Electric Vehicle Users 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.

A Game-Theoretic Approach for Electric Vehicle Aggregators Participating in Phase Balancing Considering Network Topology

With the increasing popularity of electric vehicles (EVs), uncontrolled charging of single-phase plug-in EVs will result in severe three-phase unbalance problem in distribution networks. To solve the problem, a phase balancing (PB) scheme performed by EV aggregators (EVAs) based on the linear multiphase power flow model is proposed in this paper. In the scheme, the PB compensations are provided to encourage EVAs to help the distribution system operator reduce the phase unbalance of the entire grid. Considering all EVAs are selfish and rational, the PB scheme is formulated as a game problem, in which each EVA competes with other EVAs to determine its charging strategy to minimize the charging cost minus the PB compensation. The consideration of coupling voltage constraints leads this game model to a challenging generalized Nash equilibrium problem (GNEP). By employing the theory of variational inequality, the properties of solutions for the GNEP are investigated. Furthermore, a two-level distributed algorithm is designed to find the unique variational solution, a fair and stable solution. Finally, comprehensive case studies are conducted on the IEEE-13 and IEEE-123 test systems to corroborate that the proposed PB game model can effectively mitigate voltage unbalance and cut EV users’ costs.

Interoperability of the Electric Vehicle Charging Ecosystem

Reduced pricing, range extension, quiet and luxurious ride experience, sleek designs, and most importantly ‘zero emission’ are some of the promotional features electric vehicle (EV) sellers use to entice potential ­buyers. But one thing these sellers cannot yet convince the shoppers is about the EV charging access and convenience. As the EV market share continues to grow, a common goal of all the regulatory entities, it is critical to understand the status of the charging infrastructure. This article depicts the latest status of EV charging ­infrastructure as of the year 2023–2024 and the potential needs for expanding the same using data-driven calculations and various global targets set for 2030 and beyond. It takes a fresh look at the reality of the existing charging stations and identifies the key areas of improvement to address common EV user concerns. Using a holistic approach of well-to-wheels investigation, the potential improvements discussed here ­benefit a range of stakeholders within the e-mobility ecosystem, rather than concentrating on just the charging station. With the intent of making the ­discussion more resourceful for both investors and technologists, data is presented using various public sector sources apart from the overviews on advanced technology concepts that can enhance e-mobility adoption.

Online Rectangle Packing Algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power

The vigorous development of electric vehicles (EVs) is an important means of reducing carbon emissions and mitigating environmental problems such as the greenhouse effect. Battery swapping stations (BSSs) can both provide battery swapping services for large-scale EVs and charge batteries centrally. As the supply of fully charged batteries in the BSS shrinks, it becomes necessary to schedule the charging of the depleted batteries rapidly that users have swapped for fully-charged ones. The charging schedule for depleted batteries must be made without knowledge of future battery arrivals. In this context, this paper develops a mathematical model for online charging scheduling of BSSs, formulates the charging strategy as a two-dimensional rectangle packing problem, and quickly calculates the scheduling arrangement of batteries by partitioning the remaining available capacity of a BSS. Since there are limited battery types within the BSS which can provide battery replacement services, this paper supplements the proposed model with known battery types, which improves the utilization of the available capacity of BSSs. Finally, numerical results verify the effectiveness of the proposed model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Electric vehicles (EVs) are becoming an alternative way to reduce carbon emissions in transportation systems. Herein, the optimal battery charging problem is the core problem when it comes to dispatching a huge number of EVs. Up to now, battery-swapping is widely used for EVs due to its simple, convenient way. Furthermore, a business model for the battery swapping stations (BSSs) is brought up, where EV users send their depleted batteries to the BSS and the BSS provides the users with a fully charged replacement battery from its warehouse, which only takes a few minutes. Since the maximum charging power of the BSS is limited by the capacity of the transformer connecting the BSS to the power grid, the BSS will adopt an optimal charging schedule that maximizes the charging benefit for large quantities of depleted batteries in the warehouse. However, the challenge is that the charging schedule for depleted batteries must be made without knowledge of future battery arrivals because the EV behaviors are difficult to predict. To address this problem, this paper developed an online charging scheduling algorithm, which formulates the charging strategy as a two-dimensional rectangle packing problem. The proposed method can provide battery replacement services in real-time and solve quickly without any information about incoming depleted EV batteries. The proposed model and method have been tested on the system with different numbers of batteries to show the effectiveness. Besides, the online two-dimensional rectangle packing problem can provide an online decision for BSSs.

