Electric Vehicle Drivers Research Articles

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.

Flame-retardant composite phase change material with silicone resin and melamine phosphate for battery thermal safety

<p>With the prosperity of electric vehicles (EVs), the thermal management of lithium-ion battery (LIB) is crucial for ensuring the safety of drivers on EVs. Composite phase change material (CPCM) with high latent heat has a great promising prospect in battery thermal management systems (BTMS). However, the thermal management efficiency of CPCM is limited due to the leakage, low thermal conductivity and flammability. Herein, the novel multifunctional CPCM with paraffin (PA), epoxy resin (ER), expanded graphite (EG), methyl MQ silicone resin (MQ) and melamine phosphate (MP) (PEE/MQ/MP3) has been prepared, which can achieve well anti-leakage, high flame-retardant and thermal conductivity, enhancing the thermal safety for battery module. The results reveal that PEE/MQ/MP3 with MQ and MP at a ratio of 1:2 can exhibit optimum flame retardant performance. The total heat release peak, smoke production rate, carbon monoxide production and carbon dioxide production are 169 MJ/m<sup>2</sup>, 0.05 m<sup>2</sup>/s, 0.005 g/s and 0.38 g/s, respectively. The battery module with PEE/MQ/MP3 displays excellent thermal management performance, delaying thermal propagation. Even after ten cycles at a 3 C rate, the maximum temperature is controlled below 50 ��C and the maximum temperature difference is maintained with 5 ��C. Besides, the thermal propagation processes of battery modules reveal that PEE/MQ/MP3 can absorb and transfer heat in the first stage timely and quickly, efficiently suppressing the thermal hazard occurrence. Therefore, this study has proposed a multifunctional flame-retardant CPCM as an effective solution to enhance the thermal safety of battery modules, thus ensuring the safety of EV drivers.</p>

Alleviating range anxiety with ‘Enroute’: A real-time mobile solution and infrastructure innovation for electric vehicle users

Amidst the growing adoption of electric vehicles (EVs), this study addresses the prominent issue of "range anxiety" encountered by EV users when confronted with critical charging situations during their journeys. The research background emphasises the constraints of the existing charging infrastructure, primarily designed for urban areas, leaving EV drivers susceptible to unforeseen charging emergencies in remote or less-travelled regions. To tackle this challenge, an innovative mobile application that provides a real-time solution by harnessing crowd-sourced data and forging partnerships with charging infrastructure providers was developed. This app assists users in locating available charging stations. It offers the option to book a service that can bring the necessary charging equipment to a specified location (similar to a porter service), ensuring timely assistance. The research methodology involved user surveys and data analysis, specifically emphasising seamless integration with EVs’ onboard systems, making support practical and readily accessible. The results demonstrate the effectiveness of this app in alleviating range anxiety and enhancing EV users’ confidence in long-distance travel. In conclusion, this paper outlines a promising solution to a pressing issue in electric mobility. It underscores the importance of addressing the charging infrastructure gap for the widespread adoption of EVs. The future scope of this research involves further refinement of the app, expanded partnerships with charging networks and Porter service providers, and continued collaboration with EV manufacturers to ensure seamless integration and user satisfaction, ultimately advancing the cause of sustainable transportation.

Hamlet: Hierarchical Multi-Objective Reinforcement Learning Method for Charging Power Control of Electric Vehicles With Dynamic Quantity

Electric vehicle (EV) is emerging as an effective choice to reduce carbon emissions in the modern transportation system. However, the large access of EVs brings tremendous passive influence on the stability and performance of power grid operation. Thus, it is valuable to investigate the charging power management method to improve charging experience of EV drivers while maintaining normal bus voltage of the power system. This paper builds the charging power control problem of multiple EVs as a hierarchical Markov decision process (MDP) model and propose a hierarchical multi-objective reinforcement learning method (Hamlet) to obtain the real-time charging power control decisions. Specifically, by treating EVs with the same remaining charging time as the same agent, this paper addresses the dynamic state and action spaces issue and significantly reduce the input dimension of both state and action spaces. Besides, this paper quantifies drivers’ anxiety about the battery volume and distributes the comfort reward over the entire charging interval, which overcomes the delayed comfort reward issue. In order to alleviate the influence of multiple objectives on training stability, this paper develops a multi-objective learning method to dynamically adjust the optimization direction. Simulation results on IEEE 33 bus and 69 bus test feeders prove the validity of the proposed method in mitigating voltage fluctuation, minimizing bill payment and maximizing battery comfort.

