Electric Vehicle Users Research Articles

Remedying driving range and refueling challenges in electric mobility: Consumer adoption of battery-swappable electric vehicles

Driving range (mileage) limitations and long charging times associated with electric vehicle (EV) usage have necessitated the emergence of battery-swappable electric vehicles (BSEV) for sustainable mobility. In enhancing BSEV proliferation, a thorough comprehension of consumer adoption is needed. Understanding consumer adoption or hesitancy of BSEV is important for environmental sustainability. Consequently, this study explores the adoption intention of BSEVs by extending an amalgamated technology acceptance model (TAM) and the innovation resistance theory (IRT) with knowledge. Using 552 responses obtained through a survey, hypotheses were assessed using a structural equation model (SEM). Results from the study expressed that the conceptual framework explained 70.2 % of the overall variance, better than the individual TAM (40.7 %) and IRT (20.2 %), as well as the integrated model (60.7 %). Perceived ease-of-use, attitude, and perceived usefulness were positive in influencing BSEV. Value barrier and usage barrier were negatively significant in influencing BSEV adoption intention. However, the barrier of risk was insignificant in affecting the adoption intention of BSEVs. Tradition and image barriers were negative in influencing attitude. Perceived usefulness and attitude were discovered as mediators. This research provides policymakers and stakeholders with apt recommendations to promote acceptance and mitigate BSEV adoption hesitancy.

A decision framework for improving the service quality of charging stations based on online reviews and evolutionary game theory

With “charging anxiety” gradually replacing “range anxiety” as the major obstacle for consumers to purchase electric vehicles (EVs), the quantity-driven rather than quality-driven charging station development model barely meets the development of the EV market. It is of great practical significance that a comprehensive and in-depth insight into charging stations’ customer requirements (CRs) to uncover their pain points and improvement measures. Therefore, this study develops a decision framework for improving EV charging service quality based on quality function deployment (QFD). First, CRs are extracted based on charging station online reviews and classified by the revised importance performance analysis (IPA) method to identify the charging service’s main highlights and pain points. Then, the process of achieving consensus among experts on evaluating the correlation between CRs and service design requirements (DRs) is considered as an evolutionary game of coalition, with a meritocratic Fermi rule considering affinities (MFA) being introduced. Finally, the importance of DRs is determined by combining the Shapley value and risk aversion theory. The results indicate that regulation and convenience are the main highlights of charging stations to satisfy EV users, while reliability and assurance are primary pain points. The charging station operators should prioritize their limited resources on providing efficient customer and maintenance services, installing charging stations with international standards, and regularly inspecting and updating charging infrastructures. This study is beneficial for charging station operators to understand what customers want and how to satisfy them.

Suitable Site Selection of Public Charging Stations: A Fuzzy TOPSIS MCDA Framework on Capacity Substation Assessment

The number of electric vehicles (EVs) continues to increase in the automobile market, driven by public policies since they contribute to the global decarbonization of the transportation sector. Still, the main challenge to increasing EV adoption is charging infrastructure. Therefore, the site selection of public EV charging stations should be made very carefully to maximize EV usage and address the population’s range anxiety. Since electricity demand for charging EVs introduces new load shapes, the interrelationship between the location of charging stations and long-term electrical grid planning must be addressed. The selection of the most suitable site involves conflicting criteria, requiring the application of multi-criteria analysis. Thus, a geographic information system-based Multicriteria Decision Analysis (MCDA) approach is applied in this work to address the charging station site selection, where the demographic criteria and energy density are taken into account to formulate an EV increase model. Several methods, including Fuzzy TOPSIS, are applied to validate the selection of suitable sites. In this evaluation, the impact of the EV charging station on the substation capacity is assessed through a high EV penetration scenario. The proposed method is applied in Cuenca, Ecuador. Results show the effectiveness of MCDA in assessing the impact of charging stations on power distribution systems ensuring suitable system operation under substation capacity reserves.