Comparative analysis of a supercapacitor and Li-ion battery based electric vehicle

This paper has simulated an Electric Vehicle (EV) in MATLAB/Simulink environment where Supercapacitors (SC) are compared with Li-ion batteries. Simulated EV is tested under both above discussed scenarios for evaluating its performance under certain speed and torque load conditions. The critical aim of the government or any manufacturing company is to reduce the carbon footprint and provide better living conditions for their people. The major step to achieve this aim is to maximize EV usage. Therefore, it can be attained through the implementation of battery energy storage system. This paper has paved the way towards the goal by showing supercapacitors or Li-ion as energy sorage interms of there performances and recharging time. The inhibition of supercapacitors instead of the currently used Li-ion can turn out to be a game changer as it can be reused which decreases the chances of E-wastage but it has certain limitation. Moreover, supercapacitors are known for having high speed of charge (1000 times faster than Li-ion) that increases the efficiency of the EV.

A user-friendly electric vehicle reallocation solution for uniformly utilized charging stations

Electric vehicle (EV) users often struggle to identify charging stations (EVCSs) that are both available at the time of interest and placed in convenient locations. At the same time, EVCSs in nearby locations may systematically be underutilized. This variance in the utilization of the EVCSs can have negative implications, both for the EV users (e.g. higher anxiety, waiting time, and discomfort) and the owners/operators of the EVCSs (e.g. lower income and longer payback period of investment). The present work aims to mitigate said implications by introducing a solution that efficiently allocates EV users to stations based on their expected utilization and their proximity to the charging locations of preference. To do so, a forecasting model is first used to predict the arrivals at the EVCSs. Then, the utilization rates of the EVCSs are computed and a heuristic algorithm is employed to dynamically reallocate the EV users from overutilized stations to nearby, underutilized stations. The proposed approach, which has been implemented in a web-based application, is evaluated through simulation experiments and compared with the traditional “first-come, first-served” approach. The results demonstrate that implementing the suggested method can lead to more evenly distributed utilization rates, resulting in an approximate 6% improvement. This improvement is achieved with minimal negative impact on the convenience of EV users, with a maximum increase of 214 m in walking distance. Therefore, this approach offers a practical solution to the problem under investigation

V2G from Electric School Buses: The Impact on Children, Communities and Supply Chain Management

Electric vehicles (EVs) are rapidly acquiring significance in the day-to-day lives of the masses and so is the vehicle to grid technology (V2G). While nations across the globe are pushing for rapid adoption, they have to first address the challenges of grid overload and shutdown and intermittency of electricity supply from renewable sources. V2G technology provides a respite but is not without drawbacks. The electric school buses (ESBs), that remain idle for most part of the day, have emerged as the preferred choice in implementing V2G with obvious health, environmental and economic advantages. ESBs may appear initially as the new solution to public transportation, but it will affect our lives in many aspects besides being the environmental solution to public transportation. These EVs can be used for multifaceted functionalities than just being restricted to be a charging tool, a load balancer for the Grid or just as a means of transportation. In our digitized world the need for uninterrupted energy has attained a lot of importance. Innumerable solutions and diversified usage of EVs can be innovated from EVs existing energy sources (the batteries) to its in-built technological systems. The V2G concept, combined with smart grid technologies, offers an optimal system for the realization of the benefits offered to users, utilities, economics and the environment by electric vehicles. Electric school buses (ESB) can be used effectively to build a cleaner supply chain. This article delves into the benefits that ESBs confer upon school children, upon the communities and the economy. The health, environmental and economic benefits outweigh the initial costs involved with acquisition and usage of ESB fleet. The sustainability of using the V2G technology using school buses in supply chain management, especially where the supply chain is spread across remains a concern and warrants further research. Nevertheless, beyond a doubt, electric school buses will play a significant role so far as improving the health of the children and communities and reducing emission of pollutants is concerned.

