Search

Environmental sustainability and the effective use of renewable resources are critical subjects that need to be integrated into our educational systems through practical, hands-on learning. This paper focuses on the fabrication of aluminium-air batteries using waste materials, providing an innovative and cost-effective DIY approach to energy storage education. Utilizing aluminium from food packaging and graphite from pencils, this study demonstrates the construction and functionality of an aluminium-air battery to run an LED, highlighting its potential applications in educational settings and low-cost energy solutions. The paper aims to inspire the adoption of environmentally friendly practices and enhance students' understanding of electrochemical energy conversion through accessible and engaging DIY projects. [ABSTRACT FROM AUTHOR]

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid approach for bidirectional smart charging of electric vehicles (EVs) powered by brushless DC (BLDC) motors. The proposed technique, named Atomic Orbital Search-Rat Swarm Optimizer (AOS-RSO), leverages the Atomic Orbital Search (AOS) for optimizing energy storage management and the Rat Swarm Optimizer (RSO) for enhancing motor control. The modular hybrid energy storage system (HESS) is designed with five modules, each consisting of a bidirectional DC-DC converter connecting a battery and super capacitor module. A multi-level cascaded converter (MCC) connects the HESS to the BLDC motor inverter of the EV drive, controlling the DC bus voltage. The AOS-RSO approach efficiently manages the state-of-charge (SOC) across the modules, achieving a balanced SOC of 98%. Implemented in MATLAB, the method is evaluated in both open and closed-loop systems and compared with existing techniques. Results show the proposed method achieves 96% efficiency in simulation and 94% in experiments, with a CPU time of 2.84 s. This approach significantly advances smart charging solutions for EVs, offering improved energy management and operational efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a method to validate state of charge (SOC) estimations in batteries for their use in remotely manned aerial vehicles (UAVs). The SOC estimation must provide the mission control with a measure of the available range of the aircraft, which is critical for extended missions such as search and rescue operations. However, the uncertainty about the initial state and depth of discharge during the mission makes the estimation challenging. In order to assess the estimation provided to mission control, an a posteriori re-estimation is performed. This allows for the assessment of estimation methods. A reverse-time Kalman estimator is proposed for this task. Accurate SOC estimations are crucial for optimizing the utilization of multiple UAVs in a collaborative manner, ensuring the efficient use of energy resources and maximizing mission success rates. Experimental results for LiFePO4 batteries are provided, showing the capabilities of the proposal for the assessment of online SOC estimators. [ABSTRACT FROM AUTHOR]

This paper presents a novel green combined heat and power generation system for industrial applications. It composes of a hydrogen-fueled combined cycle power plant and a battery energy storage. Firstly, the hydrogen-air rich mixture enters the combustion chamber of the gas turbine cycle for generating electricity, while recovering the heating energy from the exhausted flue gases for driving a steam turbine cycle. The heating energy extracted from the condenser heat exchanger is also used to supply the heating demand of the end-user. Moreover, a comprehensive thermodynamic model and analysis of H 2 combustion process is provided to estimate the adiabatic flame temperature based on enthalpies of reactants and products. The optimum operating point of the cogeneration system is found by solving a mixed-integer nonlinear problem using MATLAB and GAMS over a 24-h study horizon aiming to minimize its daily operation cost considering all technical constraints of system components and load-generation balance criterion. It is found that the energy efficiency of the hydrogen-fueled combined cycle changes from 46 to 58 % while generating 11.331–12.982 MW electricity and 4.436–4.984 MW heat. Two cases are studied without and with participation of battery in energy procurement strategy. It is demonstrated that the objective function of the optimization problem improves from 18 $ cost in case 1 to 29 $ revenue in case 2. • Thermodynamic model and analysis of a H 2 -fueled CHP plant is presented. • Adiabatic flame temperature is obtained based on enthalpies of reactants and products. • A high-temperature heat exchanger is used to produce steam from waste heat of gas turbine flue gases. • Optimal charge and discharge of battery is scheduled for higher cost savings. [ABSTRACT FROM AUTHOR]

Growing awareness of greenhouse gas emissions and exhaustion of fossil fuels leads to the adoption of electric vehicles (EVs). However, the major limitations with respect to EV technology are the driving range and charging time. Also, the proliferation of EVs overloads the power grid and paves the way of incorporating renewable energy sources and energy storage devices. The use of multiple sources necessitates the deployment of compact, low-cost, and high-power electronic converters. This paper proposes a novel configuration of a Multi-Source Non-Isolated Quasi-Resonant Converter (MSNQRC) to overcome the limitations in the charging infrastructure. Due to the presence of a quasi-resonant network, the proposed MSNQRC with a single switch can achieve high voltage gain even at low-duty cycles, provide continuous current and low voltage stress. In addition, this paper elaborates on the parametric design of the converter along with PV modeling based on load ratings and the reliability research of MSNQRC by analysing component failures. A prototype model of 300W is built to verify the efficacy of the designed converter model. The results thus obtained validate the designed model and prove that the proposed system can be used for off-board battery charging systems. [ABSTRACT FROM AUTHOR]

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery's capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy. [ABSTRACT FROM AUTHOR]

Batteries play a crucial role as energy storage devices across various industries. However, achieving high performance often comes at the cost of safety. Continuous monitoring is essential to ensure the safety and reliability of batteries. This paper investigates the advancements in battery monitoring technology, focusing on fiber Bragg gratings (FBGs). By examining the factors contributing to battery degradation and the principles of FBGs, this study discusses key aspects of FBG sensing, including mounting locations, monitoring targets, and their correlation with optical signals. While current FBG battery sensing can achieve high measurement accuracies for temperature (0.1 °C), strain (0.1 μ ε ), pressure (0.14 bar), and refractive index (6 × 10−5 RIU), with corresponding sensitivities of 40 pm/°C, 2.2 pm/ μ ε , −0.3 pm/bar, and −18 nm/RIU, respectively, accurately assessing battery health in real time remains a challenge. Traditional methods struggle to provide real-time and precise evaluations by analyzing the microstructure of battery materials or physical phenomena during chemical reactions. Therefore, by summarizing the current state of FBG battery sensing research, it is evident that monitoring battery material properties (e.g., refractive index and gas properties) through FBGs offers a promising solution for real-time and accurate battery health assessment. This paper also delves into the obstacles of battery monitoring, such as standardizing the FBG encapsulation process, decoupling multiple parameters, and controlling costs. Ultimately, the paper highlights the potential of FBG monitoring technology in driving advancements in battery development. [ABSTRACT FROM AUTHOR]

