Battery Source Research Articles

Optimizing hardware configuration for solar powered energy management in battery ultracapacitor hybrid electric vehicles

The design and construction of an adaptive energy management system incorporating a 12 V–2 Ah battery and a 1F ultracapacitor for solar powered hybrid electric vehicles are presented in this paper. The primary storage battery’s longevity and overall system efficiency are intended to be increased by the EMS’s ability to forecast driving circumstances and lessen the load on it. The method optimizes power distribution among numerous energy storage sources by using sophisticated hardware configurations. Using Arduino Uno software, the EMS continuously modifies power distribution in response to driving circumstances and vehicle energy needs. To conduct a thorough assessment of the system, a mechanical configuration was created to replicate the vehicle’s dynamics. This included situations in which the vehicle moved only by using its moment of inertia and not the accelerator. Testing under diverse operating conditions, such as braking, acceleration, and varying slopes is made possible by this configuration. According to experimental data, peak battery stress is significantly reduced by about 33.33%, resulting in longer battery life. With minimal energy losses of 4.3–4.9%, the system regularly achieves power efficiency ranging from 95.1 to 95.7%. Furthermore, the suggested hardware-based EMS lowers overall energy loss by roughly 10.36%, highlighting its dependability and effectiveness in contrast to traditional techniques. The longevity and efficiency of the system are enhanced by the balanced power distribution between the battery, ultracapacitor, and solar sources, which prevents any one source from being overloaded. Overall, the suggested energy management system is successful in improving energy distribution and extending the lifespan and performance of HEV components.

UV LED disinfection as a novel treatment for common salmonid pathogens

Aeromonas salmonicida and Yersinia ruckeri are common pathogenic bacteria that impact salmonid aquaculture. Although vaccinations are available against both organisms, large-scale vaccination efforts can be expensive, cumbersome, and are not always reliable. Alternatively, these pathogens have been effectively inactivated using UV radiation from mercury-based systems. These systems are energy intensive and fragile which currently limits their use to closed and semi-closed production systems. UV light emitting diodes (UV LEDs) have recently emerged as a novel alternative to traditional mercury-based treatment. UV LEDs have durable housing, a relatively low energy draw, can be powered by a battery source and are adaptable to challenging environments. This study examined the effectiveness of three UV LED wavelengths for disinfection of A. salmonicida and Y. ruckeri in pure culture and resuspended in a wastewater matrix. All tested UV LEDs were effective in disinfecting both organisms. 267 and 279 nm wavelengths outperformed 255 nm disinfection in both test matrices. Particulate matter from wastewater reduced the upper limit of treatment for A. salmonicida but results still indicated that all wavelengths were effective for disinfection in a challenging matrix. This study represents the first use of UV LEDs for disinfection of A. salmonicida and Y. ruckeri and provides impact to aquaculture producers looking to implement novel technologies for disease control.

Power Quality Enrichment and Power Management using an Adaptive Control Scheme in Microgrid System

This article proposes an ANFIS-based control scheme for improved power quality and power management that will provide optimal and sustainable energy in a grid-connected renewable energy system. The system consists of a wind energy conversion system (WECS), a photo voltaic system (PVS), and a battery energy storage system (BESS). To increase the PVS's and WECS output powers, a unique control approach is suggested in this paper. The adaptive Neuro-Fuzzy Inference System (ANFIS) controller is incorporated into the proposed controller to generate three-phase reference current signals for harmonic elimination in the system during system dynamic conditions. The proposed control technique can work under voltage quality problems such as voltage sag, voltage swell, neutral currents, and reactive power. Renewable energy sources (PV and Wind) are interfaced to improve the DC-link overall performance by minimizing short-term and long-term voltage problems. The proposed controller regulates the energy flows between the renewable energy sources to the end users with a unity power factor. A supervisory control scheme is implemented for optimal power management in the system. Based on the load requirement, and how the renewable, battery, and grid sources are sharing the power, a detailed analysis is given in the paper. Simulation results from the MATLAB/Simulink platform under several test conditions at the grid side and load side illustrate the efficacy of the proposed control mechanisms in the environment of power optimization and energy management. The comparative analysis is performed to show the efficacy of the proposed system. Finally, the proposed system is validated and the THD content of the grid currents is found good.

