Electric Vehicle Energy Research Articles

The Environmental Impacts of Future Global Sales of Hydrogen Fuel Cell Vehicles

During the last decade, developing more sustainable transportation modes has become a primary objective for car manufacturers and governments around the world to mitigate environmental issues, such as climate change, the continuous increase in greenhouse gas (GHG) emissions, and energy depletion. The use of hydrogen fuel cell technology as a source of energy in electric vehicles is considered an emerging and promising technology that could contribute significantly to addressing these environmental issues. In this study, the effects of Hydrogen Fuel Cell Battery Electric Vehicles (HFCBEVs) on global GHG emissions compared to other technologies, such as BEVs, were determined based on different relevant factors, such as predicted sales for 2050 (the result of the developed prediction model), estimated daily traveling distance, estimated future average global electricity emission factors, future average Battery Electric Vehicle (BEV) emission factors, future global hydrogen production emission factors, and future average HFCBEV emission factors. As a result, the annual GHG emissions produced by passenger cars that are expected to be sold in 2050 were determined by considering BEV sales in the first scenario and HFCBEV replacement in the second scenario. The results indicate that the environmental benefits of HFCBEVs are expected to increase over time compared to those of BEVs, due to the eco-friendly methods that are expected to be used in hydrogen production in the future. For instance, in 2021, HFCBEVs could produce more GHG emissions than BEVs by 54.9% per km of travel, whereas in 2050, BEVs could produce more GHG emissions than HFCBEVs by 225% per km of travel.

Multi-objective Optimization of Power Networks Integrating Electric Vehicles and Wind Energy

Multi-objective Optimization of Power Networks Integrating Electric Vehicles and Wind Energy

Investigating the Impact of Discretization Techniques on Real-Time Digital Control of DC-DC Boost Converters: A Comprehensive Analysis

This paper provides a thorough comparative analysis of discretization techniques commonly utilized in digital control applications for DC-DC boost converters (DBCs). Given the crucial role of DC-DC converters in various industries such as renewable energy systems, electric vehicles, and portable electronic devices, it is imperative to optimize their performance through efficient digital control. This study aims to improve the understanding of the effects of discretization methods on control system behaviour. It investigates their impact on the performance, stability and accuracy of control algorithms in the context of DBC, using dSPACE real-time interface (RTI) and hardware-in-the-loop (HIL) simulation for validation. This approach replicates real-world conditions, allowing performance to be evaluated in a controlled environment. The analysis examines behaviour in the time and frequency domains, providing insight into the strengths and weaknesses of each method. Experimental validation using MATLAB/Simulink/Stateflow on dSPACE RTI 1007 processor, DS2004 high-speed A/D and CP4002 timing and digital I/O boards ensures that the comparative analysis provides practical benefits for DBC digital control applications.

Energy Harvesting Technologies in Electric Vehicles and Applications in Sustainable Agricultural Transportation: A Review

The increasing challenges of energy depletion, environmental pollution, climate change, and agricultural transportation costs demand innovative solutions. Electric vehicles (EVs) offer a promising solution towards mitigating these issues through renewable energy sources. However, limited driving range remains a crucial barrier to their widespread adoption, particularly in environmentally friendly agricultural transportation. This review paper comprehensively analyzes energy harvesting technologies in electric vehicles and their application in agricultural transportation. The focus is on their potential to improve driving range and address the specific needs of farmers in terms of maximizing their income. Very few review articles have been published on maximum number of possibilities of harvesting energies in electric vehicles, application in agricultural transportation and future prospects for improvement. Existing and current literature was systematically reviewed on various energy harvesting techniques applicable to EVs, including solar energy harvesting, regenerative braking, aerodynamic (wind) energy recovery, and vibration energy harvesting through suspension systems which are all renewable energy sources. Their advantages, working principle, limitations, and recent advancements were critically discussed. All explored energy harvesting methods show the ability to extend EV driving range. However, solar energy and wind or aerodynamic energy harvesting in EVs are theoretically possible but still under research for practical applications due to lots of limitations. Regenerative braking and vibration energy harvesting show the most promising current applications. The Electrical or electromagnetic vibration energy harvesters produce better results over mechanical (hydraulic and pneumatic harvesters). When optimization is employed, better results were achieved. Research on the application of artificial intelligence based computer algorithms to optimize and produce the best amount of harvested energy in EVs will play a significant contribution in the adoption of EVs for sustainable agricultural transportation.