This paper proposes a transformer‐less high step‐down converter (TLHSC) with battery integration. The converter functions as a versatile three‐port converter, with an integration of battery, and offers a wide range of step‐down conversions. This work presents the operational modes along with the steady‐state analysis, design, and modeling of the controller for the TLHSC converter. The controller validates for the diverse load variations. The proposed converter for 500 W is validated on a laboratory test bed with a voltage conversion from 270 to 28 V for a 25 kHz switching frequency. The proposed TLHC demonstrates the remarkable capabilities of a three‐port converter with a high step‐down voltage ratio and reduced semiconductor stress compared to the existing converters. Various performance attributes like loss and efficiency determinations, switch stress analysis, and sensitivity have been investigated and reported. An empirical results presented and the comparative analysis presented for the proposed circuit with the recent converters in the literature entrust the superiority of the proposed converter, offering advantages such as high step‐down capability, minimal components, lower stress, reduced losses, and a simple circuit design for achieving a wide range of step‐down conversions. [ABSTRACT FROM AUTHOR]

In this work, we consider a problem from the field of power-aware scheduling in which a fleet of electric vehicles have to be charged in a minimum time. Each vehicle is equipped with a lithium-ion battery of a given capacity. The initial power used for charging each battery is known, whereas it is assumed that the power drops to zero at the moment when the battery gets fully loaded. The power usage function is linear and decreasing. The charging jobs are nonpreemptable and independent, whereas the total available amount of power is limited. The objective is to minimize the schedule length. In this paper, we analyze the case of a problem with identical jobs that already cover a wide variety of practical situations. By employing inverses of natural numbers, similar to harmonic series, we prove two properties of this case, and we also discuss the phenomenon of the stabilization of the difference between the start times of two successive jobs in a schedule. We also take under examination a few special cases of the problem. Some conclusions and directions for future research are given. [ABSTRACT FROM AUTHOR]

Microgrid systems have emerged as a favourable solution for addressing the challenges associated with traditional centralized power grids, such as limited resilience, vulnerability to outages, and environmental concerns. As a consequence, this paper presents a hybrid renewable energy source (HRES)-based microgrid, incorporating photovoltaic (PV) system and wind to achieve sustainable and reliable power generation. A novel modified Z-source Zeta converter is incorporated in the system to enhance PV voltage together with hybridized grey wolf optimized sea lion algorithm-assisted PI controller for maintaining DC-link voltage. The wind system utilizes a doubly-fed induction generator (DFIG) for efficient power generation. To ensure effective functioning of DFIG, a proportional-integral (PI)-based droop control strategy is adopted, which regulates the power transfer from wind turbine to grid. In this proposed system, a bidirectional converter with a battery is utilized for energy storage and management. The battery can be charged and discharged in accordance with the needs of the system thanks to the bidirectional converter. Excess power generated by HRES systems is stored in the battery for later use during periods of low energy generation or high demand. Additionally, a dump load is included in the system to dissipate surplus power when battery is fully charged. MATLAB software is utilized validate the effectiveness and feasibility of proposed system, demonstrating improved power quality, grid integration, and energy management. [ABSTRACT FROM AUTHOR]

Today, hydrogen fuel cells (HFCs) have become very popular in various applications because of their ability to be a clean energy source. One of the difficulties associated with HFCs is their sluggish response to variations in the load. This paper presents a nested control strategy based on a type-2 neuro-fuzzy controller (T2NFC) to improve the HFC's dynamic response in a hybrid stand-alone power network using HFC and battery. A system model is constructed using Matlab-Simulink. An interleaved converter is used to draw the maximal power from the HFC and reduce the ripple in the HFC's current. Two T2NFCs control DC voltage and battery charge/discharge current. The robustness of the T2NFC is evaluated for input disturbance, output disturbance, and both disturbances. The results show that the proposed control strategy is robust against input and output disturbances. Also, it provides improved dynamic response of the HFC, lower ripple in HFC current, and less overshoot or undershoot in DC voltage both in transient and steady-state. For step reference input, the proposed controller improves settling time of 24.06 % and overshoot of 59.55 % compared to the conventional PI controller. The results verify the effectiveness of the proposed controller under different operating conditions of hybrid stand-alone power network with fuel cell and battery. • The response of HFC is enhanced, leading to faster and more accurate power generation. • The hydrogen fuel cell's current is experienced a significant reduction in ripples. • The hydrogen fuel cell is now being operated at its maximum power point. • A Type-2 Neuro-Fuzzy Controller is developed to provide robust control of the hydrogen fuel cell's output voltage. [ABSTRACT FROM AUTHOR]

With the growing demand for new energy batteries and the increasing need for battery thermal management, the heat dissipation performance requirements of water coolers are also becoming more stringent. This research paper concentrates on the study of high energy efficiency of the water chiller system. Based on two aspects of structure and algorithm, refined calculation and selection are carried out.It introduces a linkage control logic based on three adjustable main components (water pump, fan, and compressor). The findings demonstrate that the water chiller with a refined design offers a safer and more stable water cooling system with reduced power consumption. [ABSTRACT FROM AUTHOR]

This paper deals with energy management for the photovoltaic-battery-ultra-capacitor energy system for a standalone and grid-connected. The developed energy system can operate in an isolated or connected mode according to the required power. Indeed, the photovoltaic generator is coupled with the DC bus voltage via a DC–DC power converter, to provide the maximum power from the photovoltaic panels. An energy storage system presented by batteries and ultracapacitors is employed to regulate the DC bus voltage through both DC–DC bidirectional power converters. The DC bus is connected to the AC bus through a three-phase power inverter to control the operating modes and ensure the energy demands. In effect, an energy management algorithm is developed to control the energy flow in the system for a standalone and grid-connected. Hence, the output voltage DC bus is controlled by the energy storage system using a Lyapunov approach to ensure high performance under load and irradiance profile variations. In this context, simulations have been carried out to prove the effectiveness of the proposed photovoltaic system. The developed energy system has been tested in standalone and grid-connected modes with the following scenarios: PV generator supplies the load, PV generator power is limited due to less solar lighting, the electric grid supplies the high load power, the surplus energy of the PV generator is injected into the electric grid, and standalone mode operation. [ABSTRACT FROM AUTHOR]