A non-isolated converter of switching current stress in DC-DC converter for integrating PV and battery storage system

ABSTRACT High-gain DC-DC converters have seen significant adoption in renewable energy systems like Photovoltaic (PV), Fuel Cells (FC), and other systems. However, their effectiveness is increasingly tied to the availability of renewable resources. As Renewable Energy Sources (RES) typically generate electricity at low voltage levels, integrating high-gain DC-DC converters with RES is crucial for optimal performance. This paper introduces an innovative DC-DC converter design that achieves high gain and is specifically able to integrate smoothly with RES like PV systems and battery storage. The innovation of the proposed converter lies in its integration of conventional boost and buck-boost converters, enabling a high voltage gain while minimizing output voltage ripple and reducing switch current stress. Additionally, its unidirectional power transfer port allows efficient energy flow from solar PV or battery sources to the load, making it highly adaptable for managing fluctuating energy sources, a feature that distinguishes it from current modern converters. Its design simplifies architecture by combining traditional converter types, reducing the number of components, which leads to cost savings, lower weight, and improved reliability. Additionally, the design minimizes output voltage ripple and reduces switching current stress, enhancing the converter’s durability under variable operating conditions. The converter’s performance is evaluated through simulation and experimental studies, demonstrating a 96.3% efficiency and 5.5 times of voltage gain. The converter’s feasibility and effectiveness are validated under dynamic scenarios with varying solar irradiation, making it an ideal solution for clean energy generation systems, including hybrid energy generation setups. The proposed converter topology offers an auspicious solution for high-gain DC-DC conversion in renewable energy systems.

Perspective modelling and measuring discharge voltage on truncated data of long-term stored Li-ion batteries based on functional state space model

The subject of battery sources and electrical energy accumulators is currently very topical. Moreover, the possibilities of reusing already discarded sources are being explored as so-called “second-life batteries”. This article is concerned with studying and modelling the behaviour of a battery in an electric aircraft in operation — the voltage during discharge. Outcomes from extensive experiments on real long-term stored batteries have provided statistically robust sets of data on both long-term stored and new batteries; some of the data, however, are truncated. A modern approach that neglects the truncated issues and is based on functional data analysis and modified with a specific time series is used to model the process. This suggested model is much more accurate than the model used previously as it can effectively process truncated data. It also allows a certain degree of generalization. The aim is to determine the probability density of the time when the battery reaches the critical value, including the numerical statistics, for both stored and new batteries. The results are compared using the specific statistical Kullback–Leibler divergence approach to determine the degree of difference. The proposed model applies to similar issues where battery voltage is modelled in a time domain while the data form is truncated. It is proved, however, that further use of the stored batteries does not disrupt the safe and reliable operation of an electric airplane in terms of their functionality.

A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture

This paper comprehensively explores the development of a standalone and compact microwave sensing system tailored for automated radio frequency (RF) scattered parameter acquisitions. Coupled with an emitting RF device (antenna, resonator, open waveguide), the system could be used for non-invasive monitoring of external matter or latent environmental variables. Central to this design is the integration of a NanoVNA and a Raspberry Pi Zero W platform, allowing easy recording of S-parameters (scattering parameters) in the range of the 50 kHz–4.4 GHz frequency band. Noteworthy features include dual recording modes, manual for on-demand acquisitions and automatic for scheduled data collection, powered seamlessly by a single battery source. Thanks to the flexibility of the system’s architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. As a case study, the potential application of the integrated sensor system with an RF patch antenna is explored in the context of greenwood hydration detection within the field of smart agriculture. This innovative system enables non-invasive monitoring of wood hydration levels by analyzing scattering parameters (S-parameters). These S-parameters are then processed using ML techniques to automate the monitoring process, enabling real-time and predictive analysis of moisture levels.