Overview of Fuel Cell-Hybrid Power Sources Vehicle Technology: A Review

Today, the world suffers from excessive and unfair consumption of non-renewable energy sources, such as high rate of global pollution and global warming. Accordingly, fields of life in general and industry in particular, led by the automobile industry, tended to use clean and renewable energy in industry and consumption to reduce the negative impacts on the global environment. The automotive industry tended to produce electric cars that do not depend at all on traditional energy sources from fossil fuel derivatives. Accordingly, it was necessary to find alternative energy sources that achieve both goals: avoiding the negative impact on the envi-ronment, and producing sufficient energy to achieve the requirements of performance and efficiency from the use of electric cars to be a permanent alternative to traditional cars that run on fossil fuels. This scientific paper provides an overview of one of the versions of modern technology in the field of electric vehicles energy performance to provide the vehicle with energy continuously and the mechanism of control and management in this system. This paper studies the hybrid power sources technology in electric and hybrid cars that depend on a main power source, Fuel Cells (FC), and a secondary power sources, Battery and Ultracapacitor, in the vehicle. This paper presents a brief overview of this system, its components, their characteristics, the advantages of hybridization in this type of energy source with the working mechanism of the system, an overview of the control systems in this technology and a set of challenges facing this type technology and its future perspectives.

Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides.

Unraveling the effect of electricity price on electric vehicle charging behavior: A case study in Shenzhen, China

Estimating price elasticity of demand for electric vehicle charging contributes to the accurate determination of charging price, thereby improving electric vehicle adoption and energy sustainability. However, few studies have studied the impact of electricity price on electric vehicle charging behavior, especially the demand spillover effect caused by price fluctuations. To fill the gaps, on a citywide dataset of public charging piles in Shenzhen, China, first, correlation coefficients and hypothesis tests are used to determine the relationship between charging demand and price. A learning model incorporating two-layer graph attention, temporal pattern attention, and knowledge-embedded meta-learning is developed for accurate spatio-temporal regression. Impulse response analysis is conducted to unravel several noteworthy phenomena: (1) public charging demand is inelastic to electricity price, with an average elasticity of −0.76, and distinction between different functional areas and times is revealed; (2) negative price impulses marginally change the elasticity, while positive ones make electric vehicle charging users more price sensitive, and (3) the spillover effects caused by price increases and decreases bring 89.48% and 53.88% of its local demand changes to neighbors, respectively, with a scope of 3.45 kilometer. These findings provide policy implications for promoting electric vehicle charging to facilitate renewable energy transition.

Design and Performance Evaluation of Multilevel Inverter for Solar Energy Systems and Electric Vehicle Charging with Multi Output Active Clamp Forward Converter

Design and Performance Evaluation of Multilevel Inverter for Solar Energy Systems and Electric Vehicle Charging with Multi Output Active Clamp Forward Converter

Developmental Trajectories of Electric Vehicle Research in a Circular Economy: Main Path Analysis

This study explored the development history and future trends of academic research on electric vehicles (EVs) in a circular economy. We collected 4127 articles on circular economy and EVs from the Web of Science database, and main path analysis indicated that academic research in the field of EVs in a circular economy has covered the following topics in chronological order: EVs as a power resource; vehicle-to-grid (V2G) technology; renewable energy and energy storage grids; smart grid and charging station optimization; and sustainable development of energy, water, and environmental systems. Through cluster analysis and data mining, we identified the following main research topics in the aforementioned field: recycling and reuse of EV batteries, charging stations and energy management, V2G systems and renewable energy, power frequency control systems, dynamic economic emissions, and energy management. Finally, data mining and statistical analysis revealed the following emerging research topics in this field from 2020 to 2023: microgrids, deep learning, loop supply chain, blockchain, and automatic generation control. Various achievements have been attained in research on EVs in a circular economy; however, challenges related to aspects such as sustainable battery recycling charging infrastructure and renewable energy integration remain.

Optimizing Energy and Delay in Task Offloading for Connected Vehicles with Proximal Policy Optimization

Electric-powered intelligent connected vehicles are becoming the pivotal point of the automotive industry. As vehicles integrate more applications, the computation tasks they generate increase substantially. Cloud servers cannot handle these tasks promptly, and current electric vehicles (EVs) have limited energy and computing resources. Multi-Access edge computing (MEC) performs various activities in proximity to the vehicles, resulting in decreased latency and the preservation of EV battery power. However, MEC servers have finite processing resources and may be unable to satisfy the required latency restrictions. We propose a task offloading scheme to optimize the allocation of computational resources from roadside servers across several EVs. We develop a mathematical model to optimize both computation latency and EV energy, represented as a Markov decision process (MDP). To address this, we employ the deep reinforcement learning-proximal policy optimization (DRL-PPO) algorithm. The implementation of our mathematical model, which is based on an MDP, together with the use of the DRL-PPO algorithm, showcases notable decreases in both energy consumption and latency when compared to alternative benchmark deep reinforcement learning (DRL) approaches.