Using multiple input power sources increases the reliability of electric vehicles compared to a single source. However, the inclusion of other sources exhibits complexity in the controller system, such as computing time, program difficulty, and switching speed to connect or disconnect the input power to load. To ensure optimal performance and avoid overloading issues, the EV system needs sophisticated control. This work introduces a machine-learning-based controller using an artificial neural network to solve these problems. This paper describes the detailed power management control methodology using multiple sources like solar PV, fuel cells, and batteries. Novel control with an instantaneous reference current scheme is used to manage the input power sources to satisfy the power demand of electric vehicles. The proposed work executes the power split-up operation with standard and actual drive cycles and maximum power point tracking for PV panels using MATLAB Simulink. Finally, power management with a machine learning technique is implemented in an experimental analysis with the LabVIEW software, and an FPGA controller is used to control a 48 V, 1 kW permanent-magnet synchronous machine. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are becoming very popular nowadays to reduce the consumption of oil and environmental pollution. The use of battery and super-capacitor is increasing as the power-source of EVs. The bi-directional DC-DC converter is essential to integrate the battery and super-capacitor with the motor of the vehicle. However, a conventional second-order bi-directional DC-DC converter cannot give very high/low output voltage due to its efficiency reduction at extreme duty cycles, which is prerequisite for EVs. The problems of achieving high efficiency in wide-input-wide-output (WIWO) voltage range bidirectional operation using a conventional bidirectional converter are summarized in this paper. Additionally, the available converters from the literature for WIWO voltage range operation are discussed in detail. The various wide voltage range DC-DC converters are classified into different categories based on their operating principle. Additionally, the advantage and disadvantages of these various wide voltage range converter topologies are explained for the EV application. Finally, a general conceptual solution to achieve a WIWO converter with high efficiency is proposed. It is essential to change the voltage level of the motor input as per the speed requirement. Hence, this paper presents a clear idea for the WIWO bi-directional converters for EV application. [ABSTRACT FROM AUTHOR]

Batteries are essential to mobilization and electrification as they are used in a wide range of applications, from electric vehicles to small mobile devices. All these devices are powered with AC or DC inside their systems, so they require different battery systems depending on their technical requirements. Batteries show unique characteristics depending on their types, and their needs vary based on their performance, ambient conditions, and so forth. One of the main demands for them is thermal stability. For batteries, thermal stability is not just about safety; it's also about economics, the environment, performance, and system stability. This paper has evaluated over 200 papers and harvested their data to build a collective understanding of battery thermal management systems (BTMSs). These studies are specifically designed to solve different problems. This paper has been prepared to show what these systems are, how they work, what they have been designed for, and under what conditions they should be applied. The BTMSs have been evaluated based on their method, method tools, discharge rate, maximum temperature, temperature difference values, and ambient and inlet temperatures. After evaluating over 200 studies, the results indicate that the passive BTMSs are not useful the cases where the temperature reaches higher values suddenly, especially for system systems that require higher discharge rates. On the other hand, active cooling methods do not manage the temperature difference in the battery cells. However, hybrid cooling methods address both cases admirably by compensating for both of their weaknesses and bringing out their advantages. The general optimum temperature for lithium battery batteries is 55°C. Even though there are many other parameters that need to be considered before making a decision for a BTMS design, the best performance for an optimum system seems to be methods 34, 38, and 22 as they are able to provide lower maximum temperature and temperature difference in the cells. [ABSTRACT FROM AUTHOR]

This paper reports the development of a 10Ah lithium-ion pouch battery cell using stainless-steel laminated film as the casing material for JAXA's SLIM lunar lander. By selecting the pouch format, the mass ratio of the case and electrode terminals is significantly reduced, which is superior to conventional space batteries using prismatic metal cases. A high specific energy of approximately 130Wh/kg was achieved. In addition, unlike standard batteries using aluminumlaminated film, the battery developed during this study deforms little when subjected to a vacuum. It can thus be mounted on a spacecraft with a simple, lightweight bracket, contributing to higher specific energy as a module. This paper reports the results of environment tests for the SLIM mission and the basic electrical characteristics of the battery. [ABSTRACT FROM AUTHOR]

Electric powered vehicle popularity gives us better option for reducing the hydrocarbon emissions for the clean environment and will help to minimizing the troposphere of ozone degradation. Evs that are powered by batteries are becoming more commonplace worldwide. A strong Battery Management System (BMS) is essential for the reliable and safe operation of batteries, which have been widely used in many large power applications, including EVs and hybrid EVs. Their optimal plan and the management are significant for safe and beneficial tasks. This review paper examined with about the oldest kind of battery-latest powered battery (Pb-acid battery) and the most recent innovation of battery, lithium-ion battery. The present paper deals about the recent improvement in three important areas like Battery pack shape, different battery material and thermal behaviour which covered main challenges for EVs operation. [ABSTRACT FROM AUTHOR]

• Physics-Constrained Bayesian Neural Network. • Failure prognostics of lithium-ion batteries. • Comparative study of physics-based, data-driven, and physics-constrained models. In order to predict the remaining useful life (RUL) of lithium-ion batteries, a capacity degradation model may be developed using either simplified physical laws or machine learning-based methods. It is observed that even though degradation models based on simplified physical laws are easy to implement, they may result in large error in the application of failure prognostics. While data-driven prognostics models can provide more accurate degradation forecasting, they may require a large volume of training data and may invoke predictions inconsistent with physical laws. It is also very challenging for existing methods to predict the RUL at the early stages of battery life. In this paper, we propose a Bayesian physics-constrained neural network for battery RUL prediction by overcoming limitations of the current methods. In the proposed method, a neural differential operator is learned from the first 100 cycles of data. The neural differential operator is modeled with a Bayesian neural network architecture that separates the fixed history dependence from the time dependence to isolate epistemic uncertainty quantification. Using the battery dataset presented in the paper by Severson et al. as an example, we compare our proposed method with a simplified physics-based degradation forecasting model and two data-driven prognostics models. The results show that the proposed physics-constrained neural network can provide more accurate RUL estimation than the other methods with the same group of training data. Most importantly, the proposed method allows for RUL prediction at earlier stages of the battery life cycle. [ABSTRACT FROM AUTHOR]

The design and development of a non-isolated buck-based three-port dc–dc converter for medium-power standalone PV systems are presented in this paper. The topology is obtained from a parallel connection of conventional buck and boost converters. An attractive feature of this converter is the implementation of a buck topology at the PV side, which ensures cost-effective PV panel wiring, reduced converter development cost, and better efficiency of the PV system during low irradiation periods when compared to its boost counterpart. The proposed converter includes three operating modes based on load power requirements and PV power generation. An autonomous mode transition algorithm is proposed for seamless transitions between various operating modes. The design methodology, losses, and operation of the converter are discussed in this paper. A low-cost and efficient 2 kW laboratory prototype model of the proposed converter has been developed. Experimental validations are provided to showcase the effectiveness and viability of the proposed converter under various operating conditions. [ABSTRACT FROM AUTHOR]

