Electric Vehicle Applications Research Articles

Enhancing sustainability in electric mobility: Exploring blockchain applications for secure EV charging and energy management

The global shift towards reducing carbon emissions has spurred a growing interest in electric vehicles (EVs) over traditional fossil fuel-based internal combustion engines. Concurrently, blockchain technology has emerged as a transformative solution across various sectors, characterized by its decentralized database and distributed ledger. Its notable attributes, including transparency, immutability, traceability, security, auditability, and authenticity, have led major companies to recognize its potential integration into the EV sector. As a result, there has been a steady increase in case studies and research initiatives exploring blockchain applications in EVs. This article provides a detailed overview of the use of blockchain in the EV domain, focusing on the challenges and applications in four key areas: EV charging infrastructure, energy trading, supply chain management, and energy management. Initially, the basic concepts of blockchain technology are briefly explained, followed by its applications in different fields. The study presents the opportunities and crucial requirements for implementing blockchain technology for secure energy trading, charging, energy management, and supply chain operations. While blockchain can improve security and efficiency in the EV domain, it remains vulnerable to various attacks. This article explores these attacks and other potential challenges in integrating blockchain technology in the EV sector. The main findings of this rEView provide insights into the importance of blockchain in the EV sector and underscore the need to overcome existing challenges for the widespread adoption of this technology in the near future.

Properties of Carbon/Epoxy and Glass/Epoxy Composite Produced From Prepreg as a Structure for Battery Pack in Electric Vehicle Applications

Properties of Carbon/Epoxy and Glass/Epoxy Composite Produced From Prepreg as a Structure for Battery Pack in Electric Vehicle Applications

An improved voltage gain bidirectional DC/DC converter based on voltage multiplier cell for electric vehicle application

ABSTRACT The global energy crisis has raised environmental concerns, leading governments to invest in renewable energy and address electric vehicle (EV) and hybrid EV (HEV) difficulties. One of the main challenges is the interface between the energy storage system and the DC-link of the three-phase inverter. For this aim, an improved voltage gain bidirectional DC/DC converter based on a voltage multiplier cell (VMC) is proposed in this paper, which acts like a high boost converter when the energy storage system is powering the EV and a buck converter when regenerative braking is required. The proposed bidirectional converter has advantages like low device account, simple structure, reduced switching power losses, high voltage gain, common ground, and increased efficiency. An output voltage of 10 times the input voltage in boost mode is provided by the proposed converter. Also, the proposed converter can generate a voltage 0.26 times lower than the input voltage in buck mode. The operating principle and comparative analysis with other existing converters are presented. Moreover, the efficiency calculation is discussed by assuming the non-ideal parameters. The proposed converter has a maximum efficiency of 97.78%. Finally, a 500 W simulation prototype is implemented in PSIM software to confirm the theoretical analysis.

Synthetic ester-based forced flow immersion cooling technique for fast discharging lithium-ion battery packs

Rapid advancements in Li-ion battery technology are being made to meet the growing demand for efficient energy storage solutions in electric vehicles and portable electronics. However, heat generation during rapid charging and discharging remains a significant challenge, as it can lead to overheating, fire, and explosion. To address this issue, battery thermal management systems require improvements in cooling strategies. This study aims to optimize the thermal performance of Li-ion battery packs during fast discharge operation by single-phase synthetic ester oil-based forced flow immersion cooling (FFIC) technique. The study analyzes the thermal performance of the FFIC of a 4S2P LIB pack based on various flow rates, pressure drops, and pump power consumption and compares it with various conventional cooling methods and dielectric fluids. The MSMD-based NTGK battery modeling approach is used to analyze the electrical and thermal characteristics of the battery pack at 3C discharge and environmental temperature of 25 °C. The simulation findings revealed that the synthetic ester oil has a superior cooling effect than mineral oil in the 4S2P Li-ion battery pack. The experimental results showed that the synthetic ester-based FFIC Li-ion battery pack achieves an optimal pack temperature of 31.3 °C and uniform temperature distribution at the flow rate of 5 L/min and pressure drop of 228.44 Pa. Moreover, FFIC reduces temperature rise by 51 % compared with natural air convection and 35 % compared with static flow immersion cooling (SFIC) in the 4S2P lithium-ion battery pack. Also, the practical significance of the SEO-based FFIC immersion cooling technique is assessed across extreme climate zones such as −10 °C and 40 °C and determined its superior performance in Li-ion batteries. Hence, this study proved that the FFIC technique is suitable for Li-ion batteries in electric vehicle applications in terms of optimal performance, reliability, and safety during high-current discharge operations.