Design and implementation of a universal converter for microgrid applications using approximate dynamic programming and artificial neural networks

This paper introduces a novel design for a universal DC-DC and DC-AC converter tailored for DC/AC microgrid applications using Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). The proposed converter is engineered to operate efficiently with both low-power battery and single-phase AC supply, utilizing identical side terminals and switches for both chopper and inverter configurations. This innovation reduces component redundancy and enhances operational versatility. The converter's design emphasizes minimal switch usage while ensuring efficient conversion to meet diverse load requirements from battery or AC sources. A conceptual example illustrates the design's principles, and comprehensive analyses compare the converter's performance across various operational modes. A test bench model, rated at 3000W, demonstrates the converter's efficacy in all five operational modes with AC/DC inputs. Experimental results confirm the system's robustness and adaptability, leveraging ADP-ANN for optimal performance. The paper concludes by outlining potential applications, including microgrids, electric vehicles, and renewable energy systems, highlighting the converter's key advantages such as reduced complexity, increased efficiency, and broad applicability.

Optimal Utilization of Battery Sources in Cascaded H-Bridge Inverter by Coordinated Sequence Selection

Optimal Utilization of Battery Sources in Cascaded H-Bridge Inverter by Coordinated Sequence Selection

Dynamic load reference based DQ-axis synchronous frame control method of grid connected PV hybrid microgrid

The hybrid AC-DC microgrid is a system that links AC and DC microgrids using a bidirectional AC-DC interface converter. The presence of dynamic loads is a major obstacle to the implementation of the microgrid concept, and those 50 % include asynchronous motors. This paper presents a control system for a hybrid microgrid that powers an electrical motor using the field-oriented control (FOC) method. This study investigates the significance of third-harmonic injected PWM (THIPWM) in reducing the necessary voltage across the DC bus while still achieving the required AC voltage at motor terminals. MATLAB/Simulink simulation tool and OPAL-RT for real-time simulations were used for validation of the proposed control. The outcomes show the tracking of the reference currents, achieving accurate torque control, managing various operating modes like grid-connected having SPWM and THIPWM, or operation with PV and battery sources. The research also highlights lower settling time values as well as lower total harmonic distortion (THD) compared to SPWM in different load conditions using THIPWM.

Research on topology technology of integrated battery energy storage system with reconfigurable battery and converter

In traditional battery energy storage systems (BESS), batteries are usually connected in a simple series or parallel form, and separate converters and balancing modules are typically used for energy exchange between the battery and external sources, as well as for balancing energy between batteries. This paper proposes an integrated battery energy storage system (IBESS) with reconfigurable batteries and DC/DC converters, resulting in a more compact structure. The IBESS can reconfigure the connection of energy storage batteries based on the battery's status and external demand. The DC/DC converters can operate in different modes such as boost, buck, or buck-boost converters. The IBESS has five working modes: discharging mode, balancing mode, battery discharging and balancing mode, constant current and constant voltage (CCCV) charging mode and backup mode. To verify the feasibility of the proposed energy storage system, a model with four battery modules was built using MATLAB/SIMULINK, and the operation of all working modes was demonstrated. The simulation results verified the system's performance and reliability, demonstrating its flexibility and efficiency in different operating modes.

Power converters analyzed in energy storage systems to enhance the performance of the smart grid application

This paper implements and compares the existing power supply converters for an energy storage system to determine the best suited for the smart grid application. A survey of different DC-DC converters is carried out to analyze the battery's overall performance. The main objective is to identify this application's most appropriate energy storage device. The advantages of this technology have high efficiency and reliability, which can connect various energy sources and reduce conduction losses in the power converters. Through the converter control, reference currents are imposed to charge the battery. The battery nominal voltage needs to change to see which type of converter is the most suitable and robust. Simulation results show that the operating ranges of boost-buck, buck-boost, and buck-boost converters with negative output voltage enhance the efficiency of battery and renewable energy sources and compared the DC converters to know the functional voltage for the energy storage system. The power converter's efficiency and control facility will allow us to link the energy storage system with the power grid. The overall installation is established using MATLAB/Simulink software.

