An power-train model based on the internet of things for thingspeak data tracking and EV performance evaluation

A battery’s state of charge (SOC) is used to assess its residual capacity. It is a very important parameter for the control of the electric vehicle (EV). The objective of this paper is to estimate the SOC of a lithium-ion battery (LIB) using an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) because SOC of a battery must be estimated from measurable battery parameters such as current, voltage or temperature. Two intelligent SOC estimation methods are compared according to their suitability and accuracy. ANN estimation is more precise and perfectly represents the experimental data.

The negative consequences of toxic emissions from internal combustion engines, energy security, climate change, and energy costs have led to a growing demand for clean power sources in the automotive industry. The development of eco-friendly vehicle technologies, such as electric and hydrogen vehicles, has increased. This article investigates whether hydrogen vehicles will replace electric vehicles in the future. The results showed that fuel-cell cars are unlikely to compete with electric cars. This is due to the advancements in electric vehicles and charging infrastructure, which are becoming more cost-effective and efficient. Additionally, the technical progress in battery electric vehicles (BEVs) is expected to reduce the market share of fuel-cell electric vehicles (FCEVs) in passenger vehicles. However, significant investments have been made in hydrogen cars. Many ongoing investments seem to follow the sunk cost fallacy, where decision-makers continue to invest in an unprofitable project due to their already invested resources. Furthermore, even with megawatt charging, fuel-cell trucks cost more than battery-powered electric trucks. The use cases for fuel-cell electric trucks are also much more limited, as their running expenses are higher compared to electric cars. Hydrogen vehicles may be beneficial for heavy transport in remote areas. However, it remains to be seen if niche markets are large enough to support fuel-cell electric truck commercialization and economies of scale. In summary, we believe that hydrogen vehicles will not replace electric cars and trucks, at least before 2050.

The performance analysis of electric vehicles (EVs) under varying drive cycles is crucial for optimizing energy efficiency and range. This research focuses on evaluating EV performance by simulating and analyzing behavior across diverse driving conditions, represented by standardized drive cycles. These cycles, such as the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and urban driving cycles, replicate real-world driving patterns, including acceleration, deceleration, and speed variations. The study investigates key performance indicators, including energy consumption, battery state of charge, and motor efficiency, under each drive cycle. By comparing these metrics, the impact of different driving styles and environments on EV performance is assessed. This analysis provides valuable insights for improving EV design, battery management systems, and energy optimization strategies. Ultimately, this research aims to enhance the overall efficiency and practicality of EVs for widespread adoption. Key Words : Electric vehicle (EV); Drive Cycle Performance Analysis ;Energy Consumption ; Battery State of Charge (SOC) ; Motor Efficiency; Optimization, Range; WLTC (Worldwide Harmonized Light Vehicles Test Cycle)

Problem. In connection with the increase in efficiency and environmental safety of cars, electric vehicles and hybrid cars were introduced into mass production. In the world, their number is growing every year. In Ukraine, the hybrid car Toyota Prius is used by the patrol police of Ukraine, and Nissan Leaf electric cars are widely introduced into the taxi network. For the owners of these cars, it is important to understand the features of their technical service, diagnosis and maintenance. Purpose. The aim of the work is to study the basic structures and modifications of lithium-ion batteries and to determine the rules for the technical operation of these batteries on hybrid and electric cars. Methodology. The design features of lithium-ion batteries and their varieties, which have received practical development on the example of two main types of batteries - with cylindrical and packet cells, which are used on electric vehicles from leading companies Tesla and Nissan, are analyzed. Results. The limiting technical (diagnostic) parameters that determine the operating rules for lithium-ion batteries on electric vehicles are highlighted: this is the maximum voltage on the battery element 4.30 V, the maximum heating temperature of the battery cell to 90 ° С, the threshold pressure level inside Cases 1034 kPa (10.5 kg / m2), the minimum voltage on the battery cell is 2.5 V. Originality. Through an analysis of the design features and diagnostic parameters of Li-Ion batteries, the rules of technical operation and recommendations for increasing the service life of these batteries on hybrid and electric cars in Uraine are considered. Practical value. The results of the research allow us to make certain recommendations for increasing the service life of lithium-ion batteries on hybrid and electric cars when used in Ukraine.Keywords: lithium-ion battery; electric vehicle; hybrid; diagnostics; malfunction; operation; service life.