In this paper, certain issues envisaged in the framework of development of design methodology for intelligent autonomous distributed hybrid power complexes (ADHPC) with green hydrogen and energy storage, functioning in grid mode and in the mode of interaction with the global (national) grid (GN) have been solved. Depending on their energy deficit or surplus, relative to the global grid, ADHPCs can operate as a load or as an energy source, respectively, as follows: based on the analysis of the energy balance management process in the ADHPC, a reasonable choice of the structure of the system of accumulation and distribution of power flows (SADCF) was made from the point of view of increasing the reliability of its functioning and ensuring the physical feasibility of the energy balance management process in this structure, i.e. keeping the actual power consumption of the consumers close to the required rated power at each given time t. This is achieved by including a condenser connected to the SADCF system on its assembly and distribution bus and a storage system BS with double-level ((BS1, BS2), double-circuit ((BS1(1), BS1(2)), (BS2(1), BS2(2))) structure, whereby: BS1of the level 1 - to manage the capacity balance in the SADCF under normal ADHPC regime and the variation of green hydrogen and consumption capacities within their confidence intervals assessed at the design stage; BS2 of the level 2 - to coordinate ADHPC and GN modes of operation and to control, together with BS1 of the level 1, GN, diesel generator (DG), the power balance in the SADCF when the ADHPC fails and when RES and consumption power are outside their confidence intervals; alternating charge/discharge operation of the parallel circuits will extend the life of the BS system; – a comprehensive definition of optimal ADHPC system situational energy balance management task is formulated. [ABSTRACT FROM AUTHOR]

In this research paper the feasibility of renewable-powered, selfsufficient university campuses was explored by conducting a technoeconomic analysis of standalone PV-Battery systems for the buildings of Izmir Institute of Technology (IZTECH) in Izmir, Turkey. Given the high energy demand and dependence on fossil-based grids by universities, integrating renewables becomes important for minimizing carbon footprints. In this study the campus's solar potential was focused and the techno-economic feasibility of grid-independent operations provided by PV-battery systems was evaluated. Four scenarios were investigated: (i) maximum PV installation for each building (MPVB), (ii) maximum PV installation for the entire campus (MPVC), (iii) necessary PV installation for self-sufficiency of each building (NPVB), and (iv) necessary PV installation for self-sufficiency of the whole campus (NPVC). The first two scenarios considered the maximum achievable rooftop PV installation while the latter two included additional PV installation to cover all electricity needs. For all scenarios both lead-acid and Liion batteries were considered. Mathematical models were developed using PVSol and TRNSYS software, and technoeconomic analysis was conducted using Levelized Cost of Energy (LCOE) and Net Present Value (NPV) methods. It was found that the NPVC scenario with lead-acid batteries is the most favorable, as it minimizes battery utilization by enabling more PV installation and facilitating energy transfer between buildings. Additionally, the research showed that off-grid PV-battery systems are economically less feasible compared to on-grid counterparts, primarily due to the high cost of batteries. [ABSTRACT FROM AUTHOR]

Electrically powered rail transport is constantly increasing in order to meet the high demand for people and cargo transportation, whether with high-speed trains, subways, suburban trains, or electric tramways. In these types of applications, power electronics solutions such as integrated and efficient converters with multiple functionalities are highly desirable. Among these converters, one family stands out for its ability to generating multiple output terminals with reduced number of switches, namely, the nine-switch converter. Therefore, this paper proposes a multiport converter solution based on the nine-switch converter topology that integrates multiple functionalities with a reduced switch count. The converter, responsible for the power drive of the electric tramway, is exclusively powered by a battery. Moreover, it presents a strategically connected passive filter that provides a low-impedance path for high-frequency currents, avoiding leakage of the current circulation in the induction motor. Its phase-shift pulse-width modulation is capable of reducing the high-frequency components of the current delivered by the battery. The energy storage system is designed to optimize the system capacity based on the known real load profile of a public tramway, with a maximum power of 532.1 kW. The control system is designed and applied considering the battery as the energy source. Simulations were performed in the Matlab/Simulink environment to validate the proposed system, along with experiments using a reduced-scale prototype controlled by the dSPACE platform. The results present the converter's proper operation with integration of the source and AC load, presenting improved features compared with conventional solutions in terms of reduced leakage of the current circulation from the AC load and reduced battery current ripple. [ABSTRACT FROM AUTHOR]

Batteries are one of the most crucial energy storage devices today, and battery-energy management technology has an extremely significant impact on the performance and lifespan of batteries. The traditional design approach for battery-energy management platforms often neglects considerations for charging and discharging scenarios. Additionally, functional modules are designed independently, leading to incompatibility issues between hardware and control units, thereby limiting the system's performance. To address these challenges and enhance system coordination, this paper proposes a systematic design and implementation method for a battery-energy comprehensive management platform applied in charging and swapping scenarios. The method consists of four parts: hardware design, a dynamic load charging-balance control strategy, a composite micro-source hierarchical coordination control strategy, and a system emergency-response and protection strategy. The proposed method has been successfully applied to a design and has been used to build a battery-energy comprehensive management platform. Finally, through experiments, it has been demonstrated that this system can achieve energy scheduling, battery-energy balance, mode switching, and fault protection in a stable and reliable manner. [ABSTRACT FROM AUTHOR]

Beyond solar and wind energy, wave energy is gaining great interest due to its very high theoretical potential, although its stochastic nature causes intermittent and fluctuating power production. Energy storage system (ESS) integration to wave energy converter (WEC) plants represents a promising solution to mitigate this issue. To overcome the technological limits of the single storage devices, the hybridization of complementary ESSs represents an effective solution, extending the operating range over different timeframes. This paper analyzes the benefits of Li-ion battery–supercapacitor hybrid ESS integration into a grid-connected WEC, aiming at smoothing the produced power oscillations. The hybridization concept involves coupling a power-intensive technology, such as a supercapacitor devoted to managing fluctuations at higher frequency, with a battery technology exploited to manage power variations over longer timeframes to mitigate degradation issues. In this study, a multi-objective data-driven power management strategy, based on the simultaneous perturbation stochastic approximation (SPSA) algorithm, is implemented to minimize power fluctuations in terms of power ramp (representing the power variation between two consecutive values with a 1 s time step), both at the Point of Common Coupling (PCC) and the Li-ion battery terminals, thanks to the supercapacitor peak-shaving function. The SPSA management strategy, together with a suitable sizing procedure, allows a reduction of more than 70% in the power oscillations at the PCC with respect to those at the WEC terminals, while decreasing battery stress by more than 25% if compared to a non-hybrid ESS consisting of a Li-ion battery. This shows how supercapacitor features can extend battery lifespan when integrated in a hybrid ESS. [ABSTRACT FROM AUTHOR]