Energy management optimization of hybrid electric vehicles based on deep learning model predictive control

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers’ attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems.

A study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation

This paper is a study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation. It aims to develop a prediction model for the energy efficiency of electric vehicles and compare it with the results of chassis dynamometer tests. Driving tests were conducted on a chassis dynamometer, which included various test cycles of the 5-cycle test. Using MATLAB Simulink, a vehicle dynamics analysis model for electric vehicles was constructed, and analytical research was conducted focusing on light-duty electric vehicles. By comparing and analyzing the experimental data obtained from the chassis dynamometer with the data calculated through simulation, it is possible to verify the accuracy of the electric vehicle energy efficiency prediction model. And the reliability of the simulation model was secured by comparing the experimental data obtained through chassis dynamometer test with the simulation results of the developed electric vehicle energy efficiency prediction model. There is good agreement between the experimental and simulation results. In most areas, the error rate remains below 3 %, indicating that the simulation model closely follows the experimental results from the chassis dynamometer. Based on these results, it is expected that the developed simulation model can be utilized for measuring the energy efficiency of electric vehicles.

Design, development and multi-scenario application of portable multifunctional test master based on μM-PMU: The case study of Lingang new City Grid, Shanghai

With the large-scale access of new energy generation and electric vehicle charging piles to the grids, power generation and loads are characterized by random fluctuations and intermittency, which affect the stable operation of the distribution network. μM-PMU is capable of realizing the dynamic process monitoring of the grids. In order to solve the function and performance testing problems of multiple application scenarios of μM-PMU, this thesis develops a portable master station based on synchronous phasor measurement system to realize zero drift, voltage amplitude measurement accuracy, voltage phase angle accuracy, current amplitude measurement accuracy, current phase angle measurement accuracy, frequency measurement accuracy, voltage amplitude change with frequency accuracy, and voltage phase angle change with frequency accuracy, Accuracy of current amplitude with frequency change, accuracy of current phase angle with frequency change, active power accuracy, reactive power accuracy, amplitude step response, phase angle step response, frequency step response, a total of 15 test items. It can automatically calculate test results and generate test reports according to different equipment models, reducing the mistakes and calculation errors that may be caused by manually recording test data and improving the accuracy of test results and calculation precision.

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

New Energy Electric Vehicle Industry Development Trends Forecast

This study focuses on developing the MLPRegressor model for predicting the future trends in China's new energy electric vehicle industry over the next decade. We employed a dataset comprising 16,500 records, encompassing 12 features, such as the number of public electric vehicles, the length of electric vehicle operational routes, and the number of electric vehicles owned by residents, for model training. We compared our model with traditional models like ARIMA, XGBRegressor, and Random Forest Regressor, using MAPE and MSLE as evaluation metrics. The final results revealed that our model exhibited superior performance in terms of loss, outperforming the other models significantly. This research not only provides businesses with more accurate strategies for developing the new energy electric vehicle industry but also serves as a crucial reference for the government in formulating environmentally friendly policies.

Reviewing Demand Response for Energy Management with Consideration of Renewable Energy Sources and Electric Vehicles

This review paper critically examines the role of demand response (DR) in energy management, considering the increasing integration of renewable energy sources (RESs) and the rise in electric vehicle (EV) adoption. As the energy landscape shifts toward sustainability, recognizing the synergies and challenges offered by RESs and EVs becomes critical. The study begins by explaining the notion of demand response, emphasizing its importance in optimizing energy usage and grid stability. It then investigates the specific characteristics and possible benefits of incorporating RESs and EVs into DR schemes. This assessment evaluates the effectiveness of DR techniques in leveraging the variability of renewable energy generation and managing the charging patterns of electric vehicles. Furthermore, it outlines important technological, regulatory, and behavioral impediments to DR’s mainstream adoption alongside RESs and EVs. By synthesizing current research findings, this paper provides insights into opportunities for enhancing energy efficiency, lowering greenhouse gas emissions, and advancing sustainable energy systems through the coordinated implementation of demand response, renewable energy sources, and electric vehicles.