Socialization of Safe use of Electric Vehicles in Kintamani Tourism Village

Electric vehicles (EVs) are currently one of the transportation options that are considered to be able to create a better environment. However, in practice, it has been found that EV users, especially electric motorcycles, have not behaved safely, such as underage drivers, not wearing helmets, and even exceeding the speed limit. Therefore, this socialization aims to provide education to the people of Kintamani Village on how to use EVs safely in traffic spaces. The socialization method is by giving lectures and tests to see the people's knowledge about safe driving before and after the socialization. The results of the pre-test and post-test showed that the people had an increase in understanding about safe driving

Design, control and performance comparison of PI and ANFIS controllers for BLDC motor driven electric vehicles

The research and usage of electric vehicles (EVs), including two and four-wheeler vehicles, are rapidly increasing worldwide as alternatives to oil/gas-based vehicles. Brushless direct current (BLDC) motors are popular for industrial and traction applications due to their inherent advantages. In EVs, achieving low error in steady-state and transient responses is crucial for smooth acceleration at the wheel. This paper presents the design and control of a BLDC motor for speed control during acceleration and deceleration, considering error as a key factor in the MATLAB/Simulink environment. Proportional-integral (PI) and fuzzy controllers are commonly used for motor control to improve steady-state and transient performance, thereby reducing error. In this study, the PI and adaptive neuro-fuzzy inference system (ANFIS) controllers are designed and compared for a 5-kW, 48-V, and 100-Amp BLDC motor in EV applications. The results demonstrate that the ANFIS controller enhances the dynamic performance of the BLDC motor and improves other operating characteristics such as rise time, settling time, peak overshoot percentage and the vehicle response in terms of speed and distance.

PSO‐based optimal placement of electric vehicle charging stations in a distribution network in smart grid environment incorporating backward forward sweep method

AbstractThe transition from conventional fossil‐fuel vehicles to electric vehicles (EVs) is critical for mitigating environmental pollution. The placement of electric vehicle charging stations (EVCS) significantly impacts the utility operator and electrical network. Inappropriately placed EVCS lead to challenges such as increased load, unbalanced generation, power losses, and reduced voltage stability. Incorporating distributed generation (DG) helps mitigate these issues by maximizing EV usage. This study focuses on optimizing EVCS and DG placement in radial distribution networks. The methodology employs a backward and forward sweep method for load flow analysis and utilizes the particle swarm optimization (PSO) algorithm to determine optimal EVCS and DG locations and sizes. This approach, validated on the IEEE‐33 bus system, outperforms existing methods. Results indicate a 2.5 times greater power loss reduction compared to simulated annealing (SA), 1.6 times better than artificial bee colony, and parity with genetic algorithm (GA). Overall, the PSO algorithm demonstrates superior optimization effectiveness and computational efficiency, showcasing 1–2.5 times better performance than other methodologies. Employing this approach yields significantly improved results, making it a promising technique for optimizing EVCS and DG placement in distribution networks.

Bi-Directional Power Flow Rapid Charging System Using a Carrier Wave Modulated Self-Commutated Voltage Source Converter for Electric Vehicle

This paper examines a high-performance battery charger for electric vehicles (EVs). Active power line filtering capabilities in the battery charger can be used to develop a fast-charging infrastructure that is advantageous for EV users as well as power distributors. This article discusses battery charger modeling and design difficulties in depth using a carrier wave modulated self-commutated voltage source converter architecture. A full study of the controller's features is also provided using model-based controller synthesis. The battery charger's active filtering performance is emphasized. In active filter applications, the model-based control system's intrinsic phase deviation and sensitivity to system factors decrease the active filter's performance, which is disclosed in this study. Based on numerous integrators in diverse reference frames, we provide an improved control structure for active filters to offset the model-based controller's degradation. The suggested controller demonstrates minimal sensitivity to system factors and fully compensates for the intrinsic phase deviation, as shown both theoretically and empirically. The suggested active filter controller has outstanding steady-state conditioning performance.

