Utilization Of Electric Vehicles Research Articles

Condition monitoring of electric vehicle motor testing machine’s Vital components using bagged trees and quadratic SVM: a comparative study

The future of the automotive industry lies in the utilization of electric vehicles (EVs). A crucial component of these EVs is the drive motor. The verification of critical parameters for the electrical motor is considered to be of utmost importance, and this is achieved through the use of a motor testing machine. The motor testing machine is utilized frequently to determine characteristic curve points and the electromagnetic behavior of electric motors under various conditions. However, it has been observed that the presence of a fault in the helical gear setup of the testing machine leads to frequent breakdowns and suboptimal performance, thereby affecting the efficiency of the assembly line in the production of electric vehicles. In light of these observations, the main objective of this research endeavor is to improve the motor test bench apparatus by altering essential design parameters through the utilization of vibration signal analysis and machine learning techniques. Prior to extracting statistical features, vibration signals are obtained at different gear settings. These signals are subsequently categorized using classifiers such as Bagged Trees and Quadratic SVM. Machine learning techniques are utilized to classify the gathered signals as either normal or faulty, both with and without the inclusion of a 0.25 KW load for each condition. The performance of multiple algorithms is evaluated and scrutinized. The results have demonstrated that the Bagged Trees technique outperforms the other algorithm, achieving an impressive accuracy of 95.3%. Consequently, the proposed method presents an adept solution for augmenting the motor test bench’s capability by modifying crucial design parameters.

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

To enable the regulation and utilization of electric vehicle (EV) load resources by the power grid in the electricity market environment, a third-party electric vehicle aggregator (EVA) must be introduced. The strategy of EVA participation in the electricity market must be studied. During operation, the EVA faces a double uncertainty in the market, namely, electricity demand and electricity price, and must optimize its market behavior to protect its own interests. To achieve this goal, we propose a robust pricing strategy for the EVA that takes into account the coordination of two-stage market behavior to enhance operational efficiency and risk resistance. A two-stage robust pricing strategy that takes into account uncertainty was established by first considering day-ahead pricing, day-ahead electricity purchases, real-time electricity management, and EV customer demand response for the EVA, and further considering the uncertainty in electricity demand and electricity prices. The two-stage robust pricing model was transformed into a two-stage mixed integer programming by linearization method and solved iteratively by the columns and constraints generation (CCG) algorithm. Simulation verification was carried out, and the results show that the proposed strategy fully considers the influence of price uncertainty factors, effectively avoids market risks, and improves the adaptability and economy of the EVA’s business strategy.

PV-based Performance Evaluation of Zeta and Sepic Topologies for EV Applications

In this paper, a comparative performance analysis was proposed for PV fed EV charging system for ZETA and SEPIC DC-DC converter topologies. The utilization of Electric vehicles has gained significance in recent years due to societal awareness and increasing concern for environmental sustainability. EV charging time plays a vital role in modern-day vehicle commuting with energy-efficient performance. The selection of an appropriate DC-DC converter topology plays a significant role for fast charging with improved efficiency. The paper presents an optimized parameter selection, design, simulation methodology & relative performance analysis between the ZETA and SEPIC converter topologies with and without MPPT algorithm for EV charging applications. The MPPT algorithm based on P&O and IC techniques investigates for switching time of the ZETA and SEPIC converters under standard test conditions of solar PV panel i.e., at irradiance of 1000 W/m2 and temperature of 25oC. In this article MATLAB/Simulink was developed to explore the performance of both topologies with and without MPPT approaches for battery SoC, battery voltage and charging current. The study shows that the battery charged from an SoC of 50% to 50.035% for SEPIC converter while the ZETA converter charges from 50% to 50.025% with a similar simulation time of ten seconds with Solar power input. The results demonstrate the DC-DC SEPIC converter is the best choice for EV charging applications through the PV system.