Improvement of Power Factor in Wireless Electric Vehicle Charging Using Inductive Power Transfer Topology

The EV requires energy supplied to its motors to function properly. This required energy either can be transmitted by a physical wired connection or by wireless power transfer firstly to the battery using charging of the EV system and then battery discharges to supply to the vehicle. This paper deals with the wireless transfer of power using the induction method for charging battery of the vehicle using electric energy for its functioning as it is the most suitable and fast mode to charge the batteries of many vehicles altogether at charging station. The challenges recently faced by EV’s includes the movement of energy from the energy supply point to the destination i.e., EV’s without the direct interaction of battery and energy source. Many topologies have been introduced in this regard and several simulation models are built.

Bibliometric Analysis of Microgrid Control Strategy from 2007 to 2022 Based on CiteSpace

Efficient and reliable control strategy is the key to the stable operation of microgrids. Most studies on microgrid control strategy are from single perspectives, lacking integrated views. Thus, we use CiteSpace to process a multidimensional bibliometric analysis to depict the publication statistics, collaboration dynamics, research status, and development trends; we also construct a panorama knowledge framework and show future research features. We discover that microgrid control strategy is a hot domain with global cooperation. Besides, electrical and electronic engineering is the most distinctive discipline, and droop control is one of the most critical control methods. In addition, future studies may focus on engineering application and sustainability; some ongoing hotspots have generated new trends, such as battery, stability analysis, and renewable energy sources. This research is innovative; we provide comprehensive and integrated knowledge details, show the clear theoretical logical structure, and pinpoint future practical frontiers: the research is objective, quantitative, full-scale, multidimensional, and future-oriented, making up for the shortcomings of previous review studies. The results provide valuable theory and practice references for scholars new and interested in this field.

Hybrid Random Forest Regression and Artificial Neural Networks for Modelling and Monitoring the State of Health of Li-Ion Battery

Lithium-ion batteries play a significant role in modern energy storage systems for a number of applications, including renewable energy installations, electric cars, and portable devices. In order to guarantee the long-term dependability and safety of these battery packs, comprehensive modelling and monitoring approaches are required for accurate State of Health (SoH) evaluation. This research suggests an innovative strategy that combines the strengths of Random Forest Regression (RFR) and Artificial Neural Networks (ANN) for reliable Li-ion battery SoH modelling and monitoring. The research starts with the collection of thorough Li-ion battery data, which includes voltage, current, temperature, and cycling characteristics. The battery dataset's nonlinear relationships are captured utilising RFR as the main modelling method. In terms of feature selection and prediction precision, RFR's ensemble of decision trees shines, making it a strong contender for modelling complex battery behaviour. The SoH prediction is then enhanced by the use of ANN, a deep learning framework renowned for its ability to extract detailed patterns from data. The characteristics of RFR are complemented by ANN's capacity to discover hidden features and nonlinear correlations, improving the overall prediction performance. The suggested hybrid approach, that combines RFR and ANN, is trained and verified utilising a diverse database acquired from Li-ion battery sources under operational circumstances. To assess the prediction accuracy of a suggested framework measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values are employed. The outcomes show that the hybrid RFR-ANN model works better than the individual RFR and ANN models, producing better SoH predictions. A viable solution for real-time Li-ion battery health monitoring is provided by this combination of conventional ML and deep learning approaches, which demonstrates the synergy among complexity and interpretability. This research advances battery SoH assessment and paves the way for the practical incorporation of precise monitoring systems into applications like electric vehicles, where timely and accurate SoH data is essential for maximising battery performance and extending operational lifespan.

Control energy management system for photovoltaic with bidirectional converter using deep neural network

Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS.