(Keynote) Thermal Performance Optimisation at Cell Level, to Achieve Better, Longer Lasting Battery Packs

Temperature is a major issue in the operation of lithium-ion batteries, with both high and low temperatures causing problems. Cold batteries are inefficient, while hot batteries are at risk of thermal runaway and fire, making it crucial to maintain a safe and optimal temperature range. Temperature gradients can negatively impact battery performance, reducing accessible energy and increasing the degradation rate. This is a critical issue for electric vehicles (EVs), which can experience reduced driving range and deteriorating performance over time.Despite these challenges, current lithium-ion cell designs prioritize maximizing specific energy, which can negatively impact thermal performance. Thermal management systems are used to extract heat from poorly designed cells, but they are a significant parasitic load, demanding on average 8 kW in typical operation. Further, they can introduce uneven distribution of current throughout the battery pack, leading to diminished performance and accelerated degradation. The limited performance of these systems contributes towards range anxiety, which is a major barrier to the widespread adoption of electric vehicles in the UK, Europe, and the USA.To address these challenges, the authors propose the use of a new metric, the cell cooling coefficient (CCC). The CCC is a constant for a given cell and thermal management method (e.g. surface cooling or tab cooling), and is measured in W.K-1 (the reciprocal of thermal resistance). The CCC define a cell's ability to reject heat energy. The CCC is defined as the temperature gradient across a cell in idealized operating conditions, normalized against the heat generation rate in the same conditions. This allows for the comparison of geometrically dissimilar cells and can be used to optimize the thermal performance of lithium-ion cells.Experimental results presented in the paper demonstrate that increasing the thickness of the tabs in a typical lithium-ion pouch cell by 34% can yield a tab cooling performance enhancement of 20%, with a specific energy reduction of just 0.7%. Numerical methods predict that for a large format automotive pouch cell, increasing the tab thickness from 0.2 mm (negative) and 0.45 mm (positive) to 2 mm will lead to a 90% increase in the CCC for tab cooling (see in the attached figure). This yields a 51% reduction in temperature gradient across the cell in any instance of operation. As with all geometric optimisation, our models highlight a law of diminishing returns and we discuss the optimal tab thickness for various operations and applications.The authors also consider the use of the CCC metric for edge cooling in pouch cells and cylindrical cells. They find that tabs used to connect jelly rolls to the external can of cylindrical cells create a considerable thermal bottleneck. Redesign of this tab is well documented through Tesla’s proposed tab-less 4680.The use of thermal models at cell-level alongside existing battery models is critical for the development of future lithium-ion cells. The authors demonstrate that optimisation of thermal performance is entirely achievable within the numerical domain, reducing the requirement for expensive and timely prototyping phases during any product development process.By designing cells to have better thermal performance, it is possible to reduce the temperature gradient required between the hottest and coolest points in the thermal management system. This, in turn, reduces the power required by the thermal management system, allowing for more power to be directed towards extending the driving range of electric vehicles. Reduced temperature gradients at the battery pack level will also extend battery pack lifetime, reducing the lifetime cost to end-users and lessening the strain on raw material supply chains and battery recycling technology.The use of the CCC metric offers a promising approach to optimizing the thermal performance of lithium-ion cells. By addressing thermal management challenges, it is possible to reduce the parasitic load of thermal management systems, improve the lifetime and cost-effectiveness of battery packs, and lessen range anxiety for EV users. Figure 1

Large-scale public charging demand prediction with a scenario- and activity-based approach

Transportation system electrification is expected to bring millions of electric vehicles (EVs) on road within decades. Accurately predicting the charging demand is necessary to accommodate the surge in EV deployment. This paper presents a novel modeling framework to predict the public charging demand profile derived from people’s travel trajectories, with the consideration of the demand and supply stochasticity of transportation systems and the charging behavior heterogeneity of EV users. The vehicle charging decision-making process is explicitly modeled, and the charging need of each EV user is estimated associated with their travel trajectories. The methodology enables charging demand prediction with a high spatial–temporal resolution for transportation system electrification planning. A case study was conducted in Los Angeles County to predict the demand for public charging facilities in 2035 and perform corresponding spatial–temporal analysis of EV public charging under various scenarios of future electrification levels and network conditions.