Smart home energy management using demand response with uncertainty analysis of electric vehicle in the presence of renewable energy sources

In this article, smart home energy management is proposed using real-time pricing (RTP) based demand response for effective utilization of renewable-based distributed generation (DGs), battery, and electric vehicle (EV) to reduce grid dependency. The smart home consists of eighteen smart appliances and EV. The EV can be operated either in vehicle-to-home or home-to-vehicle. The smart appliances in the smart home are categorized into two types: electrical and thermal loads. Each smart appliance’s operating time slots and durations are scheduled based on the tariff, availability of DGs, and storage devices using the Binary Particle Swarm Optimization (BPSO) algorithm without affecting user preferences. Due to the proposed method, the grid dependency of the smart home in each time slot is reduced, which shall reduce the net electricity cost. Different cases are analyzed based on the various combinations of renewable-based DGs such as wind power, solar photovoltaic (PV), and battery in the smart home to showcase the effectiveness of the proposed method. The performance of the proposed system is analyzed under uncertain EV and DG operations. A detailed comparison is made with and without appliance scheduling in the proposed smart home to showcase the necessity of appliance scheduling and reduce the grid dependency. The simulation results show the profit of ₹13.046 and the grid dependency reduced by 9.636 kW in case 5 with appliance scheduling compared to case 5 without appliance scheduling. Therefore, the electricity cost and grid dependency are reduced using the BPSO algorithm for appliance scheduling in the smart home under normal and uncertain conditions.

The Importance of Increasing Public Adoption of Electric Vehicles in Reducing Jakarta’s Air Pollution

In response to growing environmental concerns surrounding renewable energy and the adverse effects of extreme weather patterns on air quality, the Indonesian government has initiated efforts to promote the use of electric vehicles (EVs) as a substitute for conventional automobiles inside the country. Therefore, it is crucial to implement strategies aimed at promoting widespread acceptance and utilization of electric vehicles (EVs) by the general population. The objective of this study was to assess the impact of product innovation and price on the public’s adoption of electric vehicles in Jakarta. This study employed a quantitative descriptive methodology, using an associative approach and utilizing SPSS. The data collection methods involve the use of a questionnaire, with the target population being individuals residing in Jakarta who own automobiles. The findings of the investigation are presented as follows: The findings indicate a strong and statistically significant relationship between product innovation and the level of community adoption of electric vehicles in Jakarta. The hypothesis regarding the relationship between the price variable and the community adoption of electric automobiles in Jakarta has been substantiated. The present study examines the impact of both product innovation and price on the public adoption of electric cars in Jakarta.

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle’s performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system’s power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply’s potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles.

The Propensity to Adopt Electric Vehicles in the United Arab Emirates: An Analysis of Economic and Geographic Factors

In 2022, the increased utilization of electric vehicles (EVs) curtailed global carbon dioxide emissions by 13 gigatons. While EV ownership has been on the rise in the United Arab Emirates (UAE), accounting for approximately 1.3% of passenger car vehicles in 2022, it has not yet attained a level of economic feasibility compared to petroleum vehicles. The authors examined consumers’ opinions in the UAE toward adopting EVs for light-duty transport from the economic and geographic perspectives. The main research question was the following: to what extent do economic and geographic factors affect consumers’ opinions toward adopting EVs in the UAE? The objectives were to determine if a relationship exists between economic factors, such as saving money on petroleum, the cost of car maintenance, the cost of purchase, and income level, and geographic factors, including 14 cities across the seven Emirates that affect consumers’ opinions toward adopting EVs. We designed a survey that was distributed to a sample of 5459 respondents to examine this relationship. Descriptive and inferential statistics as well as PyData analytical techniques complemented by the application of data visualization tools such as Seaborn, Plotly, and Matplotlib were employed to examine the dataset. The findings demonstrated that respondents across all income levels have a positive outlook regarding the appeal of EVs for saving money on petroleum, while their interest in saving money on car maintenance and the initial cost of purchase became more pronounced in the higher income levels. Moreover, there were variations in preferences in highly populated Emirates (e.g., Dubai and Abu Dhabi) compared to Emirates with lower populations (e.g., Ras Al Khaimah and Umm al Quwain), confirming that much more emphasis is required to promote EVs in rural areas. When comparing multiple income groups to determine the impact of different factors, the ANOVA confirmed the prevailing patterns evident in the associations.