Enhanced SOC estimation in Lithium-ion Batteries using GRU Neural Networks with GSA

State of charge (SOC) is one of the most important metrics to evaluate the performance of the battery management system (BMS) in electric vehicles (EVs). Lithium-ion batteries have been utilized often for SOC estimates in EV applications because of its attractive characteristics such as extended lifespans, high voltages, energy, and capacities. However, dynamic charging and discharging profiles, material degradation, battery aging, chemical reaction, and temperature fluctuations would diverge the accuracy of SOC estimation. Deep learning has proven to become an efficient method for SOC estimation in EVs due to its strong computational capabilities, enhanced generalization performance, and excellent accuracy under dynamic loading profiles. Therefore, this study proposes the use of the gated recurrent unit neural network (GRUNN) for estimating SOC in lithium-ion batteries. The best hyperparameters of GRUNN that enable improving SOC accuracy are found using gravitational search optimization (GSA). To evaluate the functional sustainability of the suggested method, a full battery test bench model is created and accordingly, robustness of the proposed approach is verified under diverse EV drive cycles and aging impacts. The effectiveness of the proposed approach is evaluated by comparing with several existing approaches. The results demonstrate that the suggested method archives SOC error and RMSE in EV driving cycles and ageing effects below 5% and 2%, respectively. The excellent outcomes obtained by the proposed method would certainly enhance the battery charging and discharging profile and EV performance. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 1-12

Performance Investigation of 17 Level Reduced Switch Count Multilevel Inverter

The primary objective of this paper is to introduce a 17-level multilevel inverter with only eight power switches and three diodes that can be suitable for electric vehicle applications. This setup utilizes three distinct unequal DC sources to create the 17 levels of output voltage waveforms. The modes of operations of the proposed topology have been discussed in detail. The calculation of conduction losses, switching losses, efficiency, total standing voltage, and cost function per level for the suggested inverter has been elaborated. Due to more semiconductor power switches, diodes, capacitors, driver circuits, and DC sources in typical inverters, switching losses, costs, and harmonic distortion are increased. The nearest-level control technique has been utilized to control the switching elements of the recommended configuration. The performance comparison of various multilevel inverter topologies has also been discussed. The suggested multilevel inverter provides a higher efficiency of 98.60%, cost function of 3.6 for a weight coefficient of 1, improved power quality, and higher reliability. A reduction in the power switches significantly reduces the convolution of switching circuitry. The total harmonic distortion produced by this inverter is 3.49%, which comes under the IEEE standard of 5%.

Development and optimization of hybrid heat dissipation system for lithium-ion battery packs

This study presents the development and optimization of an advanced hybrid heat dissipation system for lithium-ion battery packs designed explicitly for drone applications. The system employs a novel battery capsule filled with a phase change material (PCM) compound enhanced with 2 % Huber nano-carbon, demonstrating superior thermal conductivity and stability. The capsules were manufactured using 3D printing technology, with paraffin wax and carbon black powders as the PCM components. Thermal simulations and experimental results indicated that the oval-shaped capsules with a major/minor axis ratio of 17.2/14.2 mm provided the most effective heat dissipation, maintaining the battery within a safe operating temperature range. Comparative studies revealed that the PCM-filled capsules outperformed the efficiency of air-filled and pure paraffin wax-filled capsules in thermal management. The system’s dual approach, combining passive PCM cooling with active air cooling, significantly extended the battery’s operational life and safety by ensuring optimal temperature regulation. This research highlights the potential for integrating nano-carbon-enhanced PCMs in high-performance, temperature-sensitive devices, paving the way for broader applications in electric vehicles, portable electronics, and advanced energy storage systems.