Analysis of the Potential Economic Impact of Parking Space Comprehensive Utilization on Traditional Business District

This paper investigates the latent classes of parking preference for drivers and the economic effects after implementing Parking Space Comprehensive Utilization (PSC) in traditional business districts (TBD), with a particular focus on the parking preferences of electric vehicle users (EVU). Firstly, Exploratory Factor Analysis (EFA) is used to reduce dimensionality and determine the latent structure. Then, based on the Latent Class Model (LCM), the customers are classified, and the proportion of each class under various latent variables is analyzed. Finally, the paper conducts a quantitative analysis of economic effects by considering different psychological factors across different customer classes. With the data obtained from revealed preference (RP) and stated preference (SP) surveys, this paper identifies the customers’ preferences for the three scenarios presented. The results show that (1) customers can be classified into four classes: core customers (CCS, 34%), potential customers (PCS, 29%), regular customers (RCS, 22%), and marginal customers (MCS, 15%), among which EVU do not show a significant preference for parking charging facilities in TBD; (2) the potential economic improvements for these four classes are: 9%, 12%, 8%, and 10%; (3) CCS has the greatest potential to increase store revenue by ¥7041, while PCS has the greatest potential to increase store customer flow by 31%. These findings provide a valuable reference for decision-making by TBD store managers.

A multi-timescale energy scheduling model for microgrid embedded with differentiated electric vehicle charging management strategies

This paper presents a novelty energy management model embedded with differentiated charging management strategies for optimal scheduling of the microgrid (MG), including electric vehicles (EVs) and renewable energy sources (RES) in similar official buildings and industrial parks. The study firstly synthetically considers the objectives of multiple stakeholder entities in the MG and various uncertainties and employs a multi-timescale energy schedule model to satisfy each entity’s different objective and optimize the power procurement strategy. In the day-ahead stage, an enhanced variant multi-objective particle swarm optimization algorithm (EVMOPSO) is used to solve the multi-objective equilibrium optimization model, while in the real-time stage, a rolling correction model based on model predictive control (MPC) is introduced. Secondly, considering the significant diversity of driving behaviors of the EV users unique to the MG above, this study presents a set of differentiated charging management strategies for the first time in the multi-timescale energy management model. These strategies are based on credibility, which assigns different credibility levels to users by evaluating the degree of regularity of their driving behaviors to assign corresponding charging and price strategies. Simulation results verify the method’s practicality and validity, and the model’s robustness is also verified by sensitivity analysis.

Electric vehicle charging scheduling with mobile charging stations

Mobile charging station (MCS) is a brand-new technology to electric vehicle charging, which is a supplementary service for addressing the shortcomings associated with fixed charging station (FCS), such as prolonged waiting time of charging, charging congestion of FCS, and user travel anxiety. While MCSs offer convenience to electric vehicle users, the challenge faced in the MCS operation is that MCS equipped with large batteries to provide charging services to users by visiting them one by one not only leads to high operating costs, but also prolongs the waiting time for users to utilize MCS services. To overcome the issues, a novel framework is proposed by optimizing the MCS operation in economic efficiency. In this study, a variant of mixed integer linear programming (MILP) model is developed to maximize the MCS operator's profits with considering the user's perspective, including clustering, and covering user demands, setting temporary charging location for users, dispatching MCSs for charging and discharging, and scheduling EV charging. As the scale of the problem increases, the solution time of using the proposed MILP model is inefficient. Whereas an improved genetic algorithm is developed for solving a large-scale of the proposed model. Solomon's benchmarking instance data is adopted to evaluate the performance and validity of the proposed algorithm, complemented by various sensitivity analyses aimed at providing managerial insights into MCS operations.