Optimal Scheduling for Increased Satisfaction of Both Electric Vehicle Users and Grid Fast-Charging Stations by SOR&KANO and MVO in PV-Connected Distribution Network

Optimal Scheduling for Increased Satisfaction of Both Electric Vehicle Users and Grid Fast-Charging Stations by SOR&KANO and MVO in PV-Connected Distribution Network

Modeling, design and validation of DC-DC landsman converter for low-power electric vehicle battery charging applications

As the world moves towards sustainable development, the reduction of air pollution is considered an important factor. Consequently, the transportation sector is moving from conventional internal combustion engine vehicles to electric vehicles (EVs). To encourage EV usage, governments are working towards installing more public charging systems. These systems invariably use a DC-DC converter to ensure the supply of an appropriate voltage to charge the battery. In developing countries, the majority of users prefer electric bikes and electric rickshaws, which require low power. Therefore, it becomes necessary to use low-power converters suitable to charge these batteries. In such systems, non-isolated DC-DC converters play an important role. This work is intended to explore the use of the Landsman converter for EV battery charging. The motivation behind the work is explained, and the state of the art of EV battery charging research is reviewed to show the research gap. The operation of the proposed converter is explained in detail with its dynamic loop and nodal equations. The gain equation and the memory elements design are detailed with the corresponding equations. The model of the proposed converter is provided with the loss calculation. The system is simulated on MATLAB R2022b software, and the simulation results are provided to show the behavior of voltage and current in each component. To validate the design, a 100 W hardware prototype is implemented using discrete components to supply a resistive load, and to charge a 48 V Li-ion battery. The corresponding results are provided to validate the design and operation. A comparison of the proposed converters’ performance parameters with other non-isolated converters is provided. The topology is seen to provide an instantaneous efficiency of 88% during the prototype testing for resistive load, and an efficiency of 89.6% while charging a 48 V Li-ion battery, wherein, the efficiency magnitudes are the experimental values obtained through real-time measurements on Keysight IntegraVision Power Analyzer PA2203A, during the testing of the hardware prototype, as presented in section . Consequently, Landsman converter is seen to be an attractive converter for EV battery charging applications.

The influence of socio-technical variables on vehicle-to-grid technology

Vehicle-to-grid technology (V2G) is a novel large scale energy storage option to improve the grid integration of renewable energy sources (RES). Using electric vehicle (EV) batteries to store and provide electricity to the grid, the intermittency of RES can be reduced. However, a successful implementation of this technology depends on various social and technological factors. This study analyses the influence of social and technical factors such as V2G acceptance (percentage of EV users that allow V2G energy transfer), EV battery availability for V2G service (percentage of the EV battery), EV adoption level (number of EVs in EV fleet) and the charger power for the energy transfer between the storage system and grid.Using Germany for the case study, a simulation model is developed and employed for the study. The base simulation results show that Germany needs 190 GW of PV and 170 GW of wind turbine installation to meet 80 % of electricity generation from RES in 2030. Further results show that an increased V2G acceptance and EV battery availability reduces the V2G contribution from individual EVs. A V2G acceptance of only 30 %, using half of the battery capacity dedicated to V2G, can help to reach an hourly reliability of 86.6 %. Having 50 % V2G acceptance, the hourly reliability increases by 5.3 %–91.9 %. The final analysis on charger power and EV adoption level highlights that a normal charger power of 7 kW or 11 kW can successfully accommodate V2G service. The study's overall results indicate that V2G technology will be an effective storage solution for Germany in the future.