Single source self balanced switched capacitor based singe phase nine level inverter

Multilevel inverters are becoming increasingly significant due to their ability to efficiently convert power in various applications such as renewable energy systems, electric vehicles (EVs), and industrial drives. The growing need for efficient power conversion in these areas has driven heightened interest in multilevel inverters. High inrush current is required for numerous applications, including variable frequency drives, single-phase or three-phase machine drives, locomotives, electric vehicles. However, managing this inrush current is crucial to prevent damage to the power source and other connected equipment, as well as to ensure smooth and reliable operation. The solution for this is to provide isolation between load and source. The proposed single phase switched capacitor multilevel inverter possess the feature of virtual isolation between the load and the source. This topology consists of a switched capacitor cell network and a multilevel inverter. In this work, a simplest architecture is used for the MLI section to reduce the number of switches. The MLI section consists of only eight switches. Although the switched capacitor network in this topology consists of a higher number of switches, the switches in the network carry only very low current, resulting in negligible losses. Additionally, the switched capacitor network is highly modular in nature. The proposed single-phase inverter can be converted to a three-phase multilevel inverter (MLI) by connecting identical units in each phase, with the reference sinewave signal for the pulse-width modulation technique derived from the corresponding phases. Consequently, this proposed topology is suitable for both low-power and high-power applications. This work demonstrates that self-voltage balancing of the level capacitors can be achieved without the need for auxiliary components.In this design, the level capacitors act as the source to the load and are charged through switched capacitors. The voltage THD in the suggested work is less than 5%, which is in accordance with IEEE 519 standards. High voltage gain, better load, and line regulation, are other significant advantages of this novel topology. Another feature of this work is simplicity in design and can be easily extended to higher voltage levels. Comprehensive simulation and experimental results are provided to validate the precise performance of the proposed multilevel inverter

Uncharted depths: Navigating the energy security potential of deep-sea mining

In the context of a global shift towards low-carbon energy systems, this paper provides an in-depth analysis of deep-sea mining's (DSM) potential role in enhancing global energy security. Addressing the growing demand for critical minerals essential for clean energy technologies, electric vehicles (EVs), and energy storage systems, the paper examines how DSM can diversify the global mineral supply and reduce reliance on geopolitically sensitive sources. It explores DSM's capacity to recalibrate energy prices, influence the competitive landscape of clean energy technologies, and shift geopolitical dynamics. The paper delves into the multi-faceted impacts of DSM on energy security, including geopolitical shifts, supply chain diversification, and environmental trade-offs. By providing a holistic view that links mineral supply security to sustainable energy transitions, this study extends beyond prior research focused mainly on the technical and environmental aspects of DSM. The findings illustrate DSM's intersection with international politics, its effect on energy pricing strategies, and the balance between resource exploitation and environmental stewardship. Strategic policy recommendations are offered to optimize DSM's benefits while minimizing its ecological impacts, aligning the emerging DSM industry with global sustainability goals. In addition to identifying challenges, the paper proposes actionable solutions, contributing a unique perspective to the discourse on DSM and energy security.

Research on Sales of New Energy Vehicles Based on ARIMA Model

Firstly, this article identifies seven indicators based on electric vehicles: battery endurance, number of public charging facilities, government subsidies, gasoline prices, electricity prices, carbon emissions, and number of power battery companies. Spearman correlation analysis is conducted between indicator data and sales of new energy electric vehicles. The correlation coefficients of the first five indicators are all greater than 0.6, which is the main influencing factor. Then, the sales of new energy vehicles are predicted using a yearly forecasting method. Due to the close correlation between the sales of new energy vehicles and the five main indicators, the ARIMA time series model is first used to predict the annual quantity of the five indicators. Then, using these five indicators as inputs and sales as outputs, a BP neural network model is established to predict the annual sales of new energy vehicles in China to be 46051748 units in ten years. Finally, quantifying the score of ecological environment assessment using four evaluation indicators: sulfur dioxide, smoke emissions, ammonia nitrogen, and carbon dioxide. The entropy weight method was used to determine the weights of each indicator, and the results were 37.25%, 30.96%, 21.24%, and 10.55%, respectively. Then apply it to the TOPSIS model to obtain ecological environment scores for different years, and analyze the correlation between the degree of electrification and ecological environment scores. Finally, it can be concluded that the larger the market share of new energy vehicles, the better the ecological environment.

Overcoming Obstacles of Tesla: Strategies for Problem-Solving

This study aims to explore the key issues currently faced by Tesla Inc. and its potential solutions, further proposing recommendations that have significant impacts on business operations and sustainable development. As a leading electric vehicle and clean energy company, Tesla's innovative models and challenges reflect the major trends in today's automotive industry and new energy sector. It also plays a pivotal role in advancing electric vehicles and renewable energy solutions as the global demand for reducing carbon emissions and improving energy are continuously growing. However, the company faces a series of challenges in production process, market competition and the safety concerns. By thoroughly analyzing these problems and challenges this study aims to provide solutions to help it overcome existing obstacles. By using a comprehensive research methodology to examining Tesla's practices and challenges in various aspects, this paper could identify core issues and provide strategic recommendations to help it mitigate the problems.