Multiobjective, trust‐aware, artificial hummingbird algorithm‐based secure clustering and routing with mobile sink for wireless sensor networks

AbstractWireless sensor networks (WSNs) are composed of numerous nodes distributed in geographical regions. Security and energy efficiency are challenging tasks due to an open environment and a restricted battery source. The multiobjective trust‐aware artificial hummingbird algorithm (M‐TAAHA) is proposed to achieve secure and reliable transmission over a WSN with a mobile sink (MS). The M‐TAAHA selects secure cluster head (SCH) nodes based on trust, energy, interspace between sensors, interspace between SCH and MS, and the CH balancing factor. A secure route is found by M‐TAAHA with trust, energy, and interspace between SCH and MS. The M‐TAAHA avoids the malicious nodes to improve data delivery and avoid unwanted energy consumption. The M‐TAAHA is analyzed using energy consumption, alive nodes, life expectancy, delay, data packets received in MS, throughput, packet delivery ratio, and packet loss ratio. Existing techniques (LEACH‐TM, EATMR, FAL, Taylor‐spotted hyena optimization [Taylor‐SHO], TBEBR, and TEDG) are used for comparison with the M‐TAAHA. Findings show that the energy consumption of the proposed M‐TAAHA for 1000 rounds is 0.56 J (1.78 × smaller than that of the Taylor‐SHO).

An MTPZC for the Integration of RESs using Analysis, Modeling, Control, and Implementation

This article presents a novel approach to enhance the efficiency, control, and power management capabilities of traditional Zeta Converters in renewable energy applications. The proposed particle swarm optimization (PSO)-modified three-port zeta converter (MTPZC) features a multiport structure with dedicated input ports for photovoltaic (PV) and battery sources, along with an output port. The integration of PSO optimization aims to optimize maximum power point tracking to enhance energy extraction. Extensive simulations and experimental validations showcase the MTPZC's superior performance in power extraction from both PV and Battery sources. This innovative design positions the converter as a promising solution for multiport energy conversion systems in renewable energy applications. The article contributes to the advancement of power electronics by offering improved reliability and efficiency. Simulation results, validated in MATLAB and compared with existing methods, demonstrate an impressive efficiency of 97.8%. The MTPZC outperforms existing approaches, indicating lower converter losses and highlighting its potential for practical implementation in diverse renewable energy systems.

Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control

This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions.

Power Management Strategy of a PEM Fuel Cell-Battery Powered Electric Vehicle

A MATLAB-Simulink based mathematical vehicle simulation model is developed in this investigation to analyze power management strategy of a proton exchange membrane (PEM) fuel cell and battery powered electric vehicle. The vehicle simulation model incorporated a real-world commercial passenger vehicle operator’s drive cycle inputs and associated vehicle dynamics to determine accurate estimation of total onboard power requirements and withdrawal of power from the fuel cell and battery sources to meet the demand. The power management strategy is designed to meet the vehicle’s onboard power demand based on the availability of hybrid combination of fuel cell and battery power sources. The fuel cell stack is the primary power source of the vehicle. The fuel cell is also charging the battery with excess power produced onboard and hence controls the state-of-charge of the battery. Battery is used as a supplemental power source for meeting the vehicle’s peak power demands. The parameters are optimized to implement the power management strategy by considering the battery state-of-the charge, the drive torque and the vehicle drive performance. The model simulation results with optimized parameters showed that the power requirement of the electric vehicle was significantly affected by the combination of fuel cell and battery power management system as well as the operating behaviors of the end user (driver).

Hybrid Electrical Vehicle Design by Using Solar and Battery Sources

This paper of the modelling methods and software used to model EVs with BLDC motors are provided in this research. It examines the value of simulation in enhancing control, energy, and performance techniques for vehicles. This research also examines important variables, scenarios, and difficulties in simulating BLDC motor driven EVs, emphasising their significant contribution to the development of EV technology. The battery is charged using a power converter and a DC/DC converter. In order to charge the battery, it also used regenerative braking. This model enables a thorough examination of battery performance under various operating circumstances, such as temperature changes and charge and discharge cycles. The battery efficiency of this lithium-ion battery is outstanding. An electric vehicle with a solar power source and a totally battery-powered design. We create a comprehensive and adaptable simulation environment that faithfully replicates real-world EV behaviour by integrating the Simulink and Simscape libraries from MATLAB. It not only advances the technology of electric vehicles but also looks into the integration of solar power to increase the sustainability of electric mobility. This research highlights the potential for electric vehicles to be a key contributor to a cleaner and more sustainable transportation future by fusing the advantages of lithiumion batteries, BLDC motors, and solar power.