Tribological coatings for electric vehicle applications

Tribological coatings for electric vehicle applications

Electrotribochemical formation of abrasive nano-carbon particles under electrified conditions on lubricated sliding contacts

Electric vehicle (EV) mobility represents a transformative shift in achieving better energy security, environmental cleanliness, and economic prosperity. Despite recent advancements in EV technology, several challenges persist in tribology and lubrication fronts that can hamper their long-term reliability, performance, and efficiency. In this work, we explored the tribological performance of four commercially available driveline lubricants under non-electrified and electrified sliding conditions using AISI 52100 bearing steel. The results confirmed that passing of electricity through the contact interface exacerbate the wear damage (causing as much as a 5-fold increase in wear volume). Using Raman Spectroscopy, XPS, SEM, ToF-SIMS, and HRTEM, we confirmed that such accelerated wear primarily results from the formation of highly abrasive soot-like amorphous carbon, iron carbide, and other carbonaceous products which result from the decomposition of long-chain hydrocarbon molecules of lubricating oils under electrification. These findings confirm the existence of very complex wear mechanisms in electrified contacts and suggest the need for much improved lubricants and/or materials for future EV applications.

A hybrid methodology based battery power management and torque control of open-end winding induction motor drive for electric vehicles

This paper proposes a hybrid strategy for an isolated battery-powered induction motor drive with two stages fed by an inverter and open-end winding, designed for electric vehicle applications. The hybrid approach, termed EOO-RERNN, combines the Eurasian Oystercatcher Optimizer (EOO) and Recalling Enhanced Neural Network methods. RERNN predicts the control pulses generated by EOO, which are utilized to control the voltage source inverter (VSI) switches. The system comprises dual motors, two battery packs (BPs), and VSIs supplying power to both the rear motor (RM) and front motor (FM). The primary objective of the EOO-RERNN technique is to regulate each DC-link's power share to maintain equilibrium in the electric vehicle's charge. Direct Torque Control (DTC) is employed due to its quick and precise torque response and tolerance for parameter variations. Proper tuning is required to control the state of charge battery level. The EOO-RERNN method is implemented in the Matrix Laboratory (MATLAB) platform, with performance contrasted to other methods such as Wild Horse Optimizer (WHO), Grasshopper Optimization Algorithm (GOA), and Heap-Based Optimizer (HBO). Simulation results indicate a motor speed of 4850 rpm for the EOO-RERNN method.

Review of Switched Reluctance Motor Converters and Torque Ripple Minimisation Techniques for Electric Vehicle Applications

This paper presents a review of the most common power converters and torque ripple minimisation approaches for switched reluctance motors (SRMs). Unlike conventional three-phase AC motors, namely squirrel cage induction motors and permanent magnet synchronous motors, which require a typical three-phase inverter for operation, the switched reluctance motor requires a different topology power converter for reliable and efficient operation. In addition, due to the non-linear, discrete nature of SRM torque production, torque ripple is severely pronounced, which is undesirable in servo applications like electric vehicles. Hence, deploying a proper torque control function for smooth and quiet motor operation is crucial. This paper sheds light over the most popular SRM power converters as well as torque ripple minimisation methods, and it suggests an optimal SRM drive topology for EV applications.

Structural Positive Electrodes Engineered for Multifunctionality.