Carbon-Aware Operation of Resilient Vertical Farms in Active Distribution Networks

Emerging utilization of Electric Vehicles (EVs) in Distribution Networks (DNs) decreases the power quality in the DN. The main quest of this research is to find out Can EVs and Photovoltaic (PV) systems support the DN and mitigate the voltage magnitude violations due to utilizing high penetration level EV? This article proposes a novel volt/VAR optimization (VVO) model by leveraging the reactive power support of EVs and PVs as Distributed Energy Resources (DERs) for the DN. The proposed VVO model contains the second-order conic relaxed form of the AC power flow equations. Thus, the presented VVO problem is a Mixed-Integer Second-Order Cone Programming (MISOCP) problem. To obtain the ability to support the DN, Smart Inverters (SIs) are utilized to connect EVs and PV systems to the DN. Thus, to add the PV and EV systems to the convexified form of the VVO problem, the P-Q characteristics and the model of SIs working at different operating points are presented based on experimental results. The proposed model of SIs allows the participation of DERs in the reactive power compensation to mitigate voltage issues of DN due to utilizing high penetration level EVs. Besides, the proposed model of SIs enables DERs to increase their dispatchability. The performance of the proposed model is examined in the case studies using the IEEE 33-bus system and the 123-bus system. It is illustrated in the case studies that the solar dispatchability increases by 12% when the proposed volt/VAR model is utilized for IEEE 33-bus system. Besides, it is illustrated in the case studies that the voltage profile of buses remains in the desired operating range when the proposed model is leveraged, even if the penetration level of EVs increased to 40%.

A Charging Station Planning Study to Prepare for the Utilization of Electric Vehicles on University Campuses: The Case of Ondokuz Mayıs University

Electric vehicles (EVs) have gained significant interest as a cleaner and sustainable mode of transportation compared to fossil-fuel vehicles. Although its background goes beyond a century ago, the effects of global warming have increased the development and utilization of EVs. Similarly, serious investments are being made for the development and use of EVs for batteries and charging stations, and, R&D activities are being carried out in Türkiye. With the start of domestic e-Car utilization, it is expected that e-Cars will be used throughout Türkiye. Therefore, charging stations will be established to meet needs. For this scope, a design study has been conducted at Ondokuz Mayıs University Campus and the number of stations was determined to ensure the effective use of e-Cars at the campus. A field study was conducted on the current parking lots, their capacities, and average occupancy rates. Then, charging station recommendations were developed for each parking lot based on the distribution of EVs by correlating the parking lot capacities, occupancy rates, and distribution. With similar planning to be carried out on other university campuses, universities will be at the forefront of e-Car utilization and many studies can be conducted to solve the problems that will be identified.

Impact of downstream emissions cap-and-trade policy on electric vehicle and clean utility adoption

In this research, we incorporate emissions cap-and-trade policy into a decision making model for the adoption of electric vehicles (EVs). We consider a fleet environment that includes a taxicab company (TC, a downstream tailpipe emitter) and an infrastructure-service provider (SP, an upstream energy user) that provides fast charging services. Both SP and TC must have allowances to account for their own emissions. We examine several scenarios including (i) an equilibrium case, where the selling and buying prices of carbon are equal, (ii) a “buy-low-sell-high” case with financial derivatives, and (iii) a “transient” case based on market conditions. We combine both analytical and computational approaches to assess the impact of various emission cap values and carbon pricing on TC’s adoption of EVs, SP’s utility provider selection, total emissions, and the profitability of both entities. For instance, in both buy-low-sell-high and transient scenarios, penalizing upstream utility emissions more than tailpipe emissions is preferred if electric vehicles are likely to be adopted; otherwise, tighter tailpipe emissions restrictions should be preferred. In the equilibrium case, we show that policymakers will require additional mandates or market mechanisms to influence TC and SP choices, as both entities prefer dirty technologies. Additionally, we demonstrate that providing intermediate utility options – less expensive, but dirtier or more expensive, but cleaner – did not result in reduced emissions. This indicates that expanding utility options may not be a necessary or high-priority policy.

Mitigation of the Impact of Incorporating Charging Stations for Electric Vehicles Using Solar-based Renewable DG on the Electrical Distribution System

Background:: The utilization of electric vehicles (EVs) is on the rise, which has led to significant challenges for the radial distribution system (RDS). This research aimed to investigate the impact of electric vehicle charging stations (EVCS) on power loss, voltage stability, and reliability within the RDS. Solar-based renewable distributed generation (SRDG) offers a range of benefits in mitigating the impact of EVCS on the distribution system. To simulate the integration of EV charging loads and assess their effects on the RDS, the study has employed the widely used IEEE 69-bus system as a test platform. Methods:: A novel approach utilizing the cuckoo search algorithm (CSA) is presented in this article to determine the optimal locations for EVCS and SRDG. Results:: The study has examined the potential power losses resulting from additional EV loads and explored strategies to optimize charging rates and minimize resistive losses. Voltage stability has been assessed by analyzing the voltage drop caused by high charging loads, and the efficiency of voltage regulation devices and control strategies has been evaluated. Moreover, the paper has discussed the reliability implications of EV charging, including localized overloads and the possibility of outages. Conclusion:: Overall, this research has enhanced our understanding of the challenges associated with integrating EVCS into the RDS and offered potential solutions to address these challenges.