Startup characteristics of a water loop heat pipe with dual heat sources for battery thermal management system in electric vehicle

Abstract The usage of electric vehicles has significantly reduced emissions of greenhouse gases and other pollutants. However, the high heat release generated by the electric vehicle batteries poses a challenge. To solve this problem, scientists have created a passive cooling thermal management system specifically for electric vehicle based on heat pipes, particularly loop heat pipes. A battery pack often consists of several battery modules, which results in multiple heat sources being dispersed according to their power capacity. Startup behavior of loop heat pipe has been investigated extensively in the literature. However, most of the studies use only one heat source. This paper aims to fill the research gap, particularly when the system is implemented in dual heat sources managed by only one evaporator. To achieve the research objectives, a custom loop heat pipe was constructed. This cooling system’s design is briefly described. The evaporator is made of copper, deionized water was selected as the working fluid because of its high merit number, which indicates strong performance as a heat pipe working fluid and the stainless-steel wire mesh serves as the porous wick. Battery simulator was built using aluminum material and a cartridge heater to mimic the heat produced by the battery. Two case studies were done. First, only one battery simulator was used. Second, two battery simulators were placed on both sides of the evaporator. A type-K thermocouple attached to the NI DAQ 9214 module was used to measure the temperature while the electric heat load varied between 10 W and 50 W. The study investigated the interaction between the heat load distribution and the startup behavior of the loop heat pipe. Startup behavior is crucial for the performance of the loop heat pipe. Based on the experimental results, the loop heat pipe demonstrates outstanding startup performance. It can effectively initiate operation even at a minimal heat load as low as 30 W for the first and second case study. The findings of the study indicate that the dual heat source arrangement effectively mitigates overshoot temperatures and enhances heat transfer performance by increasing the contact area.

RANCANG BANGUN PROTOTIPE PERANGKAT KERAS ELECTRIC VEHICLE SUPPLY EQUIPMENT MENGGUNAKAN PROTOKOL SAE J1772

The existence of Public Electric Vehicle Charging Stations (SPKLU) is increasing alongside the rise in electric vehicle usage in a certain area. EVCS is equipped with devices that directly interact with electric vehicles (EVs) during charging; this device is called Electric Vehicle Supply Equipment (EVSE). Both EVSE and EV require protocols to communicate with each other aimed at exchanging information such as readiness for charging between EVSE and EV. The communication protocol used in this research is the SAE J1772 protocol. This study focuses on designing a hardware prototype of EVSE based on the principles of the J1772 AC Type 1 connector as the transmission medium to electric vehicles (EVs). The design of EVSE communication simulation uses the Falstad Circuit Simulator platform, and the PCB layout design uses the EasyEDA platform. The hardware prototype of EVSE is controlled using a Raspberry Pi 3B mini PC as the core management, then integrated with the initial terminal block, power supply block, PZEM-004T sensor module, J1772 communication block, 2-channel solid-state relay (SSR) module, and J1772 terminal.

Shaping the future: Improvements in the EV charging Infrastructure: A comparative analysis of Germany and India

The shift towards sustainable transportation has accelerated the deployment of electric vehicles (EVs), demanding advancements in EV charging infrastructure. [1] This thesis, "Shaping the Future: Improvements in EV Charging Infrastructure," explores the critical role of enhancing charging networks to boost EV adoption and usage. It delves into the status, challenges, technological innovations, regulatory frameworks, and evaluates the collective impact on future mobility. In-depth investigations reveal the challenges facing the existing EV charging infrastructure, such as grid capacity limitations, interoperability issues, and regulatory complexities. [2] It also compares global strategies from countries like Germany and India to evaluate the impact of government policies, subsidies, and incentives on infrastructure deployment. The research identifies emerging trends, including wireless charging, bidirectional capabilities, and smart, connected charging stations, emphasizing their potential to revolutionize the EV charging experience. It assesses the economic and environmental sustainability of integrating renewable energy sources into charging networks, as well as the scalability and adaptability of the infrastructure to evolving demands. [3] By synthesizing these findings, the thesis provides insights into future directions for the development of EV charging infrastructure. It offers recommendations for overcoming current barriers, fostering technological innovation, and harmonizing regulations to create a seamless, sustainable, and universally accessible EV charging environment.