Electric vehicle usage, pollution damages, and the electricity price elasticity of driving

I study battery electric vehicle (BEV) usage and ownership characteristics with fundamental implications for the electrification of passenger transportation. Using data covering the entire BEV population in New York, I quantify BEV mileage and electricity consumption and highlight the important role of vehicle utilization in contributing to real-world pollution damages and their spatial variation. I then study the factors influencing how much BEVs are driven with a focus on estimating the electricity price elasticity of BEV mileage. Understanding how drivers respond to these changes in operating costs may help align the social and private costs of BEV driving and illustrates how electric utilities may affect transportation outcomes in the future. I find a 10% increase in residential electricity prices reduces mileage by 1%, but responsiveness falls as public charging stations—where prices are often decoupled from electricity costs—become available.

Privacy-Preserving and Traceable Blockchain-Based Charging Payment Scheme for Electric Vehicles

With the rapid development of the global clean energy industry, the electric vehicle (EV) has gradually replaced the traditional fuel vehicle as a promising modern transportation tool due to its environmental friendliness and competitive prices. Its widespread adoption has led to a substantial increase in demand for subsidiary facilities such as charging piles and stations. Despite the convenience and accessibility offered by charging services, the privacy of EV users may be compromised in the process, as malicious attackers can infer their true identities and consumption habits from service orders. Existing studies introduce blockchain technology into vehicle charging payment scenarios to protect the privacy of EV users. Unfortunately, it cannot achieve the desired full anonymity and may even hinder electricity regulators’ investigation of suspicious transactions and entities. Therefore, designing a vehicle charging payment scheme that simultaneously supports privacy protection and traceability is a challenge. To address these concerns, this paper proposes a privacy-preserving and traceable blockchain-based charging payment (PTB-CP) scheme for EVs. It can protect users’ identity privacy and transaction unlinkability and track abnormal transactions or entities in exceptional circumstances. We conduct a comprehensive analysis of PTB-CP from both theoretical and experimental aspects. The analysis results demonstrate that our scheme can realize privacy protection, reliability & authentication, traceability, scalability, and high efficiency.

A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response

The high penetration of electric vehicles (EVs) poses both opportunities and challenges for power systems. EV retailers, playing a critical role in the demand response mechanism, face market risks caused by uncertainties in electricity consumption and prices. To address the operational issues of large-scale EVs and tap the potential of EV retailers, a temporal and spatial domain-based optimization strategy is proposed, which is implemented on bi-level (referring to transmission and distribution grid networks). First, a physical scheduling model of the grid is established, consisting of a novel hybrid demand response mechanism considering the incentives of EV retailers and the retail electricity price of EV users, and a new robust retail electricity pricing strategy handling the uncertainties of EV behavior and the electric market. Then, a bi-level optimization strategy is presented: at the upper level, in the transmission network, based on the robust pricing strategy, a unit commitment model that coordinates the hybrid demand response with other distributed energy resources is designed to optimize load periods of EVs in the time domain; at the lower level, in the distribution network, an optimal power flow model is proposed to spatially dispatch the location of EV loads. The impacts of retail price profile, EV penetration, hybrid demand response mechanism, and EV load location are analyzed in ten tests using the IEEE 33 distribution network. Simulation results indicate that the robust pricing strategy can effectively handle uncertainties, the integration of the hybrid demand response mechanism into scheduling can ensure the benefits of all participants, and the bi-level optimization strategy can accommodate distributed energy resources temporally and spatially.

Incremental Profitability Evaluation of Vehicle-to-Grid-Enabled Automated Frequency Restoration Reserve Services for Semi-Public Charging Infrastructure: A Case Study in Belgium

The current paper defines a framework for the introduction of automated frequency restoration reserve services, enabled by vehicle-to-grid technology, into the business model of an entity owning and operating a network of semi-public Electric Vehicle Supply Equipment. It assesses the profitability of this introduction by performing a case study based on the real-life electric vehicle charging data from the EVSE network located in a hospital parking lot. From the results of the study, it is clearly visible that the introduction of vehicle-to-grid-enabled automated frequency restoration reserve services has a significant positive incremental profitability; however, this is heavily dependent on the plug-in ratio of the charging network, determined by electric vehicle users’ behavior.