Multifunctional structural batteries are of high and emerging interest in a wide variety of high-strength and lightweight applications. Structural batteries typically use pristine carbon fiber as the negative electrode, functionalized carbon fiber as the positive electrode, and a mechanically robust lithium-ion transporting electrolyte. However, electrochemical cycling of carbon fibre-based positive electrodes is still limited to tests in liquid electrolytes, which does not allow for to introduction of multifunctionality in real terms. To overcome these limitations, structural batteries with a structural battery electrolyte (SBE) are developed. This approach offers massless energy storage. The electrodes are manufactured using economically friendly, abundant, cheap, and non-toxic iron-based materials like olivine LiFePO4. Reduced graphene oxide, renowned for its high surface area and electrical conductivity, is incorporated to enhance the ion transport mechanism. Furthermore, a vacuum-infused solid-liquid electrolyte is cured to bolster the mechanical strength of the carbon fibers and provide a medium for lithium-ion migration. Electrophoretic deposition is selected as a green process to manufacture the structural positive electrodes with homogeneous mass loading. A specific capacity of 112mAhg-1 can be reached at C/20, allowing the smooth transport of Li-ion in the presence of SBE. The modulus of positive electrodes exceeded 80GPa. Structural battery-positive half-cells are demonstrated across various mass-loadings, enabling them to be tailored for a diverse array of applications in consumer technology, electric vehicles, and aerospace sectors.

Fuzzy Based Solar MPPT for Electric Vehicle Application

Fuzzy Based Solar MPPT for Electric Vehicle Application

Integrated Three-Port Converter for Solar-Charged Electric Vehicle Applications

Integrated Three-Port Converter for Solar-Charged Electric Vehicle Applications

An Implementation of Quasi-Newton Algorithm for Fast-charging Lithium-Ion Battery (LIB) Optimization in Electric Vehicle Application

Lithium-Ion Battery (LIB) is still an effective alternative technology in maximizing the efficiency of electric vehicles (EV). The application of EVs has had a significant impact in order to reduce the issue of global problems - reducing carbon gas emissions. The LIB charging mechanism with the fast-charging method is an alternative to the application of EVs on a more massive scale. However, the dynamics of the battery where the battery work function can decrease over time will affect battery performance. In addition, fast-charging efforts at LIB with maximum speed have the impact of increasing the risk of battery temperature and the existence of a larger gap in battery degradation. This paper proposes the application of Limited-Memory-Broyden-Fletcher-Goldfarb-ShannoBound Constrained (L-BFGS-B) algorithm for Lithium-Ion Battery (LIB) fast-charging optimization as an innovative solution approach in dealing with the complex LIB fastcharging dynamics. The results show that this approach is able to improve fast-charging speed and efficiency.

Cogging Torque Reduction of Brushless DC Motor: Investigating the Efficacy of Ra-dial Pole Shaping Technique with Novel Bump-Shaped Rotor Pole

With the rising demand for high-performance motors in electrical vehicle applications, permanent magnet brushless DC motors are a promising solution due to their various attractive features. However, it has a significant drawback of high cogging torque, which deteriorates the overall motor performance. This negative impact is dominating in electric vehicles for low-speed applications. It is desirable to reduce the cogging torque to improve the performance of the radial flux brushless DC motor. The prime focus of this research is reducing cogging torque through the radial pole shaping technique with a bump-shaped rotor pole surface. This work also investigates the impact of the proposed approach on torque ripple and motor performance while cutting down the requirement for rare earth material. This paper uses two ratings: 1000 W, 510 rpm, and 250 W, 150 rpm motors. Two reference motors of proposed ratings were designed using radial-shaped permanent magnet poles. Finite element software is used for the simulation and modeling of the motors. A novel bump-shaped permanent magnet pole shape is introduced, and in-depth investigations have been carried out to evaluate the impact of the proposed pole shape on cogging torque. The validity of the analysis results is further substantiated by comparing the improved and reference model results. The comparison investigation indicates that the motor equipped with the proposed pole shape performs better than the reference motor.