A Review on Fire Accidents in Electric Vehicles

Abstract: Electric vehicles are replacing internal combustion engines drastically because of high performance and efficiencies compared to internal combustion engines. Weight and cost of production are also very low compared to ICE's. Nowadays electric vehicle usage is high and some of the EVs are catching fire because of various external factors. In this study, we are going to identify the factors for fire accidents in Electric vehicles by following real-life case studies and discuss some safety measures to prevent the vehicles from catching fire.

Electric vehicle public charging choices: a qualitative investigation

ABSTRACT Understanding how potential and existing electric vehicle users choose public charging is critical for the development of public-charging infrastructure. This study employs a qualitative approach guided by stated choice tasks to examine the decision-making processes regarding public charging. Through 20 interviews of existing and potential EV users in the UK, thematic analysis of responses pointed towards three areas: the choice context, attribute non-attendance and payment method. Participants sought detailed information about the circumstances under which they had to charge their vehicles. Attribute non-attendance meant that some participants paid more attention to specific attributes than others (e.g. location, price). Responses around the payment method pointed towards differences in relation to personal attitudes. This evidence suggests that charging choices are context-dependent, choices may be guided by specific attributes and individual attitudes play a role in these choices. These are also important considerations when designing a large-scale survey involving choice experiments and public charging.

Tariff option preferences for smart and bidirectional charging: Evidence from battery electric vehicle users in Germany

Smart charging of battery electric vehicles (BEVs) can contribute to flexibility in power grids and help integrate renewable electricity. Tapping into this potential requires high user acceptance for smart charging and corresponding tariffs. In this paper, we analyze the preferences of current BEV users, representing the potential near-term adopters of smart charging, for different smart charging tariff design elements by conducting a discrete choice experiment with 689 participants in Germany. In doing so, we (1) provide an overview of current BEV users' preferences, (2) identify and characterize BEV user groups with substantial differences in their preferences, and (3) identify barriers for smart charging implementation from the perspective of current BEV users. More specifically, we find that potential cost savings along with the pricing scheme and charging mode are the most important tariff elements, whereof a pre-defined price corridor with an emergency price for grid bottlenecks and charging a safety buffer before applying smart charging are most preferred. We identify three user groups, with a large share of innovative adopters. Moreover, driving range or reluctance regarding data sharing can represent barriers for smart charging adoption. Based on our findings, we derive implications for decision-makers in policy and industry.

Research on the capacity of charging stations based on queuing theory and energy storage scheduling optimization sharing strategy

The rapid development of electric vehicles (EVs) has led to the continuous expansion of charging infrastructure, but it has also resulted in the low utilization of urban charging stations. Numerous cities have made the decision to open to the public electric bus (EB) charging stations for EVs in an effort to address the issue of charging difficulty for EVs in urban areas and increase the use of charging stations. However, when EB charging stations are open to the public, they inevitably create a load on the power grid during peak hours, and also increase the queuing time for EV users. To address these issues, this paper proposes an operational model where EVs can use the EB charging station from 6:00 AM to 8:00 PM daily, while EBs can charge at other times. In order to reduce grid load during periods of peak electricity demand and lower electricity costs, the model makes use of energy storage facilities to charge during off-peak hours and discharge during peak hours. Queue times are also decreased by optimizing the number of chargers using the M/M/s/K queuing model. The research results indicate that during peak hours at the charging station, the probability of electricity consumption exceeding the storage battery's capacity is only 3.562 %. After five years of operation, the charging station has saved 5.6610 % on electricity costs. Taking the K1 bus route in Jinan, Shandong Province as a case study, it was found that the optimal configuration involves 22 chargers. This operational model and energy storage strategy provide a feasible solution for EB charging stations, contributing positively to the sustainable operation of charging stations.