Active Battery Cell Balancing by Real-Time Model Predictive Control for Extending Electric Vehicle Driving Range

Active Battery Cell Balancing by Real-Time Model Predictive Control for Extending Electric Vehicle Driving Range

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.

Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance

The continuous advances in intelligent systems and cutting-edge technology have greatly influenced the development of intelligent vehicles. Recently, integrating multiple sensors in cars has improved and spread the advanced drive-assistance systems (ADAS) solutions for achieving the goal of total autonomy. Despite current self-driving approaches and systems, autonomous driving is still an open research issue that must guarantee the safety and reliability of drivers. This work employs images from two cameras and Global Positioning System (GPS) data to propose a 3D vision-based object localization and classification method for assisting a car during driving. The experimental platform is a prototype of a two-sitter electric vehicle designed and assembled for navigating the campus under controlled mobility conditions. Simultaneously, color and depth images from the primary camera are combined to extract 2D features, which are reprojected into 3D space. Road detection and depth features isolate point clouds representing the objects to construct the occupancy map of the environment. A convolutional neural network was trained to classify typical urban objects in the color images. Experimental tests validate car and object pose in the occupancy map for different scenarios, reinforcing the car position visually estimated with GPS measurements.

The Electromagnetic Exposure Level of a Pure Electric Vehicle Inverter Based on a Real Human Body

In order to quantitatively analyze the electromagnetic exposure dose of an inverter in a pure electric vehicle to the driver’s body and assess the safety of the electromagnetic exposure, based on a real human anatomy model in the virtual home project, a real human model with several organs and tissues, including muscles, bones, a heart, lungs, a liver, kidneys, a bladder, a skull, a scalp, white matter, and a cerebellum, was constructed. The inverter of a pure electric vehicle is considered to be the electromagnetic exposure source; for this study, an equivalent electromagnetic environment model composed of a real human body, an inverter, and a vehicle body was built. The distribution of induced fields in the driver’s tissues and organs was calculated and analyzed using the finite element method. The results show that the distribution of the magnetic flux density, induced electric field, and induced current density in the driver’s body was affected by the spatial distance of the inverter. The farther the distance was, the weaker the value was. Specifically, due to the different dielectric properties of the different tissues, the induced field in the different tissues was significantly different. However, the maximum magnetic flux density over the space occupied by the driver’s body and induced electric field in the driver’s trunk and central nervous system satisfied the exposure limits of the International Commission on Non-Ionization Radiation Protection, indicating that the electromagnetic environments generated by the inverter proposed in this paper are safe for the vehicle driver’s health. The numerical results of this study could also effectively supplement the study of the electromagnetic environments of pure electric vehicles and provide some references for protecting the drivers of pure electric vehicles from electromagnetic radiation.

Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach

The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min.

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.

PLUG: A City-Friendly Navigation Model for Electric Vehicles with Power Load Balancing upon the Grid