This paper proposes a methodological way to compensate for the imbalance between energy generation and consumption using a battery block from electric vehicles as an energy reservoir through the well-known vehicle-to-grid system (V2G). This method is based on a simulation process developed by the authors that takes into consideration the daily fluctuations in energy consumption as well as the power level generated by an energy source, either conventional, renewable, or hybrid. This study shows that for very large electric vehicle fleets, the system is rendered non-viable, since the remaining energy in the battery block that allows the electric vehicle to be usable during the daytime avoids having to compensate for the energy grid imbalance, only allowing it to cover a percentage of the energy imbalance, which the proposed methodology may optimize. The analysis of the proposed methodology also shows the viability of the system when being applied to a small fleet of electric vehicles, not only compensating for the energy imbalance but also preserving the required energy in the battery of the electric vehicle to make it run. This method allows for predicting the optimum size of an electric vehicle battery, which depends on the energy generation level, coverage factor of the energy imbalance, and size of the electric vehicle fleet. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) are powered by a battery mounted in the vehicle, which powers the motor and drives the wheels. Most commercial EVs can be charged by plugging them into a charging station. Such conductive recharging has various drawbacks, including physical plugging of the cable, safety concerns, and charging time. Manually charging EVs might be dangerous due to the chance of an electric spark or disaster. Advances in Wireless Power Transfer (WPT) demonstrate the capacity to transfer significant amounts of electricity over short and medium-range distances. The ultimate purpose of this paper is to improve the efficacy of electric car wireless charging systems. Here, a hybrid optimization-based Artificial Neural Network (ANN) model is applied to improve the efficacy of the WPT model in EVs. To optimize the weights of the ANN classifier, a hybrid approach termed as Grasshopper-Assisted Elephant Herd Optimization (GA-EHO) method is proposed. The GA-EHO is derived through the hybridization of Elephant Herd Optimization (EHO) Algorithm and the Grasshopper Optimization Algorithm (GOA) techniques. Finally, the experimental study reveals that at 70% learning rate, the proposed ANN system achieves a minimal MSE value of 0.0528, which is lower than other current classifiers, such as SVM, LSTM, and CNN. [ABSTRACT FROM AUTHOR]

Purpose: Lithium-polymer batteries have common usage in aviation industry especially unmanned aerial vehicles (UAV). Overheating is a serious problem in lithium-polymer batteries. Various cooling methods are performed to keep lithium-polymer batteries in the desired temperature range. The purpose of this paper is to examine pouch type lithium-polymer battery with plate fins by using particle image velocimetry (PIV) and computational fluid dynamics (CFD) for UAV. Design/methodology/approach: Battery models were produced with a 3D printer. The upper surfaces of fabricated battery models were covered with plate fins with different fin heights and fin thicknesses. Velocities were obtained with PIV and CFD. Temperature dissipations were acquired with numerical simulations. Findings: At the end of the study, the second battery model gave the lowest temperature values among the battery models. Temperature values of the seventh battery model were the highest temperatures. Fin cooling reduced the maximum cell temperatures noticeably. Numerical simulations agreed with PIV measurements well. Practical implications: This paper takes into account two essential tools such as PIV and CFD, for fluid mechanics, which are significant in the aviation industry and engineering life. Originality/value: The originality of this paper depends on investigation of both PIV and CFD for UAV and developing a cooling method that can be feasible for landing and take-off phases for UAV. [ABSTRACT FROM AUTHOR]

With the accelerated construction of 5G and IoT, more and more 5G base stations are erected. However, with the increase of 5G base stations, the power management of 5G base stations becomes progressively a bottleneck. In this paper, we solve the problem of 5G base station power management by designing a 5G base station lithium battery cloud monitoring system. In this paper, first, the lithium battery acquisition hardware is designed. Second, a new communication protocol is established based on Modbus. Third, the windows desktop upper computer software and the cloud-based monitoring system are designed. Finally, this paper designs the improved ResLSTM algorithm which is fused with ResNet algorithm based on Stacked LSTM. The algorithm designed in this paper is tested in comparison with SVM and LSTM. The performance of the algorithm designed in this paper is better than SVM and LSTM. Furthermore, the communication test, as well as the training and testing of the ResLSTM algorithm are outstanding. The 5G base station lithium-ion battery cloud monitoring system designed in this paper can meet the requirements. It has great significance for engineering promotion. More importantly, the ResLSTM algorithm designed in this paper can better guide the method of lithium-ion battery SOC estimation. [ABSTRACT FROM AUTHOR]

The production and consumption of non-renewable energy resources have disrupted the environment's biodiversity cycle. Global climate change, including worldwide warming, has made human life both now and in the future. The construction industry in the world has a significant share in the demand for energy consumption in these challenges. Therefore, the primary purpose of this paper is to implement standards to save and prevent energy loss to control and limit the demand for energy requested from the power network. Constructing a building with self-sufficient energy production that meets its energy needs by producing clean energy becomes more important. It also sells the excess energy to the grid, known as zero energy buildings. In the present paper, the issue is a constrained optimization problem that aims to minimize the total annual cost, including the initial investment cost for PV and batteries and their maintenance costs, as well as the cost of network exchanges. Among the limitations, the proposed model can mention the restrictions governing the battery, such as the limitations of the battery state of charge (SoC). The problem under optimization is a mixed integers nonlinear programming (MINLP) that will be solved by a particle swarm optimization (PSO) algorithm considering the total cost minimization. [ABSTRACT FROM AUTHOR]