Load redistribution attack for power systems with high penetration of EVs

This paper provides insights for cyber defenders of power systems with high penetration of electric vehicles (EVs) by proposing a novel consecutive attack model based on load redistribution for price control in both transmission networks (TNs) and distribution networks (DNs). The target of the attack is to induce massive EV users in DNs to charge simultaneously and cause a spike of loads in the TN at peak hours. The problem is formulated as a multi-slot bi-level optimization problem, where the upper level describes the hacker behavior and attacking constraints. The lower level explains the TN operator and DN operators’ behaviors and the relationship between the local marginal price and retail charging price. The bi-level problem is converted into an equivalent single-level mix-integer linear problem and is solved based on greedy algorithm. Simulations on IEEE 30-bus system prove the effectiveness of the attack strategy.

Federated Learning-Based Prediction of Energy Consumption from Blockchain-Based Black Box Data for Electric Vehicles

In modern society, the proliferation of electric vehicles (EVs) is continuously increasing, presenting new challenges that necessitate integration with smart grids. The operational data from electric vehicles are voluminous, and the secure storage and management of these data are crucial for the efficient operation of the power grid. This paper proposes a novel system that utilizes blockchain technology to securely store and manage the black box data of electric vehicles. By leveraging the core characteristics of blockchain—immutability and transparency—the system records the operational data of electric vehicles and uses federated learning (FL) to predict their energy consumption based on these data. This approach allows the balanced management of the power grid’s load, optimization of energy supply, and maintenance of grid stability while reducing costs. Additionally, the paper implements a searchable black box data storage system using a public blockchain, which offers cost efficiency and robust anonymity, thereby enhancing convenience for electric vehicle users and strengthening the stability of the power grid. This research presents an innovative approach to the integration of electric vehicles and smart grids, exploring ways to enhance the stability and energy efficiency of the power grid. The proposed system has been validated through real data and simulations, demonstrating its effectiveness and performance in managing black box data and predicting energy consumption, thereby improving the efficiency and stability of the power grid. This system is expected to empower electric vehicle users with data ownership and provide power suppliers with more accurate energy demand predictions, promoting sustainable energy consumption and efficient power grid operations.

A latent-based segmentation framework for the investigation of charging behaviour of electric vehicle users

Electrification of transport is deemed by many countries worldwide as one of the key strategies to mitigate CO2 emissions, yet the availability of reliable public charging infrastructure systems represents a potential serious bottleneck to such endeavours. Existing studies exploring battery electric vehicle (BEV) charging behaviour are typically based on either non-representative samples or stated choices experiments. This paper analyses observational data from a representative sample of German BEV owners who provided information on mileage and charging activities over a timeframe of eight weeks. BEV charging patterns, related vehicles kilometres travelled (VKT) and battery charging behaviour are assessed via a multifaceted empirical framework that pairs a hazard survival-based model with a log linear regression approach. A latent class method is also employed to segment BEV owners into different charging segments. The model suggests two types of charging behaviour exist, consisting of regular and irregular chargers. Charging frequencies and patterns are found to be radically different between the two groups under study, with regular chargers estimated to charge their vehicles 1.5 times more than irregular chargers. Lastly, the framework proposed is used to explore how charging behaviour will mutate due to both technology advancements (BEV driving range improvements) and user-centric factors (VKT variations). Neither technological or user factors are predicted to substantially affect the inter-charging duration of irregular chargers, whereas both increasing BEV driving ranges and reducing VKT results in a longer elapsed time between two consecutive charges for regular chargers.

A game-theoretic approach to mitigate charging anxiety for electric vehicle users through multi-parameter dynamic pricing and real-time traffic flow