Worldwide, in many cities, electric vehicles (EVs) have started to spread as a green alternative in transportation. Several well-known automakers have announced their plans to switch to all-electric engines very soon, although for EV drivers, battery range is still a significant concern—especially when driving on long-distance trips and driving EVs with limited battery ranges. Cities have made plans to serve this new form of transportation by providing adequate coverage of EV charging stations in the same way as traditional fuel ones. However, such plans may take a while to be fully deployed and provide the required coverage as appropriate. In addition to the coverage of charging stations, cities need to consider the potential loads over their power grids not only to serve EVs but also to avoid any shortages that may affect existing clients at their various locations. This may take a decade or so. Consequently, in this work, we propose a novel city-friendly navigation model that is oriented to serve EVs in particular. The methodology of this model involves reading real-time power loads at the grid’s transformer nodes and accordingly choosing the routes for EVs to their destinations. Our methodology follows a real-time pricing model to prioritize routes that pass through less-loaded city zones. The model is developed to be self-aware and adaptive to dynamic price changes, and hence, it nominates the shortest least-loaded routes in an automatic and autonomous way. Moreover, the drivers have further routing preferences that are modeled by a preference function with multiple weight variables that vary according to a route’s distance, cost, time, and services. Different from other models in the literature, this is the first work to address the dynamic loads of the electricity grids among various city zones for load-balanced EV routing in an automatic way. This allows for the easy integration of EVs through a city-friendly and anxiety-free navigation model.

Electric vehicle energy consumption modelling and analysis: a Malaysia case study

Previously, the Electric vehicle (EV) range anxiety greatly hindered EV adoption. The fear of being stranded due to running out of battery becomes the main concern among EV drivers. This critical issue can be solved by deploying a bigger battery in EVs. However, deploying bigger size battery contributes to the higher price of the EV itself. Hence, the optimal design of the EV powertrain is a critical factor in ensuring EV meets the users’ needs and, simultaneously, can be owned by the potential owners at an affordable price. Accurate assessment of EV energy consumption is crucial in designing an optimal EV powertrain, particularly for developing energy-efficient driving techniques in both manual and autonomous driving. One method is to perform a real-life experiment to determine the EV energy consumption, which is expensive and time-consuming. An alternative to real-world testing is creating an EV energy consumption model with the existing driving cycles. This paper studies the parameters contributing to EV energy consumption based on EV driving cycles available in MATLAB Simulink. The findings of this study will provide insight into the factors that influence EV energy consumption and serve as a guideline in EV powertrain design.

Dynamic impedance model based two‐stage customized charging–navigation strategy for electric vehicles

AbstractWith the continuously increasing penetration of electric vehicles (EVs), the mutual match between the distribution of charging resources and the spatial–temporal distribution of EV charging demands is becoming increasingly important. To address this, this paper proposes a novel two‐stage customized EV charging–navigation strategy. Building on previous research on the real‐time information from dynamic traffic networks, a personalized dynamic road impedance (PDRI) model is built to transform three main criteria (distance, time, and finance) affecting charging–navigation into comprehensive road impedance. In the first navigation stage, fast‐charging stations (FCSs) with the lowest overall objective are selected. In the second navigation stage, an improved Floyd–Warshall algorithm is utilized to identify the routes with the lowest personalized weight to the selected FCS in the PDRI model. Notably, the personalized preferences of EV drivers for the three primary criteria are considered in both stages of the navigation process. Finally, simulation results demonstrate a significant improvement in the degree of matching between charging navigation plans and drivers' personalized requirements, and a more balanced spatial–temporal distribution of EV charging demands among FCSs, which verifies the effectiveness of the proposed strategy.

Range-constrained traffic assignment for electric vehicles under heterogeneous range anxiety

This paper studied the range-constrained traffic assignment problem (RTAP), where heterogeneous range anxiety is considered among the driving population by electric vehicles (EVs). In order not to get stranded en-route, each EV driver is assumed to have his/her own driving range limit for being able to complete the trip. As a result, two types of multi-class RTAP can be defined through discrete or continuous distributed range anxiety. Given path-based side constraint structures, we proposed two variational inequality (VI) formulations for modeling discrete and continuous RTAPs, where the former is finite-dimensional according to a discrete number of user classes and the latter is infinite-dimensional accounting for an infinite number of user classes. We reformulate the continuous RTAP into finite-dimensional by merging adjacent EV drivers into one group. A unified path-based solution framework is developed to solve the two RTAPs, built upon the gradient projection algorithm. We design column generation and dropping schemes to adaptively maintain compact path sets and an inner equilibration strategy to accelerate convergence. Finally, a small network is used to examine the correctness and effectiveness of proposed models, and a large Winnipeg network is adopted to evaluate the impacts of stochastic driving range on network flows and computation costs. Numerical results provide compelling evidence of the outstanding superiority of the proposed algorithm, and show that EV drivers with heightened sensitivity towards range anxiety may contribute to the emergence of critical links experiencing severe congestion.