Fuel Cell Hybrid Electric Vehicles (FCHEV) use batteries and Photo Voltaic (PV) arrays to overcome Fuel Cell (FC) limitations like slow reaction times and low power densities. In a traditional power conversion system, three converters link FC, PV array, and battery to the output load. Despite using the FC, PV array, and battery, it has three inductors and several switches. Each switch also endures high voltage stress, possibly lowering power density and efficiency. This paper proposes a single-inductor multi-input converter (SI-MIC) that achieves 550 V as output where the input is variable. The fuzzy logic controller enables the integration of multiple power sources by selectively turning the ON and OFF switches in various operating modes. Fewer active switches during the powering process make it possible to employ semiconductors with a lower voltage. Thus, the converter generates high power with low conduction and switching losses. The state space model verifies the validity and stability of the converter under various operating modes. • Battery and PV array are integrated with the fuel cell using the single inductor multi-input converter (SI-MIC). • Fuzzy logic controller is used for switching the semiconductor devices for operating in various modes. • Battery can be charged and discharged in standby and normal operating modes. • Even if the input voltage fluctuates, the output voltage is always maintained at 550 V. [ABSTRACT FROM AUTHOR]

Bi-directional, DC/DC converters find a wide range of applications such as, electric vehicles, plug-in-hybrid electric vehicles, electric vehicle charging stations, residential micro-grid systems, uninterruptable power supplies, charging/discharging of battery storage, the realisation of a hybrid combination of battery with renewable energy sources, etc. Bi-directional DC/DC converter topologies are divided mainly in two categories namely, Isolated and Non-Isolated. Isolated and Non-Isolated topologies are further sub-categorised into nine and sixteen groups, respectively. Respective configurations, table summary, comparative plots of the component count, efficiency and voltage gain are also presented in this paper. Bi-directional DC/DC converters are being developed to improve voltage gain, efficiency, minimise size and cost, increase reliability and introduce modularity. In this work, a detailed topology-wise review is carried out along with respective applications, highlighting comparative features, modern technology trends and future scopes which throw light on specific applications. Based on the review, this paper intends to provide a logical and categorised approach to highlight various technical aspects in this domain. [ABSTRACT FROM AUTHOR]

With the ever-growing aviation industry, energy researches on aviation have turned to renewable energy sources providing electric propulsion. Fuel cells are one of the most popular sources of power for electric vehicles terms of being greener, more reliability, and higher efficiency. Among fuel cells, the most attractive option for flight is polymer electrolyte membrane fuel cells (PEMFC). This paper aims to compose of a hybrid propulsion system to a lightweight aircraft Cri-Cri for more electric aircraft concept. For this purpose, first, conceptual a hybrid propulsion system is designed for the prototype aircraft. The fuel-cell hybrid system proposed for this paper consists of electric motor, fuel cell system, battery, DC/AC converter, and DC/DC converter. Second, some analyses are done. These analyses contain required power, hydrogen consumption, energy consumption, etc.. As a result, Cri-cri needs 26 Kw during take-off, 30 Kw during climb, 10 kW during cruise, and 7 kW during descent. It also consumed 0.75 kg of hydrogen in total, including 0.21 kg in the take-off and climb, 0.42 kg in the cruise, and 0.12 in the descent. The remaining 0.25 kg of hydrogen is reserved for use in emergencies. The total amount of energy consumed is 33 kWh. [ABSTRACT FROM AUTHOR]

Environmentally friendly and pollution-free hydrogen cell, battery and supercapacitor hybrid power system has taken the attention of scientists in recent years. Several notable advancements in energy storage mechanisms with hybrid power systems have been made during the last decade, influencing innovation, research, and the possible direction for improving energy storage technologies. This paper represents a quantitative analysis of all knowledge carriers with mathematical and statistical methods of hydrogen energy storage to establish a hybrid power system. For selecting the top cited papers in this topic, related articles on energy storage mechanisms for hybrid power systems were searched in the Scopus database under specified predetermined parameters. The selection technique of the most cited paper was based on filtered keywords in the hybrid hydrogen energy storage-based hybrid power system and related research during 2008–2021. About 48% of all articles have been published between 2016 and 2019; 21% will have originated from China; and 29% of the papers have used batteries as a form of energy storage in the application of electric vehicles. Most of the articles contain experimental work (25.11%) followed by simulation analysis (25%) and systematic and nonsystematic review (18.75%). Related publications with the most citations were published in 35 different impactful journals from different publishers and nations. This research found that integrating hydrogen energy storage with battery and supercapacitor to establish a hybrid power system has provided valuable insights into the field's progress and development. Moreover, it is a thriving and expanding subject of study. Bibliometric analysis was used to identify the most significant research publications on the subject of hybrid energy storage, mapping the multidisciplinary character, illustrating nature and trends, and outlining areas for further research. The process of collecting, selecting, and analyzing the most cited articles is expected to contribute to a methodical foundation for future developments of hydrogen energy storage systems and provide viable research paths toward attaining a hybrid power system. • Hydrogen-battery-supercapacitor hybrid power system made notable advancements. • A statistical analysis of hydrogen storage integrated hybrid system is demonstrated. • Top cited papers were searched in Scopus database under predetermined parameters. • Challenges and recommendations are highlighted for future energy applications. • Bibliometric analysis was used to identify potentials for future research directions. [ABSTRACT FROM AUTHOR]

Summary: The collection of high quality battery datasets is critical for applications such as State‐of‐Charge or State‐of‐Health estimations. Missing features within the dataset is a challenging problem. The issue is further exacerbated in on‐field electric vehicle applications. One method to resolve this challenge is to impute missing battery parameter values with best possible estimates. In this paper, we explore a self‐attention technique for imputing missing battery parameters. A masked self‐attention block enables accurate estimates of missing battery data. The introduced technique helps provide potential solutions to resolve the issue of missingness in battery parameters. We provide two primary contributions, (a) we illustrate the effectiveness of self‐attention mechanism to provide best estimates of missing battery parameters, (b) we highlight the efficacy of the approach over available state‐of‐the‐art imputation models. We release the code used in this scenario in the results section of our paper. This will allow researchers to effectively reproduce the results presented here.Self‐attention based Imputation Technique [ABSTRACT FROM AUTHOR]