Suppressing thermal runaway propagation of nickel-rich Lithium-ion battery modules using silica aerogel sheets

Suppressing thermal runaway (TR) propagation within Lithium-ion battery (LIB) modules/packs/systems is one of the key factors to ensure the safety utilization of electric vehicles and energy storage systems. This contribution aims at introducing Silica Aerogel Sheets (SAS) into high energy density modules to suppress the TR propagation. The SAS, synthesized via a sol-gel process followed by supercritical fluid drying, possess almost an ideal thermal insulator characteristic due to the low thermal conductivity of 0.020 W/(m·K) at room temperature. The SAS are inserted between two adjacent pouch cells (LiNi0.86Co0.07Mn0.07O2/graphite, 320 Wh.kg−1) to assemble high energy density modules. A single pouch cell, subjected to the TR test with an Accelerating Rate Calorimetry, releases a huge amount of heat energy (1083 kJ/kg) with the maximum temperature beyond 800 °C. In the open area test site for TR propagation experiments, it can be observed that violent jet and flame burst from the cell, which is designed to undergo TR first and trigger the TR propagation of the modules. The results show that only one single layer of SAS cannot stop the TR propagation or fire spreading. However, more layers of SAS (three in the experiments) can successfully suppress the TR propagation and block fire progression. The temperature of the adjacent cell only reaches 105.4 °C. Therefore, the SAS with acceptable thickness can suppress the TR propagation successfully to improve the safety of the LIB modules.

Optimal Fractional-Order Controller for the Voltage Stability of a DC Microgrid Feeding an Electric Vehicle Charging Station

Charging stations are regarded as the cornerstone of electric vehicle (EV) development and utilization. Electric vehicle charging stations (EVCSs) are now energized via standalone microgrids that utilize renewable energy sources and reduce the stress on the utility grid. However, the control and energy management of EVCSs are challenging tasks because they are nonlinear and time-varying. This study suggests a fractional-order proportional integral (FOPI) controller to improve the performance and energy management of a standalone EVCS microgrid. The microgrid is supplied mainly by photovoltaic (PV) energy and utilizes a battery as an energy storage system (ESS). The FOPI’s settings are best created utilizing the grey wolf optimization (GWO) method to attain the highest performance possible. The grey wolf is run for 100 iterations using 20 wolves. In addition, after 80 iterations for the specified goal function, the GWO algorithm almost discovers the ideal values. For changes in solar insolation, the performance of the proposed FOPI controller is compared with that of a traditional PI controller. The Matlab/Simulink platform models and simulates the EVCS’s microgrid. The results demonstrate that the suggested FOPI controller significantly improves the transient responsiveness of the EVCS performance compared to the standard PI controller. Despite all PV insolation disruptions, the EV battery continues to charge while the ESS battery precisely stores and balances PV energy changes. The results support the suggested FOPI control’s robustness to parameter mismatches. The microgrid’s efficiency fluctuations with the insolation level and state of charge of the EV battery are discussed.

Optimal planning and operation of grid-connected PV/CHP/battery energy system considering demand response and electric vehicles for a multi-residential complex building