A modal-based approach for estimating electric vehicle energy consumption in transportation networks

<p>Electric vehicle (EV) has gained incredible interest from the past two decade as one of the hopeful greenhouse gasses solution. The number of Electric Vehicle (EV) is increasing around the world; hence that making EVs user friendly becomes more important. The main challenge in usage of EV is the charging time required for the batteries used in EV. As a consequence, this subject matter has been researched in many credentials where a wide range of solutions have been proposed. However those solutions are in nature due to the complex hardware structure. To provide an unswerving journey an Android application based charging optimization is proposed. This application is aimed at giving relevant information about the EV’s battery state of charge (SOC), accurate location of the EV, booking of the charging slots using token system and route planner. At emergency situations, an alternative service is provided by mobile charging stations. Route planner indicates the temperature by which prediction of reaching the destination can be done. In addition to that nearest places such as parks, motels are indicated. The estimated time and distance between the electric vehicle and the charging station is calculated by the charging station server according to which the parking lot is allocated. Vehicle to charging station communication is established for the time estimation of charging. This will help the EV users to know about charge status and charging station, which support fast charging method and availability of the station on the go and also when to charge their EV. The Arduino UNO board has been used for the hardware part. The hardware results are confirming the conceptual of the proposed work.</p>

The modern trends in converting a gasoline-powered vehicle (Diesel, Petrol, or CNG) into an electric vehicle requires an appropriate redesign in the placement of new electrical components. These new trends of approaches are often applied to vehicles that are manufactured for the sole purpose of operating a fully electric vehicle without any gasoline components. However, in the case of retrofitting a gasoline-powered vehicle into a battery-powered electric vehicle (EV), different approaches are used in an already running vehicle that can be converted into a fully functioning Electric Vehicle. This results in better recycling, environment-friendly usage of old vehicles, and a massive improvement in air pollution levels. A minimum amount of maintenance while owning an Electric Vehicle is a key factor where the automobile industry gains attention for buyers or customers. The conversion of traditional or conventional vehicles into fully functioning Electric Vehicles has gained preference in recent years. However, the components used in an Electric vehicle are to be discussed to fully assess the working of an EV in the process of this conversion. In this paper, we shall focus particularly on the implementation of such conversions and controlling of the electric vehicle through different types of Electric Vehicle Control. The functions of each component in an Electric Vehicle Controller or just a controller in general, show different approaches to communicating with each other to achieve an efficient range and performance of the particular electric vehicle. The relations between these components inside of an EV controller can be studied by their energy flow, circuitry, and their functions. Control Area Network (CAN) is also considered as a topology to determine the aforementioned working of each component. Fault diagnosis, charging status, and battery capacity status are some of the many factors that are applicable after installing a controller within the Electric Vehicle.

A Digital Twin (DT) for Electric Vehicles (EVs) is a virtual replica or digital model of a physical EV that captures its characteristics, performance, and real-time behavior. This study presents the development of a comprehensive DT model for EVs, designed to enable real-time monitoring, performance optimization, and predictive maintenance. The suggested framework features physical modeling and data-driven analytics to replicate the dynamic behavior of the EV powertrain, with particular emphasis on the battery, motor, and thermal subsystems. Using real driving cycle data (FTP-75), the DT continuously assesses key performance indicators (KPIs) such as battery state of charge (SoC), state of health (SoH), temperature, and motor torque to indicate real-world operating conditions. A degradation-aware battery model and simplified thermal dynamics are integrated to estimate remaining useful life (RUL) and enhance reliability. DT's predictive capability enables early fault detection and adaptive control strategies, minimizing downtime and advancing energy efficiency. Simulation results demonstrate the model's ability to accurately track SoC and thermal responses with minimal estimation error, validating its capability for integration in intelligent EV management systems. This innovative approach aims to improve the overall efficiency and reliability of EVs, thereby supporting a more sustainable transportation ecosystem.

Battery Monitoring is very important for most of the electric vehicle(EV) and battery energy storage system (BESS) since the safety, operation and even the life of the passenger/operator depends on the battery system. Checking and controlling the status of battery within their specified safe operating conditions is exactly the major function of battery management system (BMS). The state of charge (SOC) is a critical parameter of a Li-ion battery, an accurate on-line estimation of the SOC is important for forecasting the EV driving range and BESS power dispatching. A widely used method to estimate SOC is based on coulomb counting, due to the uncertainty, including unit-to-unit variation, measurement noise, operational uncertainties, and model inaccuracy, it's difficult to estimate the SOC by using coulomb counting, an online inference of open-circuit voltage (OCV) is used to calibrate the SOC, however, a relatively flat OCV curve of LiFePO4 will lead to the error of SOC is large. In this paper, an algorithm using the derivation curve (dV/dQ vs Q) to calibrate the SOC on-line is bring out. The SOC estimation has been implemented using coulomb counting and derivation curve methods thereby eliminating the limitation of the stand-alone coulomb counting method. Finally, by utilizing the actual operation data, experiments and numerical table were conducted, show that this SOC calibrate algorithm based on characteristic curve has better robust performance of the practical application of LiFePO4 battery energy storage system.

Due to recent developments in electric mobility, public charging infrastructure will be essential for modern transportation systems. As the number of electric vehicles (EVs) increases, the public charging infrastructure needs to adopt efficient charging practices. A key challenge is the assignment of EVs to charging stations (CSs) in an energy efficient manner. In this paper, a Reinforcement Learning (RL)-based EV Assignment Scheme (RL-EVAS) is proposed to solve the problem of assigning EV to the optimal CS in urban environments, aiming at minimizing the total cost of charging EVs and reducing the overload on Electrical Grids (EGs). Travelling cost that is resulted from the movement of EV to CS, and the charging cost at CS are considered. Moreover, the EV’s Battery State of Charge (SoC) is taken into account in the proposed scheme. The proposed RL-EVAS approach will approximate the solution by finding an optimal policy function in the sense of maximizing the expected value of the total reward over all successive steps using Q-learning algorithm, based on the Temporal Difference (TD) learning and Bellman expectation equation. Finally, the numerous simulation results illustrate that the proposed scheme can significantly reduce the total energy cost of EVs compared to various case studies and greedy algorithm, and also demonstrate its behavioural adaptation to any environmental conditions.