The paper's state-of-the-art review focuses on an in-depth evaluation of smart home energy management systems which employ reinforcement learning-based methods to integrate energy storages. In order to optimize energy consumption and improve overall sustainability while maintaining technical and economic constraints, the paper first investigates the multi-faceted aspects of integrating energy storages into smart homes. Second, an overview of a smart home system and a theoretical background of reinforcement learning-based algorithms are given and discussed. Consequently, this study delves into the challenges and benefits of integrating energy storage, specifically looking at ways to lessen the impact of renewable sources' intermittency, improve grid stability, and streamline efficient energy storage management. Thirdly, the paper highlights the beneficial features of smart home energy storage integration, including reduced costs, increased system resilience, and improved energy efficiency. Therefore, cutting-edge reinforcement learning-based methods utilized in smart home energy management systems that incorporate energy storage are thoroughly examined by evaluating their effectiveness and adaptability, taking into account both multi-agent and single-agent reinforcement learning-based methods. Finally, the study identifies potential research directions, including the development of hybrid reinforcement learning algorithms, integration of demand-side management strategies, and addressing privacy and security concerns in reinforcement learning-based smart home energy management systems. While some research has made use of single-agent reinforcement learning, smart home energy storage systems that use energy storages seldom use multi-agent reinforcement learning techniques. Researchers, practitioners, and policymakers will be able to use this work as a foundation to build smart, sustainable home energy systems. [Display omitted] • Performance and effectiveness of reinforcement learning in smart homes are examined. • Single and multi-agent systems in smart homes with energy storages are reviewed. • Research directions and gaps are provided for future research directions. [ABSTRACT FROM AUTHOR]

The paper presents a grid-connected microgrid with a photovoltaic system and a battery as a storage element. The optimal design and control of storage elements and power quality improvement are enhanced using sigmoid-function-based variable step size (SFB-VSS) adaptive LMS control. The DC-link voltage and battery current are enhanced using an ILA-optimization-based PI controller. Comparative analysis shows that an ILA-optimized PI controller improves battery stress and DC-link voltage fluctuations, enhancing overall system stability. The relative percentage error of Vdc is only 0.5714% for ILA-optimized values as compared to GA, PSO, and manually tuned PI gains which are 0.857%, 1.14285%, and 0.86%, respectively. ILA-optimized parameters also enhance battery current, reducing stress on the battery. The system was studied under various dynamic conditions, achieving power balance in all conditions. The system has the capability of seamless transfer of control from GC mode to SA mode when the grid is disconnected. The proposed VSC control shows better performance in steady-state and dynamic conditions, maintaining a THD under 5%, which follows IEEE standard 519, and providing better DC offset rejection, fewer oscillations in the weight component of the load, and better convergence. The proposed control also enhances the frequency of the grid, ensuring a smooth transition between modes. The system is simulated in the MATLAB Simulink environment, and all the optimization techniques were carried out offline. [ABSTRACT FROM AUTHOR]

Following the European Climate Law of 2021 and the climate neutrality goal for zero-emission transportation by 2050, electric vehicles continue to gain market share, reaching 2.5 million vehicles in Q1 of 2023. Electric vehicles utilize an electric motor for propulsion powered by lithium batteries, which suffer from high temperatures caused by peak operation conditions and rapid charging, so hybridization with supercapacitors is implemented. In this paper, a fuzzy logic controller is employed based on a rule-based scheme and the Mamdani model to control the power distribution of the hybrid system, driven by the state of charge and duty cycle parameters. An active topology with one bi-directional DC-to-DC converter at each source is exploited in the MATLAB/Simulink environment, and five power states like acceleration and coasting are identified. Results show that the ideal duty cycle is within 0.40–0.50 as a universal value for all power states, which may vary depending on the available state of charge. Total efficiency is enhanced by 6%, sizing is increased by 22%, leading to a more compact layout, and battery life is extended by 20%. Future work includes testing with larger energy sources and the application of this management strategy in real-time operations. [ABSTRACT FROM AUTHOR]

Thermal energy is a renewable energy source to generate electrical energy that is not fully developed. One device that converts thermal energy into electrical power is a thermoelectric generator (TEG). TEGs are available as modules of various sizes and voltage levels. This paper is about the design of a battery charging system powered by a TEG panel. The TEG panel is implemented using 150 TEG modules interconnected in series and parallel. Its power is transferred to a battery using two stages of DC/DC converters. The 1st stage is a Lou converter that is used for maximum power point tracking (MPPT) by a referenced perturb and observe (referenced P&O) algorithm. The 2nd stage is a bidirectional converter based on buck-boost modes of operation. The system is used to charge a 9 V 1.2 Ah battery. The proposed MPPT algorithm's performance is compared with a traditional P&O algorithm. The TEG panel provided 27.5 W at a ΔT of 30 0C. The designed system is simulated in MATLAB SIMULINK. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) may be a viable solution to reduce the huge energy consumption and greenhouse emissions of global transportation. However, the cost and range of batteries are two major obstacles for EV. An efficient Power management system for EVs, which includes Supercapacitor (SC) and battery with an optimized converter, is proposed in this paper. An optimal Direct Current (DC)-DC Bi-directional Buck-Boost Converter (BBBC) with a Proportional Integral Derivative (PID) controller is used for the optimal flow of power from the energy source to the drive during EV acceleration. The regenerative braking energy is allowed to return through the same bidirectional converter and retained in the Hybrid Energy Storage System (HESS) during the deceleration mode. A novel optimization is attained in the converter controller circuit using a Deep Convolution Neural Network (DCNN) and Adaptive Aquila Optimization Algorithm (AAqOA). The proposed strategy is validated using the results compared to conventional algorithms. In particular, the settling time of the suggested AAqOA model is 55.44%, 96.94%, 97.03%, and 91.87% better than the extant PI, DA, SSA, and AOA methods, respectively. [ABSTRACT FROM AUTHOR]

To address the problem of large errors in SOC estimation using model methods, this paper integrates data-driven and model-based methods and proposes an online SOC evaluation method based on fuzzy PID optimized unscented Kalman filter (FPID–UKF). This method establishes a fusion method SOC evaluation model architecture, analyzes the data-model fusion evaluation method, and studies the online evaluation method of FPID–UKF. The evaluation method firstly adopts the forgetting factor recursive least square (FFRLS) method to identify the parameters of the equivalent circuit and then, takes the unscented Kalman filter (UKF) to estimate the SOC of the established battery state evaluation model; meanwhile, to further improve the prediction effect of UKF, Kalman gain and innovation were controlled by PID, and the PID coefficient was adjusted by fuzzy control to realize the optimization of UKF by FPID. Finally, the battery data from the University of Maryland were verified under US06 Highway Driving Schedule (US06) and Federal Urban Driving Schedule (FUDS) conditions, and the results indicate that the SOC estimation error does not exceed 3.07% under US06 condition and 4.79% under FUDS condition, and the FPID–UKF algorithm has better estimation accuracy than EKF and UKF. [ABSTRACT FROM AUTHOR]