Ongoing technological innovations and communication infrastructure have rapidly increased the active participation of energy customers and utilization of renewable energy sources and electric vehicles (EVs) as climate change solutions. As such, the demand response program (DRP) has been developed as an effective tool for better interaction mechanisms and balancing supply and demand. This paper proposes a model for optimal planning and operation of an integrated PV/CHP/battery/gas boiler hybrid grid-connected energy system with the purpose of minimizing lifecycle cost. The model considers EV charging stations (private and shared), incentive-based DRP, and net energy metering mechanism. Different alternatives for system configuration are optimized and analyzed in terms of cost, emission, and resiliency factors. A new representative case study of a multi-residential complex building with electrical and thermal loads is depicted to validate the research methodology. The results show that the full utilization of the building roof with maximum PV capacity and adoption of the DRP initiatives for both summer and winter seasons reduces the electricity import from the grid and enhanced the use of the on-site CHP generator and battery packs. The optimal system with 73.9 kW PV, 105 kW CHP, and 53 kW inverter is of superior performance with reduction percentages of 9 %, 10 %, 45 % and 16 % in the system's net present cost, energy cost, annual utility bill, and CO2 emissions, respectively, compared to the base scenario of CHP/gas boiler/grid. Also, the proposed system also gets significant DRP revenue of 10,060$/yr and zero unmet demands and a negligible number of EVs missed charging sessions despite the grid outages and intermittency of solar resources. Overall, this work can provide an understanding and a way forward to those working on iHES development for complex buildings and a valuable benchmark model that is transferable and applicable with relative ease for other regions.

DEVELOPMENT OF ELECTRIC TRANSPORT INFRASTRUCTURE IN KAZAKHSTAN

Electric transport has become possible thanks to modern technologies and innovations in the field of energy. Today, the world is actively switching to the use of environmentally friendly transport in order to reduce the harmful impact on the environment. Leading scientists in this field have done a great job to ensure the smooth operation of electric transport. The main problem of electric transport is the limited charging of batteries and the lack of infrastructure of charging stations. Leading scientists are working on this issue and finding new solutions to ensure safe and fast charging. The main purpose of the study is to study the problems and prospects of the development of electric transport infrastructure in Kazakhstan. The study summarizes statistical indicators of the development of electric transport infrastructure in Kazakhstan, obtained from official sources of the Bureau of National Statistics of the Republic of Kazakhstan. The analysis made it possible to identify the primary tasks, the solution of which will simplify the spread of electric transport: creating a favorable economic environment for the development of the electric transport market, the introduction of electric buses in cities, stimulating business to create enterprises for the utilization of traction batteries and electric vehicles, supporting and developing the production of electric vehicles in the country, training and training of specialists in the field of electric transport, development of electric transport management plans and monitoring its use.

A Fault Diagnosis Method for Power Battery Based on Multiple Model Fusion

The widespread adoption and utilization of electric vehicles has been constrained by power battery performance. We proposed a fault diagnosis method for power batteries based on multiple-model fusion. The method effectively fused the advantages of various classification models and avoided the bias of a single model towards certain fault types. Firstly, we collected and sorted parameter information of the power battery during operation. Three common neural networks: back propagation (BP) neural network, convolution neural network (CNN), and long short-term memory (LSTM) neural network, were applied to battery fault diagnosis to output the fault types. Secondly, the fusion algorithm proposed in this paper determined the accurate fault type. Based on the improved voting method, the proposed fusion algorithm, named the multi-level decision algorithm, calculated the voting factors of the diagnostic results of each classification model. According to the set decision thresholds, multi-level decision voting was conducted to avoid neglecting effective classification information from minority models, which can occur with traditional voting methods. Finally, the accuracy and effectiveness of the proposed method were verified by comparing the accuracy of each classification model with the multiple model fusion algorithm.

Enhancing Lithium-Ion Battery Manufacturing Efficiency: A Comparative Analysis Using DEA Malmquist and Epsilon-Based Measures

Innovative carbon reduction and sustainability solutions are needed to combat climate change. One promising approach towards cleaner air involves the utilization of lithium-ion batteries (LIB) and electric power vehicles, showcasing their potential as innovative tools for cleaner air. However, we must focus on the entire battery life cycle, starting with production. By prioritizing the efficiency and sustainability of lithium-ion battery manufacturing, we can take an essential step toward mitigating climate change and creating a healthier planet for future generations. A comprehensive case study of the leading LIB manufacturers demonstrates the usefulness of the suggested hybrid methodology. Initially, we utilized the Malmquist model to evaluate these firms’ total efficiency while dissecting their development into technical and technological efficiency change components. We employed the Epsilon-Based Measure (EBM) model to determine each organization’s efficiency and inefficiency scores. The findings show that the EBM approach successfully bridged the gap in the LIB industry landscape. Combined with the Malmquist model, the resulting framework offers a powerful and equitable evaluation paradigm that is easily applicable to any domain. Furthermore, it accurately identifies the top-performing organizations in specific aspects across the research period of 2018–2021. The EBM model demonstrates that most organizations have attained their top level, except for A10, which has superior technology adoption but poor management. A1, A2, A4, A6, A8, A9, and A10 were unable to meet their targets because of the COVID-19 pandemic, despite productivity improvements. A12 leads the three highest-scoring enterprises in efficiency and total productivity changes, while A3 and A5 should focus on innovative production techniques and improved management. The managerial implications provide vital direction for green energy practitioners, enhancing their operational effectiveness. Concurrently, consumers can identify the best LIB manufacturers, allowing them to invest in long-term green energy solutions confidently.