Driving change: Electric vehicle charging behavior and peak loading

Electric vehicles (EVs) are projected to comprise 40 % of Aotearoa New Zealand's light vehicle fleet by 2040. However, charging decisions made by EV drivers, such as whether to charge immediately or delay charging, will affect peak electricity demand and lifetime of distribution network components. This study uses an agent-based model (ABM) of EV charging to investigate the effect of different EV penetration levels and owner charging decisions on components in New Zealand's residential electricity networks, although the methodology is wholly generalizable to other countries or regions. Monte Carlo simulation is performed for EV charging in a neighborhood of 71 houses, based on a representative residential distribution network, and simulated for 20 days. The key outcome measure is the rate of ‘Exceedance’ of the 300 kVA baseline transformer limit, where greater Exceedance entails shorter lifecycle and increased maintenance or capital costs to the provider. Results show delayed-charging algorithms (‘Altruistic charging’) decrease peak electricity demand and Exceedance, while drivers charging immediately (‘Selfish charging’) increases Exceedance. New Zealand's residential electricity networks are expected to accommodate a 40 % EV transition with 100 % Altruistic charging, as Exceedance is expected to increase less than 20 % from Exceedance without EVs. However, Selfish charging increases the rate of Exceedance by more than 250 %. Longer-term, increasing EV penetration and household electricity demand will require increased workplace charging infrastructure, electricity network upgrades, and/or automated and Internet of Things (IoT)-enabled Demand Side Management (DSM) of EV charging to avoid high rates of Exceedance and increased maintenance and replacement costs.

Electric vehicles embedded virtual power plants dispatch mechanism design considering charging efficiencies

The increasingly popular electric vehicles (EVs) are changing the control paradigm of the power grid due to their uncoordinated charging behaviors. However, if well coordinated, smart homes, workplaces, and other locations that support EV charging could provide the grid with the urgently required flexibility via virtual power plants (VPP). In this paper, we develop the EV charging schedule model by capturing the unwillingness of EV drivers to alter their initial charging behaviors, referred to as the discomfort function. Predictability and the value of charging time, which represent the electricity consumption stability and the time value of EV drivers, characterize the discomfort function. Rather than existing works capturing discomfort by a direct simple parameter, such a computable data-driven quantification of discomfort enables us to customize an efficient VPP dispatch mechanism for EVs. In addition, to deal with the unknown charging efficiencies of EVs, we apply chance constraints only with the knowledge about mean and standard deviation of charging efficiencies, rather than their specific distribution. Using the concept of conditional value-at-risk (CVaR), we build an effective algorithm to solve the practical non-convex VPP dispatch model considering charging efficiencies. The effectiveness of our proposed models and associated algorithms are validated by numerical studies.

A two-period game-theoretical model for heterogeneous ride-sourcing platforms with asymmetric competition and mixed fleets

In the current ride-sourcing market, significant differences generally exist among various platforms: market share, market position, and vehicle type. Most previous studies focus on symmetric duopoly competition and assume that platforms have similar market power and size. However, much less is known about the operation strategy and market outcome under platform heterogeneity and asymmetry. This paper proposes a two-period Stackelberg-based model to formulate the competition between two asymmetric ride-sourcing platforms and captures the differentiated decision-making sequence and driver utilities under mixed fleets. In the proposed model, the established leader platform operates a mixed fleet of electric vehicles (EVs) and gasoline vehicles (GVs), while the emerging follower platform adopts an all-EV fleet. The mathematical mechanism of how the follower's and leader's decisions impact the demand and supply is analytically derived. It is found that the influence path of the leader's pricing on the supply can be divided into direct and indirect ways. In some cases, the leader can utilize the leadership position to promote the electrification of ride-sourcing services. The impact of potential EV and GV drivers on the platforms' optimal strategies and the surplus of passengers and drivers are also examined. We show that more potential EV drivers benefit both platforms, while fewer potential GV drivers are only conducive to the follower. Moreover, two extended models associated with entry deterrence, order-based wage, and additional profit are developed. The deterrence strategy of the leader platform indirectly accelerates ride-souring electrification and significantly reduces passengers' average waiting time. The economic analysis gains managerial insights into platform operations on the realistic asymmetric ride-sourcing market under transportation electrification.