With the emergence of various technologies in renewable energy sources (RESs), consumers nowadays have led to multiple techniques for integrating various resources. An optimum energy flow management delivers an effective, reliable integrated renewable energy system (IRES) with higher efficiency. In this paper, an optimum model for the IRES is designed through the optimal sizing of system components for an estimated demand. Using integer linear programming, an energy management scheme has been developed based upon scheduling of energy utilization technique of deferrable loads. The considered approach is modeled to reduce the peak energy utilization (hourly) of the area of study through the consumption scheduling of machines. Furthermore, the MHP‐solar‐wind‐battery‐based IRES with energy Management strategy has been optimized in MATLAB. Size optimization of the system has been done without and with the energy management strategy using improved harmony search algorithm. The results show a significant improvement in the cost and savings using the strategy. Considering the 5% LPSP, the optimum size for the integrated system offers a total annualized cost of INR 3.21 million with a levelized cost of energy of 4.5 INR/kWh. The comparison of proposed method with PSO shows the better convergence rate of the system. Comparing systems with and without energy management, it has been noted that with a strategy, systems size and expenditures are reduced significantly. [ABSTRACT FROM AUTHOR]

Lithium-ion cells (LIC) are emerging as the critical energy source in space applications for spacecraft. The analysis of the failure of small satellites reveals that 41.3% have either suffered partial or total failures in the space environment around Earth, resulting in the accumulation of debris. The defects in batteries caused more failures than thrusters. The present paper details a framework for ensuring the durability of LIC systems in space applications. It is discussed in three verticals viz. a) Durability through Design' that gives prominence to best practices in cell chemistry selection, processing and, design through parametric modelling; b) 'Durability through quality control and testing by the manufacturer and by identifying relevant reliability models; c)'On-orbit management' through the State of Health (SOH) assessment by Random Forest algorithm and a well-equipped intelligent Battery Management System (BMS). Lacuna in standards for cell development process is brought out. Importance of quantitative approach for design, reliability and asset management during space application is highlighted. Use of agile, contemporary techniques of artificial intelligence in this quantitative approach is also discussed. The framework tries to bring out a systematized protocol to be followed during the development and operational life cycle of LIC for ensuring highly reliable systems for space application. [ABSTRACT FROM AUTHOR]

A Hybrid Electric Ship (HES) is investigated in this work to improve its dynamic response to sudden power demand changes. The HES system is based on a Variable-Speed Diesel Generator (VSDG) used for long-term energy supply, with Two Energy Storage Systems (TESSs) using Batteries and supercapacitors for transient power supply. The TESS mitigates the power demand fluctuations and reduces its impact on VSDG, which is linked to a DC-bus through a controlled rectifier. Batteries and Supercapacitors (SCs) are connected in a DC-bus using the bidirectional DC/DC converters to manage the transient and fluctuating components. Two thrusters (one in the front and the second in the back of the Ship) are considered for the propulsion system. The HES power demand includes the requirement of the thrusters and embedded power consumers (elevator, package lifting, air conditioning, onboard electronics devices, etc.). The highlight of this paper is based on the HES fast response improvement in sudden power demand situations via TESS-based batteries and supercapacitors. The other highlight concerns the SCs' electrothermal modeling using an extension of the SCs' current ripples' frequency range (0 to 1 kHz), considering parameter evolution according to using the temperature and current waveform. This energy management-based dynamic power component separation method is tested via simulations using a variable operating temperature scenario. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in the electric vehicle industry due to their recyclability and long life. However, a failure of lithium-ion batteries can cause some catastrophic accidents, such as electric car battery explosion fires and so on. To prevent such harm from occurring, it is essential to monitor the remaining useful life of lithium-ion batteries and give early warning. In this paper, an adaptive noise reduction approach is proposed to predict the RUL (Remaining Useful Life) of lithium-ion batteries, which uses CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) combined with wavelet decomposition to achieve adaptive noise reduction decomposition, and then inputs the obtained IMF (Intrinsic Mode Function) components into LS–RVM (Least Square Relevance Vector Machine) for training, prediction, and reconstruction, so as to achieve high-precision prediction of RUL. Moreover, in order to verify the validity of the model, the model in this paper is compared with other common models. The results demonstrate that the RMSE, MAPE, and MAE of the proposed model are 0.008678, 0.005002, and 0.006894, and that it has higher accuracy than the other common prediction models. [ABSTRACT FROM AUTHOR]

This paper aims to analyse two energy storage methods—batteries and hydrogen storage technologies—that in some cases are treated as complementary technologies, but in other ones they are considered opposed technologies. A detailed technical description of each technology will allow to understand the evolution of batteries and hydrogen storage technologies: batteries looking for higher energy capacity and lower maintenance, while hydrogen storage technologies pursuing better volumetric and gravimetric densities. Additionally, as energy storage systems, a mathematical model is required to know the state of charge of the system. For this purpose, a mathematical model is proposed for conventional batteries, for compressed hydrogen tanks, for liquid hydrogen storage and for metal hydride tanks, which makes it possible to integrate energy storage systems into management strategies that aim to solve the energy balance in plants based on hybrid energy storage systems. From the technical point of view, most batteries are easier to operate and do not require special operating conditions, while hydrogen storage methods are currently functioning at the two extremes (high temperatures for metal and complex hydrides and low temperatures for liquid hydrogen or physisorption). Additionally, the technical comparison made in this paper also includes research trends and future possibilities in an attempt to help plan future policies. [ABSTRACT FROM AUTHOR]

In the pursuit of more efficient vehicles on the world's roads, the vehicle thermal management system has become a limiting factor when it comes to EV range and battery life. In extreme climates, if the thermal system cannot pull down or warm up the EV powertrain in a timely manner, the battery is at serious risk of capacity loss or accelerated degradation. As waste heat is inherently limited with EVs, the way in which we provide the heat for warm-up must be as efficient as possible to reduce the load on the battery. In this paper, a revolutionary waste heat recovery (WHR) thermal management system designed by Tesla, nicknamed the 'Octovalve', is described, modelled, and simulated. This paper contributes to collective knowledge by presenting an in-depth breakdown of the key operating modes and outlining the potential benefits. Modelled in the multidomain Simulink Simscape software, the octovalve's performance is directly compared to a typical EV WHR thermal management system. The system under analysis is shown to significantly reduce EV energy consumption and battery load during warm-up but at the cost of overall warm-up time. Unlike any other WHR system found in literature, this system has a heat pump with can perform air conditioning and heat pump tasks simultaneously, which is shown to have a remarkable impact on energy efficiency and battery life. [ABSTRACT FROM AUTHOR]