ADRC-LSE Control of a Dual-Star Induction Motor Powered by a Dual 3-level NPC Inverter

In the context of electric vehicle propulsion drives, this paper presents anActive disturbances Rejection Control (ADRC) strategyapplied to a Dual Star Induction Motor (DSIM)powered by dual 3 levels inverter type NPC. The proposed method focuses on enhancing the Active Disturbance Rejection Control (ADRC) method by integrating Least Squares Estimation, establishing a direct relationship between the control law and the necessary frequency for the DSIM to turn in the exact speed whatever the load torque applied. The ADRC with Least Squares Estimation is compared with Field-Oriented Control (FOC) to evaluate their performance in tracking reference speed and stabilizing time. Additionally, another test is conducted to assess the ADRC's ability to reject disturbances. Results from the simulation demonstrate the effectiveness of ADRC with Least Squares Estimation, showcasing its potential for optimizing DSIM control in electric vehicle applications, thereby contributing to enhanced efficiency and stability.

Parametric Correlation Analysis between Equivalent Electric Circuit Model and Mechanistic Model Interpretation for Battery Internal Aging

In modern electric vehicle applications, understanding the evolution of the internal electrochemical reaction throughout the aging of batteries is as relevant as knowing their state of health. This article demonstrates the feasibility of correlating a mechanistic model of the battery internal electrochemical reactions with an equivalent electrical circuit (EEC) model, providing a practical and understandable interpretation of the internal reactions for electrical specialists. By way of electrochemical impedance spectroscopy analysis and automatic control theory, a methodology for correlating the resistance and capacitance variations of the EEC model and how they reflect the electrochemical reaction changes is proposed. These changes are represented through the time constants of the three RC parallel arrays from an EEC model. PS-260 lead–acid batteries were analyzed throughout the SOC and their useful life to validate this methodology. The result analysis allows us to establish that the first RC array corresponds to the negative electrode reactions in the range of 1.48 Hz to 10 kHz, the second RC array to the positive electrode reactions and generation of sulfates in the range of 0.5 to 1.48 Hz, and the third RC array to the generation of sulfates and their diffusion in the range of 0.01 to 0.5 Hz.

Battery pack passive insulation strategies of electric vehicles under a frigid environment

Further application of electric vehicles (EVs) in frigid regions is hindered by the diminished performance of lithium-ion batteries (LIBs) at sub-zero temperatures. Developing efficient heat preservation strategies has significant implications for the broad application of EVs and LIBs. This study focuses on passive heat preservation strategies (PHPS) for battery packs in frigid environments (−30 °C). A validated 3D battery pack heat preservation model integrated with airflow is constructed in this paper, and the model analysis results indicate that arranging aerogels at the pack level provides superior heat preservation compared to cell and module levels. Additionally, the influence of flow field distribution (FFD) schemes on heat preservation performance is discovered. Both experimental and simulation results confirm that the proposed scheme can optimize the cooling rate to about 2.3 °C/h. Commencing from an initial temperature of 30 °C, extending the battery pack retention time above 0 °C to over 12 h under −30 °C can significantly expand the application of electric vehicles in frigid regions. This paper explores the primary factors affecting the heat dissipation of battery packs in the frigid temperature environment, which have great significance in guiding the heat preservation design of battery systems.

Blockchain Abstract Architecture for Energy Collaboration for Agriculture in Indonesia based on Bibliometric Approach

The direction of development for the application of electric vehicles is starting to be seen with great certainty, this situation is driven by the vision of sustainable development goals (SDGs) where one of them is energy that is minimal from carbon. The agricultural sector is a field of activity that also requires machinery or equipment to support its activities. and currently most of them still use conventional machines that produce quite high carbon, meanwhile, when agricultural machinery is converted to electricity, the challenge is to provide affordable energy at competitive prices and reliable technology. Blockchain is a disruptive technology that has many advantages, especially in terms of flexibility and security to accommodate broad interactions from many parties in an activity chain. The purpose of this research is to develop a blockchain-based architecture to support collaborative energy supply in agriculture that is sourced from renewable energy. we use Scopus indexed data sources to formulate and describe the directions and references of the proposed architecture