Electric Vehicles Research Articles

A clustering method for charging behaviors of electric vehicle users based on an improved k-means algorithm

Abstract The uncertainty of Electric vehicle users’ (EVUs) charging behaviors affect the power grid safety. Therefore, mining the charging behavior characteristics of EVUs can help the power grid to make optimal operation plans previously to smooth the load curve and improve stability. In this regard, a clustering strategy for EVUs is proposed, using an improved K-means algorithm based on the minimum variance theory(MVT). Additionally, the Monte Carlo algorithm is utilized to obtain the simulation results of EVUs. Finally, a numerical simulation based on actual data in China is established to validate the effectiveness and reliability of the proposed cluster algorithm. The results indicate that the proposed cluster algorithm can accurately extract the characteristics of EVUs’ off-on grid behaviors and obtain the accurate daily charging demand representing the main charging rules among EVUs. The proposed algorithm results in a reduction of 69.2% of solution time compared to the traditional K-means algorithm.

Load forecasting based on multi-core learning Support Vector Machine (SVM)

Abstract The development of smart grids requires enhanced data integration, robust risk assessment, and dynamic response optimization. In this paper, a multi-core learning Support Vector Machine (SVM) model is presented to improve the accuracy and efficiency of load and photovoltaic output forecasting. The model leverages kernel function optimization and parallel computing frameworks to handle large-scale data efficiently. Additionally, a comprehensive risk assessment system is developed to quantify risks such as overvoltage, undervoltage, line overload, and load loss in distribution networks. An adaptive genetic algorithm-based risk control model is also proposed, optimized in two stages—day-ahead and intra-day—to achieve minimal comprehensive risk through real-time adjustments in distributed power output and electric vehicle charging strategies. Furthermore, an integrated virtual synchronous control online verification method for source-network-load-storage is introduced, enhancing system response speed and control accuracy. These innovations collectively provide a solid theoretical foundation and technical support for the efficient and safe operation of smart grids, addressing the increasing demands of modern energy systems.

Adaptive control of plug-in hybrid electric vehicles based on energy management strategy and dynamic programming algorithm

This study mainly analyses the fuel consumption of plug-in hybrid vehicles during operation. A new control method for automobiles based on energy management strategy and dynamic programming algorithm is proposed. The new method plans and analyses the minimum electricity consumption, and then uses dynamic programming algorithms to analyse this parameter. The research results indicated that the vehicle state was constantly changing with the variation of SOC value during driving. The energy mobilization of the vehicle was more obvious after adding dynamic programming strategy. The efficiency of the vehicle was relatively high in driving state 1, with a minimum value of 70%, which was about 20% higher than in driving state 4. The average fuel consumption in driving state 2 was 1.8L higher than in other driving states. The overall efficiency of automobiles after incorporating dynamic programming was improved, with a shorter time to reach the lowest efficiency point compared with not incorporating dynamic programming algorithms. The highest efficiency value was 7.86% higher than that of not incorporating dynamic programming models. The new control method can reduce energy consumption and improve the energy management and control effect. The study provides a better research direction for energy management and control of hybrid electric vehicles in the future.

Research on control strategy of electric vehicle thermal management system motor

Abstract The low efficiency and difficulty in low-temperature heating are the challenges currently faced by the thermal management system of new energy vehicles. A multi-heat source vehicle thermal management system for waste heat cascade recovery and utilization was proposed, and the performance of the vehicle thermal management system was studied through a combination of component testing and one-dimensional simulation. Based on a one-dimensional simulation model, the characteristics of the motor waste heat source are analyzed. The increase in the inlet water temperature of the motor coolant will lead to enhanced heat dissipation of the motor to the air, resulting in a waste of motor waste heat. Based on the results of the analysis, an energy cascade management strategy is constructed. The optimal heating mode is selected by collecting temperature signals such as motors, and the cut-off valve is controlled through logic gates to achieve flexible switching of different modes. The system effectively reduces the energy consumption of thermal management systems, which is of great significance for developing thermal management systems for new energy vehicles.

Design of an Axial-Field Flux-Modulated Permanent Magnet Machine for Electric Vehicles

Design of an Axial-Field Flux-Modulated Permanent Magnet Machine for Electric Vehicles

A Combinatory AC and DC Charging Approach for Electric Vehicles

A Combinatory AC and DC Charging Approach for Electric Vehicles

Investigating the effects of variable pulse charging on temperature during charging of battery electric vehicles