The electrification of vehicles from the automotive and public transport industries can reduce harmful emissions if implemented correctly, but there is little evidence of whether the electrification of heavy freight transportation vehicles (HFTVs), such as multi-articulated vehicles, used in the freight industry could see the same benefits. This work studied heavy multi-articulated freight vehicles and developed a comparative analysis between electric and conventional diesel power trains to reduce their total emissions. Real-world drive cycle data were obtained from a heavy multi-articulated freight vehicle operating around Melbourne, Australia, with a gross combination mass (GCM) of up to 66,000 kg. Numerical models of the case study freight vehicle were then simulated with diesel, through-the-road parallel (TTRP) hybrid and electric power trains over the five different drive cycles with fuel and energy consumption results quantified. Battery weights were added on top of the real-world operating GCMs to assure the operational payload did not have to be reduced to accommodate the addition of electric power trains. The fuel and energy consumptions were then used to estimate the real-world emissions and compared. The results showed a positive reduction in tailpipe emissions, but total greenhouse emission was worse for operation in Melbourne if batteries were charged off the grid. However, if Melbourne can move towards more renewable energy and change its emission factor for generating electricity down to 0.49 kg CO2-e/kWh, a strong decarbonization could be possible for the Australian road freight industry and could help meet emission reduction targets set out in the 2015 Paris Agreement.

The global warming and climate change problem can be solved by emitting less greenhouse gases by transportation. Switching from fossil fuel burning cars to electric vehicle cars is one of the most promising solutions, however; they are as clean as their energy source. In this study, a generic model to estimate energy generation from PV-covered parking lot system and energy demand from electrical cars is established, and Istanbul is selected for a case study. Two main effects are investigated in the demand side: i) ambient temperature and; ii) driving style. In this study, it is considered that a 150 m2 parking lot for 10 cars is covered with 90 PV modules, size of 22.5 kW, to charge EVs in Istanbul, Turkey. The results show that the total annual energy consumption can be covered by a grid-connected PV system. Additionally, an off-grid PV system can cover about 198 and 268 days of energy demand in a year for the urban and highway driving conditions, respectively. Overall, ambient and driving conditions can affect the energy demand by 40%–60%.
 
 Keywords: Driving cycles, electric vehicle, solar charging, temperature effect.

The increasing adoption of electric vehicles (EVs) presents significant implications for transportation patterns, particularly in route choice behavior. This research investigates the factors affecting EV drivers' route selection decisions for long-distance travel, focusing on charging infrastructure accessibility, environmental awareness, and range anxiety concerns. We conducted a combined revealed preference (RP) and stated preference (SP) survey using D-efficient experimental design to analyze route choice preferences among 600 EV drivers undertaking trips over 100km. The experimental design incorporates multiple attributes such as route type, charging station availability, traffic conditions, weather patterns, and battery state of charge. Our approach enables comprehensive examination of both infrastructure factors and environmental considerations in EV route selection behavior. Route choice data analysis employed multinomial logit modeling techniques. Findings reveal that travel cost has a strong negative influence on route selection, while charging infrastructure density demonstrates minimal impact on route choice decisions. Traffic flow patterns and weather conditions constitute key factors in route decision-making processes. The study makes three primary contributions to existing literature: first, through integrated analysis of infrastructure availability and environmental consciousness in EV route selection while considering vehiclespecific attributes; second, by demonstrating empirical relationships between charging infrastructure characteristics and EV route choice patterns; and third, through quantitative assessment of connections between environmental awareness and actual routing decisions among EV drivers. These results enhance theoretical understanding of EV route choice behaviors and offer evidence-based insights for strategic charging infrastructure development during the transition toward sustainable mobility systems.

The environment and the world's energy system currently face formidable obstacles. Energy usage is more environmentally friendly in the context of the energy internet as transportation fleets switch to electric, plug-in, and fuel-cell vehicles. To guarantee energy security, avoid air pollution, and encourage energy conservation and emission reduction, the smart grid's electric car, and renewable energy synergies are of utmost importance. The combination of renewable energy and electric vehicles, however, is fraught with difficulties because of the randomness, intermittent nature, and electrical nature of renewable energy. This article first illustrates the smart grid design and the relationship between electric vehicles and renewable energy sources. The effects of electric vehicles and the growth of renewable energy sources are then examined, and the main techniques, such as smart charging, coordinated scheduling, and energy management, are investigated based on the problem. Additionally, synergy effects on the economy and environment are studied. After discussing the current development issues, a brief overview of the prospects for renewable energy and electric vehicles is provided..

Battery state of charge estimation using temporal convolutional network based on electric vehicles operating data