Network equilibrium of battery electric vehicles considering drivers’ resting behavior

Driving fatigue cost is a major component of vehicle drivers’ travel costs in an intercity or regional network. The charging behavior of electric vehicle (EV) drivers, which is generally synchronized with drivers’ resting behavior, can contribute to the mitigation of drivers’ fatigue, especially after a prolonged driving period. Overlooking the impact of driving fatigue on the travel cost may overestimate the side-effects of the charging behavior for EV drivers, and result in biased flow and charging demand distribution. In this study, by considering the fatigue as part of the travel cost, we make the first attempt to investigate the impact of EV drivers’ charging and resting behaviors on their fatigue cost, and thus their travel plans and resultant flow distribution across the network. To this end, a novel network equilibrium modeling framework is first developed to capture the interaction among EV drivers’ travel plans, which specify the routing, recharging, and resting plans on a general intercity or regional network where charging stations and rest stops are deployed. When traveling between their origins and destinations, EV drivers are assumed to determine their travel plans to minimize their individual travel cost composed of driving time, rest time, charging cost, and fatigue cost, while preventing their batteries from being exhausted. The equilibrium model is then formulated as a variational inequality and transformed into a nonlinear optimization problem. An efficient solution algorithm integrating column generation and Benders decomposition approach is proposed to solve the established optimization problem. Numerical examples are presented to demonstrate the performance of the proposed models and solution algorithm. Numerical results validate that considering the impact of driving fatigue on the travel cost emphasizes the need for en-route charging for EV drivers with long-distance trips, and has an appreciable impact on the network flow and charging demand distribution. In addition, large-sized batteries and fast chargers may not necessarily reduce drivers’ travel costs for long-distance travel since charging behavior can be synchronized with drivers’ resting behavior and thus contribute to the mitigation of drivers’ fatigue.

Large Scale EV Charging Allocation in Unbalanced Multi-Microgrid System

The improvement of charging infrastructure has greatly relieved the range anxiety of electric vehicles (EV) drivers, leading to the rapid increase in EV ownership. Due to the uncertainty of EV charging behavior, a large number of EVs connected to microgrids (MGs) without coordination will aggravate the unbalanced load distribution in the multi-microgrid system. Contrariwise, EVs with appropriate coordination can be recommended to suitable charging stations. It is beneficial to the safe operation of the power grid. Considering the load distribution among MGs during the whole EV charging period, we propose a charging station recommendation strategy to dispatch EVs to suitable charging stations. The simulation results show that the proposed strategy can optimize the EV charging behavior and effectively balance the load distribution among MGs, which can alleviate the impact of a mass of EV charging behavior on the safe operation of the multi-microgrid system.

Location Selection of Battery Swap Station using Fuzzy MCDM Method: A Case Study in Indonesia

The rise of Electric Vehicles (EVs), supported by battery swap systems, brings various advantages, including reduced waiting times, lower upfront costs, and alleviating range anxiety. Battery Swap Stations (BSS) enhance green transportation by providing convenient options for EV users, especially in regions with limited fast-charging infrastructure. Many EVs, especially two-wheelers, need battery recharging after reaching their driving range. BSS availability can eliminate charging inconveniences for busy EV drivers. However, selecting BSS locations is often challenging due to budget constraints. This study aims to understand the criteria for selecting BSS locations in Indonesia. Potential location alternatives were identified using a fuzzy multi-criteria decision-making approach and input from government officials and industry experts. Factors like driving range, EV capacity, and budget availability were considered in determining the order of BSS establishment. The study found that technological and social aspects were the top criteria, suggesting that BSS development should prioritize established locations like mini markets and petrol stations.