This study investigates the efficacy of variable pulse charging (VPC) on charging 18,650 secondary battery packs (12 V, 20 Ah) with NMC chemistry. VPC, a modern technique applied to secondary battery charging, aims to mitigate effects like a thermal runaway and thermal propagation caused by increased charging temperature. VPC involves varied duty factors (10 % to 90 %), charging rates (0.5C, 1C, 1.5C), and an optimal switching frequency determined through frequency response analysis. Its digital model is based on in situ electrochemical impedance spectroscopy measurements and MATLAB/Simulink simulations. At charging rates of 0.5C (10 A), 1C (20 A), and 1.5C (30 A), the temperature of the battery pack reaches 42.46 °C, 57.87 °C, and 70.37 °C, respectively. However, implementing VPC at a 50 % duty factor yields temperature reductions of 3.66 °C, 5.06 °C, and 5.42 °C, respectively. Similarly, employing VPC at a 10 % duty cycle results in temperature reductions of 11.2 °C, 17.7 °C, and 19.1 °C, respectively. The results indicate a significant reduction in charging temperature compared to constant current charging. Furthermore, the 1C condition is validated using a custom-made dual active bridge DC-DC variable pulse charger. In conclusion, applying optimal frequency-based VPC with specific duty factors demonstrates the potential to reduce temperature elevation during battery pack charging significantly.

Fuel constrained sustainable water-gas-heat-power generation expansion planning of isolated microgrid

On account of slowly reducing of fossil-fuel, profitable utilization of available fossil-fuel to produce electric power is a major concern for electric power producers. Here, short-period fuel constrained power, heat, natural gas and clean water augmentation planning (FCPHGWAP) of isolated microgrid considering carbon capture is presented. FCPHGWAP finds out the most economical and consistent augmentation plan to meet the prophesied power, heat, natural gas and clean water demand over a short-term time horizon whilst satisfying several technical as well as societal constraints. An archetypical system having extant diesel generators (DGs), PVT panel, wind turbine generator (WG), micro-cogeneration unit (MCU), battery energy storage system (BESS), plug-in electric vehicles (PEVs), thermal energy storage system, natural gas storage unit, carbon capture unit (CCU), electrolyzer, hydrogen storage unit and aspirant PVT panel, WGs, MCUs and PEVs is taken into consideration. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and grey wolf optimization are used to solve this FCPHGWAP problem. It is observed that net present value having fuel constraints is less than that of without fuel constraints.

An Optimal Two-Dimensional Maintenance Policy for Self-Service Systems with Multi-Task Demands and Subject to Competing Sudden and Deterioration-Induced Failures

Self-service systems, such as electric vehicle charging piles (EVCPs), are typically deployed without on-site personnel. While frequent maintenance ensures high service revenue, it also leads to significant maintenance setup costs. Therefore, balancing service revenue and maintenance costs is essential for profit maximization. In this paper, we develop a maintenance policy optimization framework to maximize the profit rate of a fleet of self-service systems. First, we propose a maintenance policy that ensures sufficient functional systems while preventing high corrective maintenance costs. Next, we model the fleet's state transition process and its profit rate by characterizing two unique failure-induced demand-and-system interactions: demand switching and stepwise demand arrival rates, where the demands involve multiple tasks and systems are subject to multiple failure modes with non-constant occurrence rates. We develop a Tabu-search algorithm with random exploration to optimize the maintenance policy. Building on this, we investigate the impacts of key model parameters through a case study of thirteen EVCPs in Hong Kong and draw implications for profit maximization.

Lithium‑sulfur batteries for next-generation automotive power batteries carbon emission assessment and sustainability study in China

The rise of electric vehicles has ushered in a revolution in the automotive industry, propelling the global automotive sector towards sustainable development. However, considerable controversy persists regarding the comprehensive performance of electric vehicle batteries and their environmental impact. The development of next-generation power batteries, aimed at enhancing energy storage performance while mitigating environmental consequences, has become a focal point of research in this field. Lithium‑sulfur batteries (LSBs), with their innovative structural design and environmentally friendly materials, not only enhance energy storage performance but also harbor significant environmental potential. When coupled with an all-solid-state battery structure, the all-solid-state lithium‑sulfur battery (A-LSB) demonstrates even more superior performance. This combination represents an ideal next-generation power battery for automotive applications, offering heightened efficiency and environmental sustainability. This study conducts a prospective life cycle assessment (LCA) to examine the environmental performance, particularly carbon emissions, during the production processes of LSBs and A-LSBs in the Chinese region. The primary objective is to uncover the potential for sustainable development in the future of lithium‑sulfur battery technologies. During the research process, we conducted comparative validation by comparing with traditional ternary lithium-ion batteries (NCM) and lithium iron phosphate batteries (LFP). Experimental setups were designed to simulate both laboratory and industrial production scenarios, with a primary focus on analyzing the sustainable performance of LSB and A-LSB. The results indicate that, compared to traditional lithium-ion batteries, both LSB and A-LSB exhibit lower carbon emissions and superior environmental performance, with A-LSB showing particular promise. However, A-LSB demonstrates higher sensitivity to environmental impacts, displaying significant variations before and after industrial production. Additionally, it exhibits a greater dependency on regional resources and energy structures. In contrast, LSB displays better regional adaptability.

