain and machine learning in Internet of vehicles

To solve the privacy leakage problem faced by Internet of Vehicles (IoV) users when enjoying location-based services (LBS), a privacy-protecting predictive cache method based on blockchain and machine learning (BML-PPPCM) is proposed. First, a Bi-directional Long-Short Term Memory (Bi-LSTM) model is used to predict query requests over a future period based on historical request information. The predicted results are recommended to neighbors and broadcast to requestors. Then, deep Q-learning (DQN) is utilized to determine the optimal cache decision. Finally, a trust mechanism is introduced to calculate trust values, and blockchain is used to store transaction data and trust data, preventing malicious tampering by attackers. The simulation results show that BML-PPPCM has a higher cache hit ratio than other similar schemes and performs well in privacy protection and suppression of malicious and incentive denial of service providers.