Evaluating the role of innovation in hybrid electric vehicle-related technologies to promote environmental sustainability in knowledge-based economies

Knowledge-based economies (KBEs) are the most technologically-advanced industrialized economies in the world. Despite impressive renewable energy consumption (RNEC), economic development, and high gross domestic product per capita (GDPPC) records, weather patterns in these economies have worsened, causing serious concerns among the government and masses. Recognizing innovation in hybrid electrical vehicle-related technologies (IHEVRTs) as an essential tool for combating surging environmental damage, the KBEs have dedicated enormous financial resources towards novel ecological technologies associated with hybrid electric vehicles over the past two decades. The existing literature reflects a lack of theoretical and empirical information on whether IHEVRTs reduce carbon dioxide emissions (CO2e) in these economies. This work empirically explores the influence of expansionary monetary policy (EPMP), IHEVRTs, trade openness (TRDOPS), natural resources (NRs), contractionary monetary policy (CNMP), RNEC, GDPPC, contractionary commercial policy (CNCP), and CO2e in the KBEs for the period between 1990 and 2020. The findings signified that IHEVRTs, RNEC, CNMP, and CNCP reduced CO2e levels, while EPMP, NRs, TRDOPS, EPCP, and GDPPC raised CO2e levels. Ultimately, we suggest that KBEs should promote the utilization of hybrid electric vehicles, enhance green R&D spending, employ clean technology in natural resource extraction, and enact green monetary and commercial policies.

Solar Off-Grid Electric Vehicle Charging Station

Abstract: Nowadays, there is a great development in electric vehicle production and utilization. It has no pollution, high efficiency, low noise, and low maintenance. However, the charging stations, required to charge the electric vehicle batteries, impose high energy demand on the utility grid. One way to overcome the stress on the grid is the utilization of renewable energy sources such as photovoltaic energy. The utilization of standalone charging stations represents good support to the utility grid. Nevertheless, the electrical design of these systems has different techniques and is sometimes complex. This paper introduces a new simple analysis and design of a standalone charging station powered by photovoltaic energy. Simple closed-form design equations are derived, for all the system components. Case-study design calculations are presented for the proposed charging station. Other important features of an off-grid solar EV charging station might include the use of smart charging technology to optimize charging speed and efficiency, as well as the ability to monitor and control the system remotely. Safety features such as overcharge protection and grounding would also be important considerations. We can visualize the expected results using a Cayenne Software, we can also control the street lighting by Internet of Things. The results show that the charging process of the electric vehicle battery is precisely steady for all the PV insolation disturbances. In addition, the charging/discharging of the energy storage battery responds perfectly to store and compensate for PV energy variations.

A novel method of discharge capacity prediction based on simplified electrochemical model-aging mechanism for lithium-ion batteries

Obtaining the State of Health of lithium-ion batteries and mastering its degradation laws are crucial for the utilization of Electric Vehicles. However, the prediction of discharge capacity of lithium-ion batteries requires high accuracy, which is subject to the variation of cells and the uncertainty of operating conditions. In this work, a discharge capacity prognostics method for lithium-ion batteries is developed based on a simplified electrochemical coupled aging mechanism model. Firstly, the solid-phase diffusion process is analyzed by using a simplified electrochemical model, and the particle rupture stress at different C rates is obtained. Then, based on the aging mechanisms in terms of Solid Electrolyte Interphase (SEI) layer growth model and particle volume expansion model, the SEI growth rate and correlated aging kinetics parameters are optimized by using particle swarm optimization algorithm. Finally, combined with the further analysis of aging mechanisms and variation of model parameters at early, middle, and late stage of degradation, the developed discharge capacity prediction method is verified at separate stages for batteries at 1C, 2C and 3C respectively, with the average relative error of full life cycle no more than 4 %.