Automatic control algorithm for V2G frequency response mode of electric vehicle charging station

Abstract To solve the problem of excessive fluctuation of the V2G load of the electric vehicle charging pile, this paper proposes an automatic control algorithm for the V2G frequency response mode of the electric vehicle charging pile. The FPGA is used as the core processing unit to collect and process the V2G frequency data of the charging pile; the multiple linear regression model is used to predict the charging and discharging loads, and the automated two-layer control model is established; the sparrow search algorithm is used to optimize the weights and thresholds of the BP neural network to realize the control of the V2G frequency response mode. The experimental results show that the load prediction error of this method is small. After the automatic control, the load fluctuation of the charging pile is small, and the electric vehicle charging pile’s V2G frequency response control effect is better.

A novel approach to control thermal induced buckling during laser welding of battery housing through a unilateral N-2-1 fixturing principle

Battery housing (BH) in modern electric vehicles must meet demanding functional requirements. The design and geometry of the BH become intricate to prevent damage during collisions and to ensure absolute impermeability to gases and water during operation. Moreover, in the pursuit of a lightweight BH, manufacturers rely on high-strength 6xxx aluminium alloys, posing significant challenges for the welding processes. It is estimated that up to 30 m of weld length is required during the construction of battery housings including joining the lid and under-shield to the main structural frame and joining the ribs to the frame for standard vehicles. Due to the increasing use of thin sheets for lightweighting the structure, thermal-induced buckling may occur and generate critical dimensional unconformities going beyond design tolerances. This underpins the need to optimise fixturing design to control thermal-induced buckling.This paper goes beyond the state-of-the-art “N-2-1” approaches for fixturing thin and deformable parts and proposes the new principle of “unilateral N-2-1 fixturing”. The driving idea is adding unilateral restraints to the direction of thermal contraction, which ultimately causes buckling; and, keeping the direction where the thermal expansion occurs in a free state. The methodology is based on three main steps: (1) physics-based modelling of parts and fixtures using a thermo-mechanical FEA simulation; (2) calibration of the weld heat source using metallographic data; (3) validation using optical scanning technology. The methodology was demonstrated during the laser beam welding of a high-strength aluminium 6xxx thin deformable lid to a rigid high-strength 6xxx aluminium extrusion frame. Results indicated that the thermal induced buckling deformation was reduced from 15 mm, when using the state-of-the-art fixturing approach, to approximately 2 mm with the proposed methodology.

Petrol Pumps to Power Plugs: Is the Surge in Electric Two-Wheeler Adoption in Kathmandu Due to Unstable Petroleum Prices?

Since the dawn of the 21st century, the awareness towards environmental concern and sustainability slowly began to rise and the “Electric Wave” truly encompassed the world with electric automobiles. Nepal was no exception to this global electric wave. This research explores a key determinant behind this transition, the influence of fluctuating petroleum price. Using secondary data obtained from registrations of electric and petroleum two-wheelers at the Transport Management Office in Kathmandu, under the Ministry of Labor, Employment, and Transport as well as historical petrol prices over the past 4 years from 2019 to 2023, this research seeks to offer valuable insights into the connection between petrol prices and the adoption of electric vehicles in Kathmandu, Nepal. Using SPSS software, a linear regression analysis was conducted and it revealed that while petroleum prices played a role, they were not the sole determinant affecting adoption. This study contributes to the growing body of knowledge on sustainable transportation choices and their drivers. It provides valuable insights for policymakers, consumers, and industry stakeholders, as it underscores the influence of fuel prices on consumer choices and the potential for electric two-wheelers to become a viable alternative in the context of Kathmandu’s urban transport.

Drivetrain Modelling of Hybrid Electric Vehicles

Drivetrain Modelling of Hybrid Electric Vehicles

The Moderating Roles of Environmental Awareness and Product Knowledge on the Impact of Marketing Mix Elements on Purchase Intention