While price-based demand response is universally recognized as crucial for managing electric vehicle charging load, the academic literature has explored diverse mechanisms for its implementation. Prior research has revealed that applying load management schemes based on price-based demand response programs results in higher scheduling costs for low or constant-energy consumers. In addition, the inherent uncertainties involved in arrival, service requirements, market pricing and user behaviours have posed serious challenges in managing real-time charging operations. To handle these challenges, this work has addressed the charging problem at the scheduling level by analysing arrival, waiting, service and departure requests. This work proposed a mathematical model to coordinate charging to minimize the total charging cost and time for a given number of vehicles. We have developed an advanced charging algorithm and introduced a multi-leader multi-follower Stackelberg game. This strategic model ensures a Nash equilibrium, aligning the interests of consumers (vehicles) and providers (charging stations), ensuring a fair and balanced charging system. Where, charging load demand, real-time pricing, charging location, and vehicle types are used as input parameters. In the first stage, the real-time pricing obtained from the Spanish Electricity network is used to schedule the charging load, whereas the second stage is dedicated to using the proposed multi-parameter pricing for customer satisfaction and reduced cost. The results demonstrate the superior performance of our proposed scheme compared to existing approaches. Finally, we present a real-life case study to illustrate the practical effectiveness of the proposed techniques. The study underscores the potential of our approach in real-world scenarios, offering a promising avenue for enhancing efficiency and user experience in charging systems.

Electric Vehicle Usage Patterns in Multi-Vehicle Households in the US: A Machine Learning Study

Electric vehicles (EVs) play a significant role in reducing carbon emissions. In the US, EVs are mostly owned by multi-vehicle households, and their usage is primarily studied in the context of vehicle miles traveled. This study takes a unique approach by analyzing EV usage through the lens of vehicle choice (between EVs and internal combustion engine vehicles) within multi-vehicle households. A two-step machine-learning framework (clustering and decision trees) is proposed. The framework determines the preferred trip category for EV use and captures the effects of household attributes, driver attributes, built-environment factors, and gas prices on EV use in multi-vehicle households. Results indicate that discretionary trips (accumulated local effect = 0.037) are mostly preferred for EV use. EV preference is more pronounced among households with fewer workers (<2) and lower income levels. These findings are valuable for policymakers and auto manufacturers in targeting specific market segments and promoting EV adoption.

Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm

The rapid growth of Electric Vehicles (EVs) has led to issues such as insufficient and unevenly distributed charging stations, posing an increasingly severe challenge. This not only means higher economic and time costs for EV users traveling to charging stations, but also indicates that EVs need to incur additional energy consumption. To address these challenges, this paper first establishes an extended space-time-state network to describe the temporal variability and uncertainty of the traffic environment. Secondly, considering the impact of the energy consumption incurred by EVs traveling to charging stations (CSs) on the layout of CSs, a co-evolutionary optimization model of the location-routing problem is proposed. To solve the model, a two-stage Adaptive Co-evolutionary Clustering Algorithm (ACECA) integrating an adaptive clustering framework and a co-evolutionary mechanism is designed. Finally, the experiment results indicate that there exists a game between a short-term economic investment and long-term benefits of energy conservation and emission reduction. Moreover, ACECA demonstrates stronger solution performance and robustness compared to other algorithms, with the resulting CS location schemes showing superior advantages in energy conservation and in cost saving for users. The research findings can provide theoretical support for the planning of charging station locations for electric vehicles.

Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments

With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper proposes a bi-level planning model for EV charging stations that takes into account the characteristics of the spatial–temporal distribution of charging loads under an uncertain environment. First, the Origin–Destination (OD) matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced and combined with the Larin Hypercube Sampling (LHS) method to establish the EV charging load prediction model considering the spatial and temporal distribution characteristics. Second, the upper objective function with the objective of minimizing the cost of EV charging station planning and user charging behavior is constructed, while the lower objective function with the objective of minimizing the cost of distribution network operation and carbon emission cost considering the uncertainty of wind power and photovoltaics is constructed. The constraints of the lower-layer objective function are transformed into the upper-layer objective function through Karush–Kuhn–Tucker (KKT) conditions, the optimal location and capacity of charging stations are finally determined, and the model of EV charging station siting and capacity determination is established. Finally, the validity of the model was verified by planning the coupled IEEE 33-node distribution network with the traffic road map of a city in southeastern South Dakota, USA.