The purpose of this study is to examine the moderating effects of environmental awareness (EA) and product knowledge (PK) on the relationship between marketing mix elements and the purchase intention of electric vehicles (EVs) in Thailand. Based on a quantitative research design, 253 samples of Thai respondents aged 20 and above were collected via an online questionnaire as a research tool. Structural Equation Modeling (SEM) analysis was used to analyze the proposed hypotheses. The results indicate that all four marketing mix elements, which are product attributes, pricing strategy, distribution channels (place), and promotional activities, significantly influence the purchase intention of electric vehicles. Product attributes (b = 0.523) have the strongest impact on purchase intention, followed by pricing strategy (b = 0.501), distribution channels (place) (b = 0.477), and promotional activities (b = 0.334) respectively. The results of moderation analysis reveal that environmental awareness significantly moderates the effect of product attributes (b = 0.412) and pricing strategy (b = 0.305) on purchase intention but is not found in the relationships of distribution channels (place) and promotional activities toward purchase intention. Additionally, consumer product knowledge of EVs was found to significantly and negatively moderate only the influence of promotional activities (b = -0.343) on purchase intention. In other words, potential consumers who possess higher EV knowledge seem to be less influenced by promotional activities in their decision to buy EVs. These findings highlight the importance of integrating environmental concerns and EV product knowledge into the firm’s marketing strategies to boost EV adoption in emerging countries. The implications and recommendations will be discussed in the paper.

A Study on the Mid-frequency Tire-Sourced Cabin Noise in Electric Vehicles

ABSTRACT This study investigates and provides a framework for addressing a reported tire- sourced cabin noise in the mid-frequency range of 300–500 Hz for an electric vehicle (EV). Noise, vibration, and harshness (NVH) of EVs present new challenges compared with internal combustion engine vehicles because of significant design changes between the two vehicle types. In turn, the tire–road interaction noise becomes a more significant source of cabin noise in EVs. In this regard, some prominent EV manufacturers recently reported relatively high measured cabin noise, particularly in the mid-frequency range of 300–500 Hz. Identifying the root cause(s) of noise in that range is a challenging task, as both tire structure–borne and airborne sources are contributors in that frequency range of elevated noise. The current work presents the results of an experimental modal analysis to provide insight into some potential sources of the reported noise for an all-season tire/wheel assembly designed for an EV. The subsequent parametric simulations, conducted via the tire–vehicle finite element model, evaluate some of the mitigation solutions for the reported noise.

Hierarchical Optimal Dispatching of Electric Vehicles Based on Photovoltaic-Storage Charging Stations

Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the “curse of dimensionality” and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services.

Control of a BLDC Motor of an Electric Vehicle Powered by a Photovoltaic System based on Fuzzy Logic MPPT (FLC-MPPT)

Photovoltaic Systems (PV) represent a viable solution for converting solar energy into electricity without emitting harmful gases. This technology remains highly sought-after in the market, primarily due to its renewable characteristics and the absence of movable components, which renders it a low-maintenance option. This produced energy can be stored in batteries tobe used for example in operating electric vehicles. If the generated voltage is inadequate or requires regulation, a boost converter may be employed or a buck boost To manage energy during storage and discharge to provide the energy needed to run the induction motor. In this paper, we proposed the field-oriented control as an approach for controlling the speed of BLDC Motor drive electrical vehicles to improve the overall effectiveness of the proposed system, The BLDC Motor is powered by battery storage from a photovoltaic system. Furthermore, this work also addresses the specifics of modeling the Fuzzy logic control MPPT (FLC-MPPT) used for the energy management system (PV and batteries), as well as the proposed control approach for the electric vehicle motor, which are subsequently simulated in the MatLab environment.

Solar Wireless Electric Vehicle Charging System

This project outlines the design and implementation of a solar-powered electric vehicle charging station that addresses the dual challenges of high gasoline prices and harmful emissions. The number of countries adopting electric vehicles continues to rise, as they not only promote environmental sustainability but also significantly lower transportation costs by substituting expensive fuels with more affordable electricity. In this context, developing an electric vehicle charging infrastructure offers an innovative and effective solution. This system enables electric vehicles to charge while in motion, eliminating the need for stationary charging stops. Powered entirely by solar energy, it requires no additional power supply. The system incorporates solar panels, batteries, transformers, regulator circuits, copper coils, AC/DC converters, Atmega328P controllers, and LCD displays. This technology demonstrates the feasibility of an on-road solar-powered wireless charging system for electric vehicles.

Towards Sustainable Decarbonization: Addressing Challenges in Electric Vehicle Adoption and Infrastructure Development

The transition to electric vehicles (EVs) plays a pivotal role in achieving decarbonization within the transportation sector. However, the widespread adoption of EVs faces multifaceted challenges, particularly concerning infrastructure development. This paper investigates the intersection of sustainability, decarbonization, and EV adoption, with a focus on identifying and analyzing the challenges associated with infrastructure deployment. Strictly adhering to the methodological principles and process of systematic literature reviews, this paper analyzes research spanning the fields of engineering, energy, computer science, environmental science, social sciences, and others to elucidate the barriers hindering EV adoption, ranging from technological limitations to regulatory complexities and market dynamics. Furthermore, it examines the critical role of infrastructure, encompassing charging networks, grid integration, and supportive policies, in facilitating EV uptake and maximizing environmental benefits. The findings are finally used to present the implications for theory, practice, and policies and to highlight the avenues for future research.